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1
Maulana, Luthfi, Yusuf Gladiensyah Bihanda, and Yuita Arum Sari. "Color space and color channel selection on image segmentation of food images." Register: Jurnal Ilmiah Teknologi Sistem Informasi 6, no. 2 (September 1, 2020): 141. http://dx.doi.org/10.26594/register.v6i2.2061.
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Image segmentation is a predefined process of image processing to determine a specific object. One of the problems in food recognition and food estimation is the lack of quality of the result of image segmentation. This paper presents a comparative study of different color space and color channel selection in image segmentation of food images. Based on previous research regarding image segmentation used in food leftover estimation, this paper proposed a different approach to selecting color space and color channel based on the score of Intersection Over Union (IOU) and Dice from the whole dataset. The color transformation is required, and five color spaces were used: CIELAB, HSV, YUV, YCbCr, and HLS. The result shows that A in LAB and H in HLS are better to produce segmentation than other color channels, with the Dice score of both is 5 (the highest score). It concludes that this color channel selection is applicable to be embedded in the Automatic Food Leftover Estimation (AFLE) algorithm.
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Daubner, Tomas, Jens Kizhofer, and Mircea Dinulescu. "Experimental investigation of five parallel plane jets with variation of Reynolds number and outlet conditions." EPJ Web of Conferences 180 (2018): 02018. http://dx.doi.org/10.1051/epjconf/201818002018.
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This article describes an experimental investigation in the near field of five parallel plane jets. The study applies 2D Particle Image Velocimetry (PIV) for ventilated and unventilated jets, where ventilated means exiting into a duct with expansion ratio 3.5 and unventilated means exiting to the free atmosphere. Results are presented for Reynolds numbers 1408, 5857 and 10510. The Reynolds number is calculated for the middle channel and is based on the height of the nozzle (channel) equivalent diameter 2h. All characteristic regions of the methodology to describe multiple interacting jets are observed by the PIV measurements - converging, merging and combined. Each of the five parallel channels has an aspect ratio of 25 defined as nozzle width (w) to height (h). The channels have a length of 185 times the channel height guaranteeing a fully developed velocity profile at the exit from the channel. Spacing between the single plane jets is 3 times the channel height. The near field of multiple mixing jets is depended on outlet nozzle geometry. Blunt geometry of the nozzle was chosen (sudden contraction).
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Sun, Bo, Abdullah M. Iliyasu, Fei Yan, Jesus A. Garcia Sanchez, Fangyan Dong, Awad Kh Al-Asmari, and Kaoru Hirota. "Multi-Channel Information Operations on Quantum Images." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 2 (March 20, 2014): 140–49. http://dx.doi.org/10.20965/jaciii.2014.p0140.
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Quantum circuits to realize color operations of channel of interest, channel swapping, and alpha blending on images are proposed using five kinds of quantum gates, i.e., NOT, CNOT, Toffoli, Rotation, and Controlled Rotation gates. Complexities of the proposed circuits are for an N-sized image, whereas the color information must be transformed pixel by pixel in the case of operators on classical computers. Simulations on the proposed three quantum color operations using three human facial and one Japanese style house images demonstrate that at most 9, 3, and 5 basic quantum gates are requested, that shows the feasibility of quantum circuits. Based on proposed three operations, all invertible classical color information transformation on imagesmay be designed and many applications can be realized on quantum computer, and the channel of interest based watermarking is being researched which the experiment results show that from the point of PSNR, our proposal is about 10 dB better than the chosen method of quantum image watermarking.
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Choi, Chulmin, Lai‐Hoon Kim, Se‐Jin Doo, Yang‐Ki Oh, Dae‐Up Jeong, and Koeng‐Mo Sung. "A five‐channel microphone system for detecting 3‐D acoustic image sources." Journal of the Acoustical Society of America 109, no. 5 (May 2001): 2284. http://dx.doi.org/10.1121/1.4743991.
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Yang, Jun, Qilong Min, Weitao Lu, Ying Ma, Wen Yao, and Tianshu Lu. "An RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection." Atmospheric Measurement Techniques 10, no. 3 (March 29, 2017): 1191–201. http://dx.doi.org/10.5194/amt-10-1191-2017.
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Abstract. The inhomogeneous sky background presents a great challenge for accurate cloud recognition from the total-sky images. A channel operation was introduced in this study to produce a new composite channel in which the difference of atmospheric scattering has been removed and a homogeneous sky background can be obtained. Following this, a new cloud detection algorithm was proposed that combined the merits of the differencing and threshold methods, named differencing and threshold combination algorithm (DTCA). Firstly, the channel operation was applied to transform 3-D RGB image to the new channel, then the circumsolar saturated pixels and its circularity were used to judge whether the sun is visible or not in the image. When the sun is obscured, a single threshold can be used to identify cloud pixels. If the sun is visible in the image, the true clear-sky background differencing algorithm is adopted to detect clouds. The qualitative assessment for eight different total-sky images shows the DTCA algorithm obtained satisfactory cloud identification effectiveness for thin clouds and in the circumsolar and near-horizon regions. Quantitative evaluation also shows that the DTCA algorithm achieved the highest cloud recognition precision for five different types of clouds and performed well under both visible sun and blocked sun conditions.
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Wang, Zhi-guo, Wei Wang, and Baolin Su. "Multi-sensor Image Fusion Algorithm Based on Multiresolution Analysis." International Journal of Online Engineering (iJOE) 14, no. 06 (June 22, 2018): 44. http://dx.doi.org/10.3991/ijoe.v14i06.8697.
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<p class="0abstract">To solve the fusion problem of visible and infrared images, based on image fusion algorithm such as region fusion, wavelet transform, spatial frequency, Laplasse Pyramid and principal component analysis, the quality evaluation index of image fusion was defined. Then, curve-let transform was used to replace the wavelet change to express the superiority of the curve. It integrated the intensity channel and the infrared image, and then transformed it to the original space to get the fused color image. Finally, two groups of images at different time intervals were used to carry out experiments, and the images obtained after fusion were compared with the images obtained by the first five algorithms, and the quality was evaluated. The experiment showed that the image fusion algorithm based on curve-let transform had good performance, and it can well integrate the information of visible and infrared images. It is concluded that the image fusion algorithm based on curve-let change is a feasible multi-sensor image fusion algorithm based on multi-resolution analysis. </p>
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Dong, Pinliang, Ruofei Zhong, Jisheng Xia, and Shucheng Tan. "A semi-automated method for extracting channels and channel profiles from lidar-derived digital elevation models." Geosphere 16, no. 3 (March 10, 2020): 806–16. http://dx.doi.org/10.1130/ges02188.1.
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Abstract With the advent of digital elevation models (DEMs) and geographic information systems (GIS), several methods have been proposed to extract channels from raster DEMs. Light detection and ranging (lidar) can produce high-resolution DEMs and poses new challenges to existing methods for channel extraction. This paper introduces a semi-automated method for extracting stream channels and channel profiles from high-resolution DEMs using image processing techniques. Based on user-specified approximate locations of start and end points and a few simple parameters, the method implements five automated steps: (1) channel detection using a local minimum value search; (2) channel delineation using Bresenham’s line algorithm and mathematical morphological operation; (3) vectorization; (4) profile generation; and (5) accuracy assessment. The method is implemented as an ArcGIS Python add-in toolbar named Channel Extraction. The application of the toolbar is demonstrated using a lidar-derived DEM in a study area along the San Andreas fault in California, USA. The software and test data are freely available for download (see Supplemental Files1). The demonstrated samples suggest that this new semi-automated method for extracting channels and channel profiles is flexible and user-friendly and can produce accurate results to support geomorphic studies.
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Martinez-Perez, M. Elena, Nicholas Witt, Kim H. Parker, Alun D. Hughes, and Simon A. M. Thom. "Automatic optic disc detection in colour fundus images by means of multispectral analysis and information content." PeerJ 7 (June 27, 2019): e7119. http://dx.doi.org/10.7717/peerj.7119.
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The optic disc (OD) in retinal fundus images is widely used as a reference in computer-based systems for the measurement of the severity of retinal disease. A number of algorithms have been published in the past 5 years to locate and measure the OD in digital fundus images. Our proposed algorithm, automatically: (i) uses the three channels (RGB) of the digital colour image to locate the region of interest (ROI) where the OD lies, (ii) measures the Shannon information content per channel in the ROI, to decide which channel is most appropriate for searching for the OD centre using the circular Hough transform. A series of evaluations were undertaken to test our hypothesis that using the three channels gives a better performance than a single channel. Three different databases were used for evaluation purposes with a total of 2,371 colour images giving a misdetection error of 3% in the localisation of the centre of the OD. We find that the area determined by our algorithm which assumes that the OD is circular, is similar to that found by other algorithms that detected the shape of the OD. Five metrics were measured for comparison with other recent studies. Combining the two databases where expert delineation of the OD is available (1,240 images), the average results for our multispectral algorithm are: TPR = 0.879, FPR = 0.003, Accuracy = 0.994, Overlap = 80.6% and Dice index = 0.878.
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Yang, J., Q. Min, W. Lu, W. Yao, Y. Ma, J. Du, T. Lu, and G. Liu. "An automated cloud detection method based on the green channel of total-sky visible images." Atmospheric Measurement Techniques 8, no. 11 (November 5, 2015): 4671–79. http://dx.doi.org/10.5194/amt-8-4671-2015.
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Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.
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Varga, Domonkos. "No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features." Journal of Imaging 6, no. 8 (July 30, 2020): 75. http://dx.doi.org/10.3390/jimaging6080075.
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The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.
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Catak, Ferhat Ozgur, Javed Ahmed, Kevser Sahinbas, and Zahid Hussain Khand. "Data augmentation based malware detection using convolutional neural networks." PeerJ Computer Science 7 (January 22, 2021): e346. http://dx.doi.org/10.7717/peerj-cs.346.
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Due to advancements in malware competencies, cyber-attacks have been broadly observed in the digital world. Cyber-attacks can hit an organization hard by causing several damages such as data breach, financial loss, and reputation loss. Some of the most prominent examples of ransomware attacks in history are WannaCry and Petya, which impacted companies’ finances throughout the globe. Both WannaCry and Petya caused operational processes inoperable by targeting critical infrastructure. It is quite impossible for anti-virus applications using traditional signature-based methods to detect this type of malware because they have different characteristics on each contaminated computer. The most important feature of this type of malware is that they change their contents using their mutation engines to create another hash representation of the executable file as they propagate from one computer to another. To overcome this method that attackers use to camouflage malware, we have created three-channel image files of malicious software. Attackers make different variants of the same software because they modify the contents of the malware. In the solution to this problem, we created variants of the images by applying data augmentation methods. This article aims to provide an image augmentation enhanced deep convolutional neural network (CNN) models for detecting malware families in a metamorphic malware environment. The main contributions of the article consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a CNN model. In the first component, the collected malware samples are converted into binary file to 3-channel images using the windowing technique. The second component of the system create the augmented version of the images, and the last part builds a classification model. This study uses five different deep CNN model for malware family detection. The results obtained by the classifier demonstrate accuracy up to 98%, which is quite satisfactory.
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Lee, Yooho, Sang-hyo Park, Eunjun Rhee, Byung-Gyu Kim, and Dongsan Jun. "Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network." Applied Sciences 11, no. 17 (August 25, 2021): 7803. http://dx.doi.org/10.3390/app11177803.
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Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.
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Gao, Yu, Xintong Han, Xun Wang, Weilin Huang, and Matthew Scott. "Channel Interaction Networks for Fine-Grained Image Categorization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10818–25. http://dx.doi.org/10.1609/aaai.v34i07.6712.
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Fine-grained image categorization is challenging due to the subtle inter-class differences. We posit that exploiting the rich relationships between channels can help capture such differences since different channels correspond to different semantics. In this paper, we propose a channel interaction network (CIN), which models the channel-wise interplay both within an image and across images. For a single image, a self-channel interaction (SCI) module is proposed to explore channel-wise correlation within the image. This allows the model to learn the complementary features from the correlated channels, yielding stronger fine-grained features. Furthermore, given an image pair, we introduce a contrastive channel interaction (CCI) module to model the cross-sample channel interaction with a metric learning framework, allowing the CIN to distinguish the subtle visual differences between images. Our model can be trained efficiently in an end-to-end fashion without the need of multi-stage training and testing. Finally, comprehensive experiments are conducted on three publicly available benchmarks, where the proposed method consistently outperforms the state-of-the-art approaches, such as DFL-CNN(Wang, Morariu, and Davis 2018) and NTS(Yang et al. 2018).
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Li, Bing, and Bin-Jie Hu. "Imaging Method Based on Time Reversal Channel Compensation." International Journal of Antennas and Propagation 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/894608.
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The conventional time reversal imaging (TRI) method builds imaging function by using the maximal value of signal amplitude. In this circumstance, some remote targets are missed (near-far problem) or low resolution is obtained in lossy and/or dispersive media, and too many transceivers are employed to locate targets, which increases the complexity and cost of system. To solve these problems, a novel TRI algorithm is presented in this paper. In order to achieve a high resolution, the signal amplitude corresponding to focal time observed at target position is used to reconstruct the target image. For disposing near-far problem and suppressing spurious images, combining with cross-correlation property and amplitude compensation, channel compensation function (CCF) is introduced. Moreover, the complexity and cost of system are reduced by employing only five transceivers to detect four targets whose number is close to that of transceivers. For the sake of demonstrating the practicability of the proposed analytical framework, the numerical experiments are actualized in both nondispersive-lossless (NDL) media and dispersive-conductive (DPC) media. Results show that the performance of the proposed method is superior to that of conventional TRI algorithm even under few echo signals.
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Widiastuti, Rina, and Lastria Nurtanzila. "Membaca Citra Indonesia Dalam Arsip Audio Visual Kementerian Pariwisata." Diplomatika: Jurnal Kearsipan Terapan 2, no. 1 (November 22, 2018): 44. http://dx.doi.org/10.22146/diplomatika.35300.
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This article aims to explore the image of Indonesia created by the Ministry of Tourism through the audio-visual archive, which is stored and published on its official youtube channel. This study is in response to the Indonesian government's policy to improve the image of Indonesia and build a strong nation brand for Indonesia to compete in global level. Using the content analysis method, I analyzed the videos on the Indonesia.Travel channel to reveal the image of Indonesia promoted by the Indonesian government. Based on the icons that appear on the 218 videos on the youtube channel, we can find five main icon categories. The iconic categories are natural beauty, cultural uniqueness, resident friendliness, delicacy of food, and environmental peace. These five iconic categories build one image of Indonesia as a wonderful country. This is in accordance with the official slogan used by the Ministry of Tourism to promote Indonesia, namely Wonderful Indonesia.Artikel ini bertujuan untuk mengeksplorasi arsip audio-visual yang disimpan dan dipublikasikan di saluran resmi youtube. Penelitian ini merupakan tanggapan terhadap kebijakan bahasa Indonesia untuk meningkatkan citra Indonesia dan membangun kebangsaan yang kuat bagi Indonesia untuk bersaing di tingkat global. Menggunakan metode analisis isi, saya menganalisis video di Indonesia. Saluran perjalanan untuk mengungkap citra Indonesia dipromosikan oleh pemerintah Indonesia. Berdasarkan ikon yang muncul pada 218 video di saluran YouTube, kita dapat menemukan lima kategori ikon utama. Kategori ikonik adalah keindahan alam, keunikan budaya, keramahan penduduk, kelezatan makanan, dan kedamaian lingkungan. Kelima kategori ikon citra Indonesia sebagai negara yang indah. Hal ini sesuai dengan slogan resmi yang digunakan oleh Kementerian Pariwisata untuk mempromosikan Indonesia, yaitu Wonderful Indonesia.
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Bhuyian, Md N. M., and Alfred Kalyanapu. "Predicting Channel Conveyance and Characterizing Planform Using River Bathymetry via Satellite Image Compilation (RiBaSIC) Algorithm for DEM-Based Hydrodynamic Modeling." Remote Sensing 12, no. 17 (August 28, 2020): 2799. http://dx.doi.org/10.3390/rs12172799.
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Digital Elevation Models (DEMs) are widely used as a proxy for bathymetric data and several studies have attempted to improve DEM accuracy for hydrodynamic (HD) modeling. Most of these studies attempted to quantitatively improve estimates of channel conveyance (assuming a non-braided morphology) rather than accounting for the actual channel planform. Accurate representation of river conveyance and planform in a DEM is critical to HD modeling and can be achieved with a combination of remote sensing (e.g., satellite image) and field data, such as water surface elevation (WSE). Therefore, the objectives of this study are (i) to develop an algorithm for predicting channel conveyance and characterizing planform via satellite images and in situ WSE and (ii) to estimate discharge using the predicted conveyance via an HD model. The algorithm is named River Bathymetry via Satellite Image Compilation (RiBaSIC) and uses Landsat satellite imagery, Shuttle Radar Topography Mission (SRTM) DEM, Multi-Error-Removed Improved-Terrain (MERIT) DEM, and observed WSE. The algorithm is tested on four study areas along the Willamette River, Kushiyara River, Jamuna River, and Solimoes River. Channel slope and predicted hydraulic radius are subsequently estimated for approximating Manning’s roughness factor. Two-dimensional HD models using DEMs modified by the RiBaSIC algorithm and corresponding Manning’s roughness factors are employed for discharge estimation. The proposed algorithm can represent river planform and conveyance in single-channeled, meandering, wandering, and braided river reaches. Additionally, the HD models estimated discharge within 14–19% relative root mean squared error (RRMSE) in simulation of five years period.
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Alasafi, Layth, Tuna Göksu, and Ammar Albayati. "Copyright Protection by Robust Digital Image Watermarking in Unsecured Communication Channels." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (July 1, 2017): 234. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp234-249.
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The transition from analog technologies to digital technologies has increased the ever-growing concern for protection and authentication of digital content and data. Owners of digital content of any type are seeking and exploring new technologies for the protection of copyrighted multimedia content. Multimedia protection has become an issue in recent years, and to deal with this issue, researchers are continuously searching for and exploring new effective and efficient technologies. This thesis study has been prepared in order to increase the invisibility and durability of invisible watermarking by using the multilayer Discrete Wavelet Transform (DWT) in the frequency plane and embedding two marks into an image for the purpose of authentication and copyright when digital content travels through an unsecured channel. A novel watermarking algorithm has been proposed based on five active positions and on using two marks. In addition to the extraction process, watermarking images will be subjected to a set of attack tests. The evaluation criteria have been the bases of assessing the value of SNR, PNSR, MAE and RMSE for both the watermarking images and the watermarking images after attacks, followed by the invisibility of the watermarking being measured before and after the attacks. Our lab results show high robustness and high quality images obtaining value for both SNR and PNSR.
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An, Shunmin, Xixia Huang, Linling Wang, Zhangjing Zheng, and Le Wang. "Unsupervised water scene dehazing network using multiple scattering model." PLOS ONE 16, no. 6 (June 28, 2021): e0253214. http://dx.doi.org/10.1371/journal.pone.0253214.
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In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unlike previous image dehazing methods, our method uses the unsupervised neural network and the atmospheric multiple scattering model and solves the problem of difficult acquisition of ideal datasets and the effect of multiple scattering on the image. In our method, in order to embed the atmospheric multiple scattering model into the unsupervised dehazing network, the unsupervised dehazing network uses four branches to estimate the scene radiation layer, transmission map layer, blur kernel layer and atmospheric light layer, the hazy image is then synthesized from the four output layers, minimizing the input hazy image and the output hazy image, where the output scene radiation layer is the final dehazing image. In addition, we constructed unsupervised loss functions which applicable to image dehazing by prior knowledge, i.e., color attenuation energy loss and dark channel loss. The method has a wide range of applications, with haze being thick and variable in marine, river and lake scenes, the method can be used to assist ship vision for target detection or forward road recognition in hazy conditions. Through extensive experiments on synthetic and real-world images, the proposed method is able to recover the details, structure and texture of the water image better than five advanced dehazing methods.
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Pourdarbani, Razieh, Sajad Sabzi, Mario Hernández-Hernández, José Luis Hernández-Hernández, Ginés García-Mateos, Davood Kalantari, and José Miguel Molina-Martínez. "Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions." Remote Sensing 11, no. 21 (October 30, 2019): 2546. http://dx.doi.org/10.3390/rs11212546.
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Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most effective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods.
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Zhu, Li, Minghu Wu, Xiangkui Wan, Nan Zhao, and Wei Xiong. "Image Recognition of Rapeseed Pests Based on Random Forest Classifier." International Journal of Information Technology and Web Engineering 12, no. 3 (July 2017): 1–10. http://dx.doi.org/10.4018/ijitwe.2017070101.
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Rapeseed pests will result in a rapeseed production reduction. The accurate identification of rapeseed pests is the foundation for the optimal opportunity for treatment and the use of pesticide pertinently. Manual recognition is labour-intensive and strong subjective. This paper propsed a image recognition method of rapeseed pests based on the color characteristics. The GrabCut algorithm is adopted to segment the foreground from the image of the pets. The noise with small area is filtered out. The benchmark images is obtained from the minimum enclosing rectangle of the rapeseed pests. Two types of color feature description of pests is adopt, one is the three order color moments of the normalized H/S channel; the other is the cross matching index calculated by the reverse projection of the color histogram. A multi-dimensional vector, which is used to train the random forest classifier, is extracted from the color feature of the benchmark image. The recognition results can be obtained by inputing the color features of the image to be detected to the random forest classifier and training.The experiment showed that the proposed method may identify five kinds of rapeseed accurately such as erythema, cabbage caterpillar, colaphellus bowringii baly, flea beetle and aphid with the recognition rate of 96%.
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Zhao, Xiaolei, Jing Zhang, Jimiao Tian, Li Zhuo, and Jie Zhang. "Residual Dense Network Based on Channel-Spatial Attention for the Scene Classification of a High-Resolution Remote Sensing Image." Remote Sensing 12, no. 11 (June 10, 2020): 1887. http://dx.doi.org/10.3390/rs12111887.
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The scene classification of a remote sensing image has been widely used in various fields as an important task of understanding the content of a remote sensing image. Specially, a high-resolution remote sensing scene contains rich information and complex content. Considering that the scene content in a remote sensing image is very tight to the spatial relationship characteristics, how to design an effective feature extraction network directly decides the quality of classification by fully mining the spatial information in a high-resolution remote sensing image. In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative networks of CNN, which shows a stronger feature learning ability as it fully utilizes all the convolutional layer information. Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image. First, multi-layer convolutional features are fused with residual dense blocks. Then, a channel-spatial attention module is added to obtain more effective feature representation. Finally, softmax classifier is applied to classify the scene after adopting data augmentation strategy for meeting the training requirements of the network parameters. Five experiments are conducted on the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID), and the competitive results demonstrate that our method can extract more effective features and is more conducive to classifying a scene.
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Manera, Maurizio. "The Use of Texture Analysis in the Morpho-Functional Characterization of Mast Cell Degranulation in Rainbow Trout (Onchorhynchus mykiss)." Microscopy and Microanalysis 19, no. 6 (September 4, 2013): 1436–44. http://dx.doi.org/10.1017/s1431927613013408.
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AbstractDegranulation of intestinal mast cells in rainbow trout was studied ex vivo by means of texture analysis and related to the maximal intestinal contraction elicited by degranulation itself. Two strips from the same intestinal segment from ten trout were sampled, processed for light microscopy and stained with Giemsa solution. One of the two strips was exposed to an incremental dose of compound 48/80 in an isolated organ bath before processing. Gray-level RGB channel equivalent and 8-bit gray-level images of five granular cytoplasm areas of mast cells for each section were analyzed for texture features and to evaluate discrimination possibility between treatment groups by means of linear discriminant analysis according to feature selection methods and RGB stacks. Differential mean values (after–before compound 48/80) of the green (r2 = 0.84, p < 0.01) and blue (r2 = 0.83, p < 0.01) RGB channels and 8-bit grayscale (r2 = 0.76, p < 0.05) image correlated significantly with the respective value of maximal intestinal contraction. A possible acidic (anionic) nature for the putative pro-contractile basophil agonist can be inferred.
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Meisheng, Cao, Mi Desheng, Pu Yinbin, and Liu Jinghaung. "Application of nonlinear colour enhancement on transparencies for interpretation of glacier surface characteristics." Annals of Glaciology 16 (1992): 190–92. http://dx.doi.org/10.3189/1992aog16-1-190-192.
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According to the analysis of grey scale range on MSS-4, -5, -6 and -7 channel image films for five snow-ice categories on glacier surface, the grey scale among snow, bare ice, ice pinnacle, moraine-covered ice surface and gully bed has been spread nonlinearly by using duplicative processing on high-contrast film. As a result of the rescaling of grey levels, the colour differences of morphological features of Rongbu Glacier in the Qpmolangma region have been increased on false colour composite photography. It is also shown that using MSS-6 to composite false colour images compared to MSS-5 will supply more information for the interpretation of the glacier area.
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Meisheng, Cao, Mi Desheng, Pu Yinbin, and Liu Jinghaung. "Application of nonlinear colour enhancement on transparencies for interpretation of glacier surface characteristics." Annals of Glaciology 16 (1992): 190–92. http://dx.doi.org/10.1017/s026030550000505x.
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According to the analysis of grey scale range on MSS-4, -5, -6 and -7 channel image films for five snow-ice categories on glacier surface, the grey scale among snow, bare ice, ice pinnacle, moraine-covered ice surface and gully bed has been spread nonlinearly by using duplicative processing on high-contrast film. As a result of the rescaling of grey levels, the colour differences of morphological features of Rongbu Glacier in the Qpmolangma region have been increased on false colour composite photography. It is also shown that using MSS-6 to composite false colour images compared to MSS-5 will supply more information for the interpretation of the glacier area.
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Smith, Joseph R., Don W. King, Yong D. Park, Mark R. Lee, Gregory P. Lee, and Patrick D. Jenkins. "Magnetic source imaging guidance of gamma knife radiosurgery for the treatment of epilepsy." Journal of Neurosurgery 93, supplement_3 (December 2000): 136–40. http://dx.doi.org/10.3171/jns.2000.93.supplement_3.0136.
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Object. The purpose of this study was to determine if magnetic source (MS) imaging could provide useful information in the planning and performance of gamma knife radiosurgery (GKS) for epilepsy. Methods. Magnetic source imaging of interictal epileptiform dipoles was studied in 53 epilepsy surgery candidates. All patients underwent volumetric magnetic resonance (MR) imaging. Subsequently, magnetoencephalography (MEG) was performed using single or dual 37—channel units. The MR images and MEG recordings were then coregistered to produce the MS imaging data. Magnetic source imaging epileptiform data were reviewed in a blinded fashion and spatial distributions were classified as focal, regional, multiple, scattered, or none. Postresection operative photographs were compared with MS image results to determine whether extensive or partial/no resection of the MS image focus had been accomplished. Magnetoencephalography dipoles were identified in 47 patients (89%), in 46 of whom the lesions were resected. This included 20 (80%) of 25 anterior temporal lobe (ATL) cases, and 26 (93%) of 28 extratemporal lobe (ETL) cases. Of the six patients who underwent extensive ATL resections, three (50%) were seizure free. Of 14 patients who underwent partial/no resection of the ATL, seven (50%) were seizure free. There was no clear relation between MS image spatial distribution and surgery-related outcome. Of the seven ATL cases with hippocampal atrophy, five patients (71%) were seizure free. Of 12 ETL cases (three lesional), 10 patients (83%) were seizure free. Of 14 patients who underwent partial/no ETL resections (three lesional), two (14%) were seizure free. Of five nonlesional ETL cases with focal MS image dipoles, four patients (80%) were seizure free. Of five nonlesional ETL cases with regional dipoles, three patients (60%) were seizure free. Of eight ETL cases with multiple MS image dipoles, two patients (25%) were seizure free. Spatial agreement of MS imaging and electrographic data had no apparent effect on outcome of either ATL or ETL cases. Conclusions. Nonlesional ETL cases with focal (and in some cases multiple or regional) epileptiform MS image dipole distributions benefit significantly from inclusion of the MS image epileptiform focus in the resections. Nonlesional ETL cases suitable for GKS may similarly benefit from including the MS image focus in the irradiated area.
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Xie, Jiangjian, Anqi Li, Junguo Zhang, and Zhean Cheng. "An Integrated Wildlife Recognition Model Based on Multi-Branch Aggregation and Squeeze-And-Excitation Network." Applied Sciences 9, no. 14 (July 12, 2019): 2794. http://dx.doi.org/10.3390/app9142794.
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Infrared camera trapping, which helps capture large volumes of wildlife images, is a widely-used, non-intrusive monitoring method in wildlife surveillance. This method can greatly reduce the workload of zoologists through automatic image identification. To achieve higher accuracy in wildlife recognition, the integrated model based on multi-branch aggregation and Squeeze-and-Excitation network is introduced. This model adopts multi-branch aggregation transformation to extract features, and uses Squeeze-and-Excitation block to adaptively recalibrate channel-wise feature responses based on explicit self-mapped interdependencies between channels. The efficacy of the integrated model is tested on two datasets: the Snapshot Serengeti dataset and our own dataset. From experimental results on the Snapshot Serengeti dataset, the integrated model applies to the recognition of 26 wildlife species, with the highest accuracies in Top-1 (when the correct class is the most probable class) and Top-5 (when the correct class is within the five most probable classes) at 95.3% and 98.8%, respectively. Compared with the ROI-CNN algorithm and ResNet (Deep Residual Network), on our own dataset, the integrated model, shows a maximum improvement of 4.4% in recognition accuracy.
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Wibisono, Christian. "ANALISIS FAKTOR KOMUNIKASI ONLINE DAN BRAND LOVE MANCHESTER UNITED SEBAGAI SPORTS BRAND PADA PENGGEMAR MANCHESTER UNITED DI BANDUNG." Bina Ekonomi 22, no. 1 (December 18, 2018): 1–10. http://dx.doi.org/10.26593/be.v22i1.3104.1-9.
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A brand for sport teams nowadays is a very valuable asset (Burrow, 2013). Sports team need to improve brand image like businesses do. These sports team conduct online communication to improve their brand image. Manchester United do this by managing their official Facebook, Twitter, Instagram and official website account. With so many factors to manage and monitor, the task of managing this online communication channel can be a very demanding task. The objective of this paper is to try to make it less complicated. From twenty indicators of online communication, using factor analysis, the result is five factors namely: twitter junkie, website user, IG and FB user, news hunter, and brand love.Keywords: brand love; sports marketing; online communication; factor analysis
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., Christina. "Strategi Pemasaran TVRI dalam Menaikkan Brand Image." Jurnal Komunikasi Nusantara 2, no. 1 (May 20, 2020): 67–79. http://dx.doi.org/10.33366/jkn.v2i1.37.
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Televisi Republik Indonesia (TVRI) is a state television. Since its establishment on August 24, 1962, TVRI has indeed experienced various phases in its life. Through Law No.32 of 2002 concerning Broadcasting, TVRI was established as a Public Broadcasting Institution (LPP). LPP is a broadcasting institution in the form of a legal entity established by the state, independent, neutral, non-commercial and functions to provide services for the benefit of the community. At present, TVRI has changed its Director since March 2018 namely Helmi Yahya, known as the King of Quiz. With the new Managing Director, TVRI began many enthusiasts who watched with new faces and more interesting programs. This research uses a qualitative research method, and is descriptive in nature which will later explain the marketing strategy of TVRI which has begun to attract the public and an increase in rating and audience share. This study uses interview techniques and data collection from social media and the internet as well as journals, books as primary and secondary data. The marketing strategy undertaken by TVRI includes five rebranding media including channel branding packages, promo on air, news packages, social media, and off air promotions. Of all the rebranding media, the most effective media is social media because aside from being a new media or digital marketing, it can reach all people, both TVRI viewers and private TV viewers
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PARK, SEUNG MAN, YOUNG UK MIN, MYUNG JIN KANG, KYUNG CHUN KIM, and HO SEONG JI. "IN VITROHEMODYNAMIC STUDY ON THE STENOTIC RIGHT CORONARY ARTERY USING EXPERIMENTAL AND NUMERICAL ANALYSIS." Journal of Mechanics in Medicine and Biology 10, no. 04 (December 2010): 695–712. http://dx.doi.org/10.1142/s0219519410003812.
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To investigate the hemodynamic and hemorheological features related to circulatory diseases, in vitro experiments are carried out using a micro-particle image velocimetry (μ-PIV) technique. Numerical simulations using a commercial computational fluid dynamics (CFD) code are also performed to compare with the experimental results. Five different non-Newtonian blood models and a Newtonian water model are employed to investigate the blood flow characteristics through a stenotic right coronary artery (RCA). The in vitro model is made of two-dimensional (2D) polydimethylsiloxane (PDMS) channel based on the clinical angiogram of the RCA with stenotic lesion. The hemodynamic and hemorheologic behaviors in the control volume near the stenotic lesion are evaluated by velocity profiles. The predicted and measured velocity profiles at the center of the channel have a reasonable agreement.
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Yamashita, K., K. Shimizu, and S. Haga. "Navigation of sentinel node biopsy and selective axillary dissection using 3D-CT mammary lymphography with the arm lymph flow enhancement." Journal of Clinical Oncology 29, no. 27_suppl (September 20, 2011): 98. http://dx.doi.org/10.1200/jco.2011.29.27_suppl.98.
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98 Background: In early breast cancer, the presence of metastasis in axillary lymph nodes (AN) is an important factor in prognosis and further treatment. However, AN dissection causes many complications such as contracture of shoulder joint, lymph edema, and paralysis of upper extremities. Sentinel node (SN) biopsy provides us a valuable information about no need to dissect AN for node-negative patients. However, on node-positive patients, the conventional AN dissection has been performed. 3D-CT lymphography (LG) can show the precise individual lymphatic flow not only from the breast tumor to SN but also from SN to venous angle, which means breast lymphatic channel. We applied 3D-CT LG to distinguish them from the arm channel to avoid any arm complications. Methods: 3D-CT LG was performed before surgery to mark SN on the skin. Above the tumor and near the areola and the arm pit, 2 ml of iopamidol 300 was injected subcutaneously. Images of CT scan were taken at 1, 3, and 10 min after injection. They were reconstructed to produce a 3D image of lymph ducts and lymph nodes by the volume rendering software. The axillary node groups were classified to 5 groups as described before. The arm lymph channel was classified to 4 regions. SN biopsy and AN sampling were performed by dye-staining method using endoscopy. Results: We have performed 3D-CT LG on 200 patients and evaluated the arm lymph flow on 50 patients. The average age was 55.1 years old. The average tumor size was 2.4 cm. The average sampled number of SN was 2.3. The arm lymph channels were observed at 10 minutes after injection. They were divided into four directions: around and above the axillary vein (64%), the axilla (12%), the supraclavicule (8%), and the lateral chest (16%). The connection between the breast and the arm channel was observed in each five groups of axillary nodes shown by 3D-CT LG. We could detect their position on the axillary lymphatic mapping by 3D-CT LG during surgery. The selective axillary dissection can be performed by avoiding the arm lymph channel. Conclusions: The arm lymph channel can be observed by 3D-CT LG with the arm enhancement, which will help the selective axillary dissection to prevent the arm lymph edema.
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Chen, Zhong, Shengwu Xiong, Qingzhou Mao, Zhixiang Fang, and Xiaohan Yu. "An Improved Saliency Detection Approach for Flying Apsaras in the Dunhuang Grotto Murals, China." Advances in Multimedia 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/625915.
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Saliency can be described as the ability of an item to be detected from its background in any particular scene, and it aims to estimate the probable location of the salient objects. Due to the salient map that computed by local contrast features can extract and highlight the edge parts including painting lines of Flying Apsaras, in this paper, we proposed an improved approach based on a frequency-tuned method for visual saliency detection of Flying Apsaras in the Dunhuang Grotto Murals, China. This improved saliency detection approach comprises three important steps: (1) image color and gray channel decomposition; (2) gray feature value computation and color channel convolution; (3) visual saliency definition based on normalization of previous visual saliency and spatial attention function. Unlike existing approaches that rely on many complex image features, this proposed approach only used local contrast and spatial attention information to simulate human’s visual attention stimuli. This improved approach resulted in a much more efficient salient map in the aspect of computing performance. Furthermore, experimental results on the dataset of Flying Apsaras in the Dunhuang Grotto Murals showed that the proposed visual saliency detection approach is very effective when compared with five other state-of-the-art approaches.
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TÖRNBLOM, OLLE, BJÖRN LINDGREN, and ARNE V. JOHANSSON. "The separating flow in a plane asymmetric diffuser with 8.5° opening angle: mean flow and turbulence statistics, temporal behaviour and flow structures." Journal of Fluid Mechanics 636 (September 25, 2009): 337–70. http://dx.doi.org/10.1017/s0022112009007940.
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The flow in a plane asymmetric diffuser with an opening angle of 8.5° has been studied experimentally using time-resolving stereoscopic particle image velocimetry. The inlet condition is fully developed turbulent channel flow at a Reynolds number based on the inlet channel height and bulk velocity of Re = 38000. All mean velocity and Reynolds stress components have been measured. A separated region is found on the inclined wall with a mean separation point at 7.4 and a mean reattachment point at 30.5 inlet channel heights downstream the diffuser inlet (the inclined wall ends 24.8 channel heights downstream the inlet). Instantaneous flow reversal never occurs upstream of five inlet channel heights but may occur far downstream the point of reattachment. A strong shear layer in which high rates of turbulence production are found is located in a region outside the separation. The static wall pressure through the diffuser is presented and used in an analysis of the balance between pressure forces and momentum change. It is demonstrated that production of turbulence causes a major part of the losses of mean flow kinetic energy. The character of the large turbulence structures is investigated by means of time-resolved sequences of velocity fields and spatial auto-correlation functions. Pronounced inclined structures are observed in the spanwise velocity and it is suggested that these are due to the legs of hairpin-like vortices.
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Praljak, Niksa, Brandon Shipley, Ayesha Gonzales, Utku Goreke, Shamreen Iram, Gundeep Singh, Ailis Hill, Umut A. Gurkan, and Michael Hinczewski. "A Deep Learning Framework for Sickle Cell Disease Microfluidic Biomarker Assays." Blood 136, Supplement 1 (November 5, 2020): 15–16. http://dx.doi.org/10.1182/blood-2020-141693.
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Introduction: Vaso-occlusive crises (VOCs) are a leading cause of morbidity and early mortality in individuals with sickle cell disease (SCD). These crises are triggered by sickle red blood cell (sRBC) aggregation in blood vessels and are influenced by factors such as enhanced sRBC and white blood cell (WBC) adhesion to inflamed endothelium. Advances in microfluidic biomarker assays (i.e., SCD Biochip systems) have led to clinical studies of blood cell adhesion onto endothelial proteins, including, fibronectin, laminin, P-selectin, ICAM-1, functionalized in microchannels. These microfluidic assays allow mimicking the physiological aspects of human microvasculature and help characterize biomechanical properties of adhered sRBCs under flow. However, analysis of the microfluidic biomarker assay data has so far relied on manual cell counting and exhaustive visual morphological characterization of cells by trained personnel. Integrating deep learning algorithms with microscopic imaging of adhesion protein functionalized microfluidic channels can accelerate and standardize accurate classification of blood cells in microfluidic biomarker assays. Here we present a deep learning approach into a general-purpose analytical tool covering a wide range of conditions: channels functionalized with different proteins (laminin or P-selectin), with varying degrees of adhesion by both sRBCs and WBCs, and in both normoxic and hypoxic environments. Methods: Our neural networks were trained on a repository of manually labeled SCD Biochip microfluidic biomarker assay whole channel images. Each channel contained adhered cells pertaining to clinical whole blood under constant shear stress of 0.1 Pa, mimicking physiological levels in post-capillary venules. The machine learning (ML) framework consists of two phases: Phase I segments pixels belonging to blood cells adhered to the microfluidic channel surface, while Phase II associates pixel clusters with specific cell types (sRBCs or WBCs). Phase I is implemented through an ensemble of seven generative fully convolutional neural networks, and Phase II is an ensemble of five neural networks based on a Resnet50 backbone. Each pixel cluster is given a probability of belonging to one of three classes: adhered sRBC, adhered WBC, or non-adhered / other. Results and Discussion: We applied our trained ML framework to 107 novel whole channel images not used during training and compared the results against counts from human experts. As seen in Fig. 1A, there was excellent agreement in counts across all protein and cell types investigated: sRBCs adhered to laminin, sRBCs adhered to P-selectin, and WBCs adhered to P-selectin. Not only was the approach able to handle surfaces functionalized with different proteins, but it also performed well for high cell density images (up to 5000 cells per image) in both normoxic and hypoxic conditions (Fig. 1B). The average uncertainty for the ML counts, obtained from accuracy metrics on the test dataset, was 3%. This uncertainty is a significant improvement on the 20% average uncertainty of the human counts, estimated from the variance in repeated manual analyses of the images. Moreover, manual classification of each image may take up to 2 hours, versus about 6 minutes per image for the ML analysis. Thus, ML provides greater consistency in the classification at a fraction of the processing time. To assess which features the network used to distinguish adhered cells, we generated class activation maps (Fig. 1C-E). These heat maps indicate the regions of focus for the algorithm in making each classification decision. Intriguingly, the highlighted features were similar to those used by human experts: the dimple in partially sickled RBCs, the sharp endpoints for highly sickled RBCs, and the uniform curvature of the WBCs. Overall the robust performance of the ML approach in our study sets the stage for generalizing it to other endothelial proteins and experimental conditions, a first step toward a universal microfluidic ML framework targeting blood disorders. Such a framework would not only be able to integrate advanced biophysical characterization into fast, point-of-care diagnostic devices, but also provide a standardized and reliable way of monitoring patients undergoing targeted therapies and curative interventions, including, stem cell and gene-based therapies for SCD. Disclosures Gurkan: Dx Now Inc.: Patents & Royalties; Xatek Inc.: Patents & Royalties; BioChip Labs: Patents & Royalties; Hemex Health, Inc.: Consultancy, Current Employment, Patents & Royalties, Research Funding.
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Zha, Lei, Yu Yang, Zicheng Lai, Ziwei Zhang, and Juan Wen. "A Lightweight Dense Connected Approach with Attention on Single Image Super-Resolution." Electronics 10, no. 11 (May 22, 2021): 1234. http://dx.doi.org/10.3390/electronics10111234.
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In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training. To deal with deep model training problems, researchers utilize dense skip connections to promote the model’s feature representation ability by reusing deep features of different receptive fields. Benefiting from the dense connection block, SRDensenet has achieved excellent performance in SISR. Despite the fact that the dense connected structure can provide rich information, it will also introduce redundant and useless information. To tackle this problem, in this paper, we propose a Lightweight Dense Connected Approach with Attention for Single Image Super-Resolution (LDCASR), which employs the attention mechanism to extract useful information in channel dimension. Particularly, we propose the recursive dense group (RDG), consisting of Dense Attention Blocks (DABs), which can obtain more significant representations by extracting deep features with the aid of both dense connections and the attention module, making our whole network attach importance to learning more advanced feature information. Additionally, we introduce the group convolution in DABs, which can reduce the number of parameters to 0.6 M. Extensive experiments on benchmark datasets demonstrate the superiority of our proposed method over five chosen SISR methods.
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Qiao, Wenting, Qiangwei Liu, Xiaoguang Wu, Biao Ma, and Gang Li. "Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module." Sensors 21, no. 9 (April 21, 2021): 2902. http://dx.doi.org/10.3390/s21092902.
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Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.
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Gagliardo, Cesare, Massimo Midiri, Roberto Cannella, Alessandro Napoli, Paul Wragg, Giorgio Collura, Maurizio Marrale, Tommaso Vincenzo Bartolotta, Carlo Catalano, and Roberto Lagalla. "Transcranial magnetic resonance-guided focused ultrasound surgery at 1.5T: a technical note." Neuroradiology Journal 32, no. 2 (December 18, 2018): 132–38. http://dx.doi.org/10.1177/1971400918818743.
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Magnetic resonance-guided focused ultrasound is one of the emerging non-invasive technologies offering both image guidance and thermal monitoring. In recent years transcranial application of this technology is starting to impact heavily the neuroscience field. We present here the imaging protocol and the technological methods successfully used with a transcranial magnetic resonance-guided focused ultrasound system certified for clinical treatments of functional neurological disorders, integrated for the first time with a 1.5T magnetic resonance scanner. Compared to the body radiofrequency coil (the one commonly used with transcranial magnetic resonance-guided focused ultrasound system integrated with 3T magnetic resonance scanners), the use of a dedicated two channel coil enabled a signal-to-noise ratio gain up to five times higher.
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Cappella, Paolo, and Fabio Gasparri. "Highly Multiplexed Phenotypic Imaging for Cell Proliferation Studies." Journal of Biomolecular Screening 19, no. 1 (July 29, 2013): 145–57. http://dx.doi.org/10.1177/1087057113495712.
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The application of multiplexed imaging technologies in phenotypic drug discovery (PDD) enables profiling of complex cellular perturbations in response to drug treatment. High-content analysis (HCA) is among the most pursued approaches in PDD, with a proven capability to identify compounds with a given cellular mechanism of action (MOA), as well as to unveil unexpected drug cellular activities. The ability of fluorescent image-based cytometric techniques to dissect the phenotypic heterogeneity of cell populations depends on the degree of multiplexing achievable. At present, most high-content assays employ up to four cellular markers separately detected in distinct fluorescence channels. We explored the possibility to increase HCA multiplexing through analysis of multiple proliferation markers in the same fluorescence channel by taking advantage of the different timing of antigen appearance during the cell cycle, or differential intracellular localization. Simultaneous analysis of DAPI staining and five immunofluorescence markers (BrdU incorporation, active caspase-3, phospho-histone H3, phospho-S6, and Ki-67) resulted in the first six-marker high-content assay readily applicable to compound MOA studies. This approach allows detection of rare cell subpopulations, unveiling a high degree of phenotypic heterogeneity in exponentially growing cell cultures and variability in the individual cell response to antiproliferative drugs.
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Alhudhaif, Adi, Zafer Cömert, and Kemal Polat. "Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm." PeerJ Computer Science 7 (February 23, 2021): e405. http://dx.doi.org/10.7717/peerj-cs.405.
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Background Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone. Methods In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes. Results The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.
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Hrysiuk, V. P. "The Representation of sexuality in humor programs on Ukrainian television." Science and Education a New Dimension IX(254), no. 46 (June 30, 2021): 62–64. http://dx.doi.org/10.31174/send-hs2021-254ix46-15.
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The process of sexuality representation in humor programs on Ukrainian television in 2017-2020 is considered on example of the "Studia Kvartal 95" (1 + 1), "Diesel Show" (ICTV) and "Varyaty Show" (New Channel) content. A model of creating a sexually marked humor content on Ukrainian television has also been designed, consisting of five elements: 1) stereotype and / or cliché; 2) topical and / or interesting topic for the audience; 3) artistic conjecture and / or hyperbolization, 4) real context; 5) the image and charisma of the speaker. These elements are based on seven categories, which were represented by Dutch scholars Monek Buisen and Patti Valkenburg: farce, clowning, surprise, misunderstanding, irony, satire and parody. The relevance of the study is due to the need of regulating the process of media sexualization by setting the borders and ways to distribute relevant content.
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Wang, Zhang, and Wang. "Attention Bilinear Pooling for Fine-Grained Classification." Symmetry 11, no. 8 (August 9, 2019): 1033. http://dx.doi.org/10.3390/sym11081033.
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Fine-grained image classification is a challenging problem because of its large intra-class differences and low inter-class variance. Bilinear pooling based models have been shown to be effective at fine-grained classification, while most previous approaches neglect the fact that distinctive features or modeling distinguishing regions usually have an important role in solving the fine-grained problem. In this paper, we propose a novel convolutional neural network framework, i.e., attention bilinear pooling, for fine-grained classification with attention. This framework can learn the distinctive feature information from the channel or spatial attention. Specifically, the channel and spatial attention allows the network to better focus on where the key targets are in the image. This paper embeds spatial attention and channel attention in the underlying network architecture to better represent image features. To further explore the differences between channels and spatial attention, we propose channel attention bilinear pooling (CAB), spatial attention bilinear pooling (SAB), channel spatial attention bilinear pooling (CSAB), and spatial channel attention bilinear pooling (SCAB) as four alternative frames. A variety of experiments on several datasets show that our proposed method has a very impressive performance compared to other methods based on bilinear pooling.
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Jókay, Matúš, and Martin Košdy. "STEGANOGRAPHIC FILE SYSTEM BASED ON JPEG FILES." Tatra Mountains Mathematical Publications 57, no. 1 (November 1, 2013): 65–83. http://dx.doi.org/10.2478/tmmp-2013-0036.
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ABSTRACTA steganographic system provides a hidden communication channel in background of a public channel. The existence of the hidden channel must remain secret, i.e. the adversary cannot decide whether the public channel contains any covert information or not. The public channel that is used in construction of a steganographic system can often be embedded in a static file (medium), that is called a carrier (if the steganographic information is present). Most of the current research focuses on a single medium. The most suitable types of media, such as images or music files, contain a lot of redundancy. Small changes in the redundant parts are not easily detected. However, new methods for the detection of this information are developed along with the new algorithms for embedding the hidden information. Our work describes a new steganographic system design, where the hidden information is spread among many static images in a form of a virtual steganographic filesystem. We note that the implementation of the system must also take into account “steganographic side-channels”, i.e., some information channels that are present in the operating system (in our case Linux, and Android) that leak information about the presence of the hidden channel.
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Navarro, Pedro J., Leanne Miller, Alberto Gila-Navarro, María Victoria Díaz-Galián, Diego J. Aguila, and Marcos Egea-Cortines. "3DeepM: An Ad Hoc Architecture Based on Deep Learning Methods for Multispectral Image Classification." Remote Sensing 13, no. 4 (February 17, 2021): 729. http://dx.doi.org/10.3390/rs13040729.
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Current predefined architectures for deep learning are computationally very heavy and use tens of millions of parameters. Thus, computational costs may be prohibitive for many experimental or technological setups. We developed an ad hoc architecture for the classification of multispectral images using deep learning techniques. The architecture, called 3DeepM, is composed of 3D filter banks especially designed for the extraction of spatial-spectral features in multichannel images. The new architecture has been tested on a sample of 12210 multispectral images of seedless table grape varieties: Autumn Royal, Crimson Seedless, Itum4, Itum5 and Itum9. 3DeepM was able to classify 100% of the images and obtained the best overall results in terms of accuracy, number of classes, number of parameters and training time compared to similar work. In addition, this paper presents a flexible and reconfigurable computer vision system designed for the acquisition of multispectral images in the range of 400 nm to 1000 nm. The vision system enabled the creation of the first dataset consisting of 12210 37-channel multispectral images (12 VIS + 25 IR) of five seedless table grape varieties that have been used to validate the 3DeepM architecture. Compared to predefined classification architectures such as AlexNet, ResNet or ad hoc architectures with a very high number of parameters, 3DeepM shows the best classification performance despite using 130-fold fewer parameters than the architecture to which it was compared. 3DeepM can be used in a multitude of applications that use multispectral images, such as remote sensing or medical diagnosis. In addition, the small number of parameters of 3DeepM make it ideal for application in online classification systems aboard autonomous robots or unmanned vehicles.
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Federico, S., C. Bellecci, and R. L. Walko. "A LEPS approach to the predictability of intense rain storms in the Central Mediterranean basin." Advances in Geosciences 16 (April 9, 2008): 89–95. http://dx.doi.org/10.5194/adgeo-16-89-2008.
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Abstract. This study investigates a method for best member selection of a Limited area model Ensemble Prediction System (LEPS) with the goal to increase quantitative precipitation forecast. A case study that occurred between 22-24 May 2002 over Calabria, southern Italy, is discussed. Mediterranean storms often develop under upper level disturbances which are usually associated with high values of potential vorticity. Anomalously high values of potential vorticity can be identified by the METEOSAT water vapor channel centered around 6.3 μm because they are associated with dark band on the METEOSAT image. This signature offers a chance to identify the upper level disturbance that can be exploited in data void countries as Calabria. The working hypothesis is that the uncertainty in the representation of the upper-level disturbance has a major impact on the precipitation forecast. This issue is utilized in an ensemble forecast where member forecasts are compatible with the analysis and forecast errors. These members are grouped in five clusters by a hierarchical clustering technique which utilizes the height of the dynamical tropopause to compute distances between members. Therefore the members of a cluster have a similar representation of the upper level disturbance. For each cluster a representative member is selected and its pseudo water vapor image is compared with the corresponding METEOSAT 7 water vapor image at a specific time, antecedent to the rain occurrence over Calabria. The subjective evaluation of the comparison allows to gain physical insight in the storm evolution and to select representative members which are more in agreement with the METEOSAT image. Results, even if for a case study, show the feasibility of the methodology that, if confirmed by further investigations, could be valuable in data void countries as the central Mediterranean basin.
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Pan, Xuran, Fan Yang, Lianru Gao, Zhengchao Chen, Bing Zhang, Hairui Fan, and Jinchang Ren. "Building Extraction from High-Resolution Aerial Imagery Using a Generative Adversarial Network with Spatial and Channel Attention Mechanisms." Remote Sensing 11, no. 8 (April 15, 2019): 917. http://dx.doi.org/10.3390/rs11080917.
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Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper, we propose a generative adversarial network with spatial and channel attention mechanisms (GAN-SCA) for the robust segmentation of buildings in remote sensing images. The segmentation network (generator) of the proposed framework is composed of the well-known semantic segmentation architecture (U-Net) and the spatial and channel attention mechanisms (SCA). The adoption of SCA enables the segmentation network to selectively enhance more useful features in specific positions and channels and enables improved results closer to the ground truth. The discriminator is an adversarial network with channel attention mechanisms that can properly discriminate the outputs of the generator and the ground truth maps. The segmentation network and adversarial network are trained in an alternating fashion on the Inria aerial image labeling dataset and Massachusetts buildings dataset. Experimental results show that the proposed GAN-SCA achieves a higher score (the overall accuracy and intersection over the union of Inria aerial image labeling dataset are 96.61% and 77.75%, respectively, and the F1-measure of the Massachusetts buildings dataset is 96.36%) and outperforms several state-of-the-art approaches.
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Bellini, Silvia, Simone Aiolfi, and Maria Grazia Cardinali. "How to Promote Healthier Shopping Behaviour: Which Are the Most Effective Retail Marketing’ Levers in E-Commerce Grocery." International Journal of Business and Management 16, no. 3 (February 22, 2021): 101. http://dx.doi.org/10.5539/ijbm.v16n3p101.
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The World Health Organization (WHO) suggests people to eat at least five portion of fruits and vegetables a day, but the level of consumption of this category is far from been reached. Considering that the majority of food purchases occurs in grocery context, increasingly in e-commerce channels, understanding how retailers could improve food choice, both in physical and digital stores, is paramount to healthier living. This paper aims to understand which are the most effective retail marketing’ levers in stimulating impulse buying in fruits and vegetables category, therefore in promoting healthier shopping behavior. Since fruit and vegetable is known for its healthy vocation and its role in differentiating and enhancing the perceived image of retailers, this category is the ideal place to host nutritional marketing initiatives. We used a quantitative survey method to explore shoppers’ behaviour in an online setting, focusing on fruit and vegetables’ category. Respondents were exposed to nine marketing stimuli, according to different communication contents (price versus non price). All the data was considered for linear regression analysis. Our results show that the pre-shopping preparation has an effect on purchasing behaviour, limiting its impulsiveness. Furthermore, price levers and communication levers influence the intention to buy impulse in the online channel, with the latter more effective than the other ones. Therefore, as this process takes place in the digital context, marketing efforts need to focus on dimensions that increase the propensity to make impulse purchases online: communicative and price stimuli.
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Kocaman, Selahattin, and Kaan Dal. "A New Experimental Study and SPH Comparison for the Sequential Dam-Break Problem." Journal of Marine Science and Engineering 8, no. 11 (November 11, 2020): 905. http://dx.doi.org/10.3390/jmse8110905.
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The floods following the event of a dam collapse can have a significant impact on the downstream environment and ecology. Due to the limited number of real-case data for dam-break floods, laboratory experiments and numerical models are used to understand the complex flow behavior and to analyze the impact of the dam-break wave for different scenarios. In this study, a newly designed experimental campaign was conducted for the sequential dam-break problem in a rectangular channel with a steep slope, and the obtained results were compared against those of a particle-based numerical model. The laboratory tests permitted a better understanding of the physical process, highlighting five successive stages observed in the downstream reservoirs: dam-break wave propagation, overtopping, reflection wave, run-up, and oscillations. Experimental data were acquired using a virtual wave probe based on an image processing technique. A professional camera and a smartphone camera were used to obtain the footage of the experiment to examine the effect of the resolution and frame rate on image processing. The numerical results were obtained through the Smoothed Particle Hydrodynamics (SPH) method using free DualSPHysics software. The experimental and numerical results were in good agreement generally. Hence, the presented data can be used as a benchmark in future studies to validate the SPH and other Computational Fluid Dynamics (CFD) methods.
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Herman, Jay, Guoyong Wen, Alexander Marshak, Karin Blank, Liang Huang, Alexander Cede, Nader Abuhassan, and Matthew Kowalewski. "Reduction in 317–780 nm radiance reflected from the sunlit Earth during the eclipse of 21 August 2017." Atmospheric Measurement Techniques 11, no. 7 (July 25, 2018): 4373–88. http://dx.doi.org/10.5194/amt-11-4373-2018.
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Abstract. Ten wavelength channels of calibrated radiance image data from the sunlit Earth are obtained every 65 min during Northern Hemisphere summer from the EPIC (Earth Polychromatic Imaging Camera) instrument on the DSCOVR (Deep Space Climate Observatory) satellite located near the Earth–Sun Lagrange 1 point (L1), about 1.5 million km from the Earth. The L1 location permitted seven observations of the Moon's shadow on the Earth for about 3 h during the 21 August 2017 eclipse. Two of the observations were timed to coincide with totality over Casper, Wyoming, and Columbia, Missouri. Since the solar irradiances within five channels (λi=388, 443, 551, 680, and 780 nm) are not strongly absorbed in the atmosphere, they can be used for characterizing the eclipse reduction in reflected radiances for the Earth's sunlit face containing the eclipse shadow. Five channels (λi=317.5, 325, 340, 688, and 764 nm) that are partially absorbed in the atmosphere give consistent reductions compared to the non-absorbed channels. This indicates that cloud reflectivities dominate the 317.5–780 nm radiances reflected back to space from the sunlit Earth's disk with a significant contribution from Rayleigh scattering for the shorter wavelengths. An estimated reduction of 10 % was obtained for spectrally integrated radiance (387 to 781 nm) reflected from the sunlit Earth towards L1 for two sets of observations on 21 August 2017, while the shadow was in the vicinity of Casper, Wyoming (42.8666∘ N, 106.3131∘ W; centered on 17:44:50 UTC), and Columbia, Missouri (38.9517∘ N, 92.3341∘ W; centered on 18:14:50 UTC). In contrast, when non-eclipse days (20 and 23 August) are compared for each wavelength channel, the change in reflected light is much smaller (less than 1 % for 443 nm compared to 9 % (Casper) and 8 % (Columbia) during the eclipse). Also measured was the ratio REN(λi) of reflected radiance on adjacent non-eclipse days divided by radiances centered in the eclipse totality region with the same geometry for all 10 wavelength channels. The measured REN(443 nm) was smaller for Columbia (169) than for Casper (935), because Columbia had more cloud cover than Casper. REN(λi) forms a useful test of a 3-D radiative transfer models for an eclipse in the presence of optically thin clouds. Specific values measured at Casper with thin clouds are REN(340 nm) = 475, REN(388 nm) = 3500, REN(443 nm) = 935, REN(551 nm) = 5455, REN(680 nm) = 220, and REN(780 nm) = 395. Some of the variability is caused by changing cloud amounts within the moving region of totality during the 2.7 min needed to measure all 10 wavelength channels.
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Hu, Xin, Jun Liu, Jie Ma, Yudai Pan, and Lingling Zhang. "Fine-Grained 3D-Attention Prototypes for Few-Shot Learning." Neural Computation 32, no. 9 (September 2020): 1664–84. http://dx.doi.org/10.1162/neco_a_01302.
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In the real world, a limited number of labeled finely grained images per class can hardly represent the class distribution effectively. Due to the more subtle visual differences in fine-grained images than simple images with obvious objects, that is, there exist smaller interclass and larger intraclass variations. To solve these issues, we propose an end-to-end attention-based model for fine-grained few-shot image classification (AFG) with the recent episode training strategy. It is composed mainly of a feature learning module, an image reconstruction module, and a label distribution module. The feature learning module mainly devises a 3D-Attention mechanism, which considers both the spatial positions and different channel attentions of the image features, in order to learn more discriminative local features to better represent the class distribution. The image reconstruction module calculates the mappings between local features and the original images. It is constrained by a designed loss function as auxiliary supervised information, so that the learning of each local feature does not need extra annotations. The label distribution module is used to predict the label distribution of a given unlabeled sample, and we use the local features to represent the image features for classification. By conducting comprehensive experiments on Mini-ImageNet and three fine-grained data sets, we demonstrate that the proposed model achieves superior performance over the competitors.
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Hu, Ying-Li, and Shu Chien. "Effects of Shear Stress on Protein Kinase C Distribution in Endothelial Cells." Journal of Histochemistry & Cytochemistry 45, no. 2 (February 1997): 237–49. http://dx.doi.org/10.1177/002215549704500209.
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We studied the effects of shear stress on protein kinase C (PKC) in cultured human umbilical vein endothelial cells by use of a flow channel and a monoclonal antibody (MAb 1.3) that recognizes the PKC β-isozyme. The fluorescence intensity (FI) of the secondary antibody, crystalline tetramethylrhodamine isothiocyanate, was determined by image analysis. The results on each of five shearing experiments were normalized by using the paired stationary control. After 30-min shearing at 2 N/m2, FI per cell increased to 1.6 times that of control, as did the mean FI per unit cell area. The FI per unit stained area and the stained area/cell area ratio were also increased significantly by shearing. The distribution of immunostaining in each cell was determined for its cortical, cytoplasmic, perinuclear, and nuclear regions. The normalized FI per unit area in all four regions and the stained area/cell area ratio in cortical and cytoplasmic regions were significantly higher in the sheared cells than in control; the increases were greatest in the cortical area. Double staining with rhodamine-phalloidin and MAb 1.3 showed the association of actin with the PKC isozyme in both stationary and sheared cells.
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Szepieniec, Wioletta Katarzyna, Hanna Szweda, Maksym Wróblewski, and Paweł Szymanowski. "Three-Dimensional Urethral Profilometry—A Global Urethral Pressure Assessment Method." Diagnostics 11, no. 4 (April 12, 2021): 687. http://dx.doi.org/10.3390/diagnostics11040687.
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Background: To present a new method of urethral pressure examination, and to evaluate diagnostic capabilities of three-dimensional profilometry, as an alternative to classical urethral profile (UPP). Using five channel catheters and dedicated software, a global urethral pressure image is obtained. The method eliminates the main limitation of classical urethral profilometry, where the catheter orientation determines the pressure picture limited to only one point in the urethral circumference; we observed up to 50% differences in pressure measures depending on the point of urethral circumference where the measurement was taken. Methods: This is a preliminary study containing a method presentation and analysis of the use in varied clinical cases of either healthy patients or patients with lower urinary tract symptoms (LUTS). The article includes a technique and equipment description and a full evaluation of selected cases, including three-dimensional urethral pressure distribution graphics. Results and Conclusions: Three-dimensional profilometry compared to the classical technique is comparable regarding the time, cost, technical difficulty and patient discomfort. At the same time, we obtained much more data on the urethral pressure and its distribution. The results are easy to interpret due to the 3D movable graphics created automatically by the dedicated software.