Journal articles: 'Image cropping'

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Li, Ya Feng, and Ying Lin. "Adaptive Image Cropping Based Depth of Field." Advanced Engineering Forum 6-7 (September 2012): 895–99. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.895.

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The communication and sharing of visual media show a strong cross-platform feature. Massive images are shown on various devices with different resolution. Automatic image cropping, usually used to change the resolution of image, often discards important content in the image. This paper proposed a novel adaptive photo cropping method. The main idea is exploiting the characteristics of photograph aesthetics in photograph works. The algorithm infers the intention of photographer according to the depth of field in the image. The effects brought out by the focus and unfocus are utilized to exact importance information of the image. So, the method can be more satisfied with the subjective evaluation. And, it has advantage in term of computational speed. Experimentations are presented to demonstrate the validity of proposed method.

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Gao, Shangbing, Youdong Zhang, Wanli Feng, and Dashan Chen. "Image Cropping by Patches Dissimilarities." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 8 (August 31, 2015): 79–88. http://dx.doi.org/10.14257/ijsip.2015.8.8.09.

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Yuhandri. "Perbandingan Metode Cropping pada Sebuah Citra untuk Pengambilan Motif Tertentu pada Kain Songket Sumatera Barat." Jurnal KomtekInfo 6, no. 1 (June 1, 2019): 97–107. http://dx.doi.org/10.35134/komtekinfo.v6i1.45.

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At the time of image processing where we only need a certain part of an image according to the needs called the Region of Interest (ROI), in order to obtain that, the processing is carried out in a cropping process. Cropping is mostly done by researchers, especially those who research in the field of image processing in order to do data processing on an image, the results of cropping process on an image are usually done to make it easier for researchers to focus on something that is needed only. In this study is to compare existing cropping methods to get a motif found in an image of West Sumatra songket fabric. In this study using the method of cropping rectangle, square, circle, ellipse, polygon and tested using the Matlab programming language. The results of comparison of 5 cropping methods for taking certain motifs on the songket image with 5 different songket image samples, shows that the best results are obtained by using the polygon method. Polygon method can reach certain coordinate points in a songket image, so that the results of cropping are better and other motives that are carried along during the cropping process can be reduced.

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Darwis, Dedi, Akmal Junaidi, Dewi Asiah Shofiana, and Wamiliana. "A New Digital Image Steganography Based on Center Embedded Pixel Positioning." Cybernetics and Information Technologies 21, no. 2 (June 1, 2021): 89–104. http://dx.doi.org/10.2478/cait-2021-0021.

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Abstract In this study we propose a new approach to tackle the cropping problem in steganography which is called Center Embedded Pixel Positioning (CEPP) which is based on Least Significant Bit (LSB) Matching by setting the secret image in the center of the cover image. The evaluation of the experiment indicated that the secret image can be retrieved by a maximum of total 40% sequential cropping on the left, right, up, and bottom of the cover image. The secret image also can be retrieved if the total asymmetric cropping area is 25% that covered two sides (either left-right, left-up or right-up). In addition, the secret image can also be retrieved if the total asymmetric cropping area is 70% if the bottom part is included. If asymmetric cropping area included three sides, then the algorithm fails to retrieve the secret image. For cropping in the botom the secret image can be extracted up to 70%.

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Lu, Weirui, Xiaofen Xing, Bolun Cai, and Xiangmin Xu. "Listwise View Ranking for Image Cropping." IEEE Access 7 (2019): 91904–11. http://dx.doi.org/10.1109/access.2019.2925430.

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Khalid, Shamsul Kamal Ahmad, Mustafa Mat Deris, and Kamaruddin Malik Mohamad. "Anti-cropping digital image watermarking using Sudoku." International Journal of Grid and Utility Computing 4, no. 2/3 (2013): 169. http://dx.doi.org/10.1504/ijguc.2013.056253.

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Ciocca, Gianluigi, Claudio Cusano, Francesca Gasparini, and Raimondo Schettini. "Self-Adaptive Image Cropping for Small Displays." IEEE Transactions on Consumer Electronics 53, no. 4 (November 2007): 1622–27. http://dx.doi.org/10.1109/tce.2007.4429261.

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Chang, Chin Chen, I. Ta Lee, Tsung Ta Ke, and Wen Kai Tai. "An Object-Based Image Reducing Approach." Advanced Materials Research 1044-1045 (October 2014): 1049–52. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1049.

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Common methods for reducing image size include scaling and cropping. However, these two approaches have some quality problems for reduced images. In this paper, we propose an image reducing algorithm by separating the main objects and the background. First, we extract two feature maps, namely, an enhanced visual saliency map and an improved gradient map from an input image. After that, we integrate these two feature maps to an importance map. Finally, we generate the target image using the importance map. The proposed approach can obtain desired results for a wide range of images.

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Yang, Rong, Robert Wang, Yunkai Deng, Xiaoxue Jia, and Heng Zhang. "Rethinking the Random Cropping Data Augmentation Method Used in the Training of CNN-Based SAR Image Ship Detector." Remote Sensing 13, no. 1 (December 23, 2020): 34. http://dx.doi.org/10.3390/rs13010034.

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The random cropping data augmentation method is widely used to train convolutional neural network (CNN)-based target detectors to detect targets in optical images (e.g., COCO datasets). It can expand the scale of the dataset dozens of times while consuming only a small amount of calculations when training the neural network detector. In addition, random cropping can also greatly enhance the spatial robustness of the model, because it can make the same target appear in different positions of the sample image. Nowadays, random cropping and random flipping have become the standard configuration for those tasks with limited training data, which makes it natural to introduce them into the training of CNN-based synthetic aperture radar (SAR) image ship detectors. However, in this paper, we show that the introduction of traditional random cropping methods directly in the training of the CNN-based SAR image ship detector may generate a lot of noise in the gradient during back propagation, which hurts the detection performance. In order to eliminate the noise in the training gradient, a simple and effective training method based on feature map mask is proposed. Experiments prove that the proposed method can effectively eliminate the gradient noise introduced by random cropping and significantly improve the detection performance under a variety of evaluation indicators without increasing inference cost.

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DHARWADKAR, NAGARAJ V., and B. B. AMBERKER. "STEGANOGRAPHIC SCHEME FOR GRAY-LEVEL IMAGE USING PIXEL NEIGHBORHOOD AND LSB SUBSTITUTION." International Journal of Image and Graphics 10, no. 04 (October 2010): 589–607. http://dx.doi.org/10.1142/s0219467810003901.

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The exchange of secret message using images has vital importance in secret communication. Steganographic scheme is employed to achieve the task of secret message communication using images. The existing scheme based on pixel value differencing (PVD) with least significant bit (LSB) sequential substitution suffer from low embedding capacity. The embedding capacity is increased by using the edge regions of image obtained by neighborhood connectivity of pixel. We propose an adaptive steganographic scheme for gray-level images. Our scheme relies on the neighborhood connectivity of pixels to estimate the embedding capacity and resolves the problem of sequential substitution by jumbling the bits of secret message. The effect of cropping and filtration attacks on stegoimage is minimized by embedding the copies of secret message into four different regions of the cover image. The performance of the scheme is analyzed for various types of image processing attacks like cropping, blurring, filtering, adding noise, and sharpening. The proposed scheme is found rigid to these attacks.

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Rosnelly, Rika, Linda Wahyuni, and Jani Kusanti. "Optimization of Region of Interest (ROI) Image of Malaria Parasites." Journal of Applied Intelligent System 3, no. 2 (December 27, 2018): 87–95. http://dx.doi.org/10.33633/jais.v3i2.2060.

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The stage of region of interest (ROI) is the determining part to the next stage in image processing. ROI is a process of taking certain parts or regions in an image. ROI can be done by manual and automatic cropping. Some previous studies still use cropping manually for detection of malaria parasites. This study uses cropping automatically for detection of malaria parasites. The types of malaria parasites used were falciparum, vivax and malariae with ring stages, tropozoite, schizon and gametocytes. Data from malaria parasites were obtained at the North Sumatra Provincial Health Laboratory. The results show that the ROI image can crop the malaria parasite region. Keyword - malaria parasite, ROI.

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Mohanty, Manoranjan, Muhammad Rizwan Asghar, and Giovanni Russello. "$2DCrypt$ : Image Scaling and Cropping in Encrypted Domains." IEEE Transactions on Information Forensics and Security 11, no. 11 (November 2016): 2542–55. http://dx.doi.org/10.1109/tifs.2016.2585085.

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Zhou, Jingkai, Chi-Man Vong, Qiong Liu, and Zhenyu Wang. "Scale adaptive image cropping for UAV object detection." Neurocomputing 366 (November 2019): 305–13. http://dx.doi.org/10.1016/j.neucom.2019.07.073.

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Lu, Peng, Hao Zhang, XuJun Peng, and Xiang Peng. "Aesthetic guided deep regression network for image cropping." Signal Processing: Image Communication 77 (September 2019): 1–10. http://dx.doi.org/10.1016/j.image.2019.05.010.

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Xu, Yifei, Wujiang Xu, Mian Wang, Li Li, Genan Sang, Pingping Wei, and Li Zhu. "Saliency aware image cropping with latent region pair." Expert Systems with Applications 171 (June 2021): 114596. http://dx.doi.org/10.1016/j.eswa.2021.114596.

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Rufin, Philippe, David Frantz, Stefan Ernst, Andreas Rabe, Patrick Griffiths, Mutlu Özdoğan, and Patrick Hostert. "Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning." Remote Sensing 11, no. 3 (January 23, 2019): 232. http://dx.doi.org/10.3390/rs11030232.

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Spatially explicit information on cropland use intensity is vital for monitoring land and water resource demands in agricultural systems. Cropping practices underlie substantial spatial and temporal variability, which can be captured through the analysis of image time series. Temporal binning helps to overcome limitations concerning operability and repeatability for mapping large areas and can improve the thematic detail and consistency of maps in agricultural systems. We here assessed the use of annual, quarterly, and eight-day temporal features for mapping five cropping practices on annual croplands across Turkey. We used 2403 atmospherically corrected and topographically normalized Landsat Collection 1 L1TP images of 2015 to compute quarterly best-pixel composites, quarterly and annual spectral-temporal metrics, as well as gap-filled eight-day time series of Tasseled Cap components. We tested 22 feature sets for binary cropland mapping, and subsequent discrimination of five cropping practices: Spring and winter cropping, summer cropping, semi-aquatic cropping, double cropping, and greenhouse cultivation. We evaluated area-adjusted accuracies and compared cropland area estimates at the province-level with official statistics. We achieved overall accuracies above 90%, when using either all quarterly features or the eight-day Tasseled Cap time series, indicating that temporal binning of intra-annual image time-series into multiple temporal features improves representations of cropping practices. Class accuracies of winter and spring, summer, and double cropping were robust, while omission errors for semi-aquatic cropping and greenhouse cultivation were high. Our mapped cropland extent was in good agreement with province-level statistics (r2 = 0.85, RMSE = 7.2%). Our results indicate that 71.3% (±2.3%) of Turkey’s annual croplands were cultivated during winter and spring, 15.8% (±2.2%) during summer, while 8.5% (±1.6%) were double-cropped, 4% (±1.9%) were cultivated under semi-aquatic conditions, and 0.32% (±0.2%) was greenhouse cultivation. Our study presents an open and readily available framework for detailed cropland mapping over large areas, which bears the potential to inform assessments of land use intensity, as well as land and water resource demands.

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Bae, Joungeun, and Hoon Yoo. "Image Enhancement for Computational Integral Imaging Reconstruction via Four-Dimensional Image Structure." Sensors 20, no. 17 (August 25, 2020): 4795. http://dx.doi.org/10.3390/s20174795.

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This paper describes the image enhancement of a computational integral imaging reconstruction method via reconstructing a four-dimensional (4-D) image structure. A computational reconstruction method for high-resolution three-dimensional (3-D) images is highly required in 3-D applications such as 3-D visualization and 3-D object recognition. To improve the visual quality of reconstructed images, we introduce an adjustable parameter to produce a group of 3-D images from a single elemental image array. The adjustable parameter controls overlapping in back projection with a transformation of cropping and translating elemental images. It turns out that the new parameter is an independent parameter from the reconstruction position to reconstruct a 4-D image structure with four axes of x, y, z, and k. The 4-D image structure of the proposed method provides more visual information than existing methods. Computer simulations and optical experiments are carried out to show the feasibility of the proposed method. The results indicate that our method enhances the image quality of 3-D images by providing a 4-D image structure with the adjustable parameter.

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Xie, Jian Bin, Tong Liu, Zhong Zhu Huang, Pei Qin Li, and Wei Yan. "A Robust and Rapid Image Preprocessing Method for Finger Vein." Advanced Materials Research 605-607 (December 2012): 2126–30. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2126.

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In order to achieve robust and rapid image preprocessing for finger vein, this paper presents a robust and rapid image preprocessing method. With this method, locating & cropping, normalization, equalization and filtering are executed step by step. Then, quality assessment is done to exclude the images of low quality. The results of simulate experiments show that this method is effective and it is able to ensure reliable feature extraction.

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Eraso, Francisco Eduardo, William C. Scarfe, Yoshihiko Hayakawa, Jane Goldsmith, and Allan G. Farman. "Teledentistry: protocols for the transmission of digitized radiographs of the temporomandibular joint." Journal of Telemedicine and Telecare 2, no. 4 (December 1, 1996): 217–23. http://dx.doi.org/10.1258/1357633961930103.

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Tomograms of the temporomandibular joint were digitized in three different formats using a PC-based system. The image resolution for various projections was determined at different camera-film distances. Three series of images were transmitted by telephone, and transmission times were measured. The original radiographs, the digitized images, the transmitted images and the transmitted-and-printed images were presented to 10 observers, who were asked to rate image quality. No difference in image quality was found between the initial digitized and the transmitted images. However, transmitted and transmitted-and-printed images were of significantly lower quality than the original radiographs or the digitized images viewed on a computer monitor. Transmission time was reduced significantly 50 by cropping the images before transmission. The image quality of individual radiographs was better than radiographs formatted as a series.

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IWAMURA, Kiyohiko, Jun Younes LOUHI KASAHARA, Alessandro MORO, Atsushi YAMASHITA, and Hajime ASAMA. "Image Captioning with Data Augmentation Using Cropping and Mask Based on Attention Image." Journal of the Japan Society for Precision Engineering 86, no. 11 (November 5, 2020): 904–10. http://dx.doi.org/10.2493/jjspe.86.904.

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Mohammadi Ziabari, Seyed Sahand. "Enhancement of Genetic Image Watermarking Robust Against Cropping Attack." International Journal in Foundations of Computer Science & Technology 4, no. 2 (March 31, 2014): 21–26. http://dx.doi.org/10.5121/ijfcst.2014.4203.

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Deng, Pingke, Ming Diao, Mingguang Shan, Zhi Zhong, and Yabin Zhang. "Multiple-image encryption using spectral cropping and spatial multiplexing." Optics Communications 359 (January 2016): 234–39. http://dx.doi.org/10.1016/j.optcom.2015.09.056.

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Liang, Yun, Chuntao Wang, Dong Wang, Xiaonan Luo, and Zhuo Su. "Optimised image retargeting using aesthetic-based cropping and scaling." IET Image Processing 7, no. 1 (February 1, 2013): 61–69. http://dx.doi.org/10.1049/iet-ipr.2012.0308.

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Yan, Jianzhou, Stephen Lin, Sing Bing Kang, and Xiaoou Tang. "Change-Based Image Cropping with Exclusion and Compositional Features." International Journal of Computer Vision 114, no. 1 (February 3, 2015): 74–87. http://dx.doi.org/10.1007/s11263-015-0801-5.

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Tu, Yi, Li Niu, Weijie Zhao, Dawei Cheng, and Liqing Zhang. "Image Cropping with Composition and Saliency Aware Aesthetic Score Map." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12104–11. http://dx.doi.org/10.1609/aaai.v34i07.6889.

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Aesthetic image cropping is a practical but challenging task which aims at finding the best crops with the highest aesthetic quality in an image. Recently, many deep learning methods have been proposed to address this problem, but they did not reveal the intrinsic mechanism of aesthetic evaluation. In this paper, we propose an interpretable image cropping model to unveil the mystery. For each image, we use a fully convolutional network to produce an aesthetic score map, which is shared among all candidate crops during crop-level aesthetic evaluation. Then, we require the aesthetic score map to be both composition-aware and saliency-aware. In particular, the same region is assigned with different aesthetic scores based on its relative positions in different crops. Moreover, a visually salient region is supposed to have more sensitive aesthetic scores so that our network can learn to place salient objects at more proper positions. Such an aesthetic score map can be used to localize aesthetically important regions in an image, which sheds light on the composition rules learned by our model. We show the competitive performance of our model in the image cropping task on several benchmark datasets, and also demonstrate its generality in real-world applications.

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Shi, Jiyuan, Ji Dang, Mida Cui, Rongzhi Zuo, Kazuhiro Shimizu, Akira Tsunoda, and Yasuhiro Suzuki. "Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet." Applied Sciences 11, no. 2 (January 7, 2021): 518. http://dx.doi.org/10.3390/app11020518.

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In this research, 200 corrosion images of steel and 500 crack images of rubber bearing are collected and manually labeled to build the data set. Then the two data sets are respectively adopted to train VGG-Unet models in two methods, aiming to conduct Damage Segmentation by inputting different size of data set. One method is Squashing Segmentation to input squashed images from high resolution directly into VGG-Unet model while Cropping Segmentation uses cropped image with size 224 × 224 as input images. Because the proportion of damage pixels in the data set is different, the results produced by the two data sets are quite different. For large size damage (such as corrosion) segmentation, Cropping Segmentation has a better result while for minor damage (such as crack) segmentation, the result is opposite. The main reason is the gap in the concentration of valid data from the data set. To improve the capability of crack segmentation based on Cropping Segmentation, Background Data Drop Rate (BDDR) is adopted to reduce the quantity of background images to control the proportion of damage pixels from the data set in pixel-level. The ratio of damage pixels from the data set can be decided by different value of BDDR. By testing, the accuracy of Cropping Segmentation becomes relatively higher under BDDR being 0.8.

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Kekre, H. B., Tanuja Sarode, and Shachi Natu. "Effect of Weight Factor on The Performance of Hybrid Column Wavelet Transform used for Watermarking under Various Attacks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 10 (April 21, 2014): 3997–4013. http://dx.doi.org/10.24297/ijct.v12i10.2993.

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Digital image watermarking is aimed at copyright protection of digital images. Strength of embedded watermark plays an important role in robustness and invisibility of watermarking technique. In this paper, effect of two parameters namely, watermark strength and middle frequency coefficients of host image used for embedding watermark is studied. In the given watermarking technique, watermark is normalized before embedding. This reduces the strength of watermark so that there will be minimum possible distortion in watermarked image. However, it has been observed in our work proposed in previous paper that, such embedment responds poorly to various image processing attacks like compression, cropping, resizing, noise addition etc. Hence in this paper, an attempt has been made to increase the strength of embedded watermark by using suitable weight factor so that robustness of watermarking technique proposed in our previous paper is further increased with small acceptable decrease in imperceptibility. Also middle frequency elements of host image selected for embedding watermark are varied by selecting different rows of host such that slowly we move from middle frequency components towards high frequency components. For certain attacks like image cropping, selection of middle frequency coefficients affects the robustness achieved. Increase in weight factor significantly improves the performance of given watermarking technique by more than 50% as proposed in our previous paper where weight factor value was 25.

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Pasqualin, Côme, François Gannier, Claire O. Malécot, Pierre Bredeloux, and Véronique Maupoil. "Automatic quantitative analysis of t-tubule organization in cardiac myocytes using ImageJ." American Journal of Physiology-Cell Physiology 308, no. 3 (February 1, 2015): C237—C245. http://dx.doi.org/10.1152/ajpcell.00259.2014.

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The transverse tubule system in mammalian striated muscle is highly organized and contributes to optimal and homogeneous contraction. Diverse pathologies such as heart failure and atrial fibrillation include disorganization of t-tubules and contractile dysfunction. Few tools are available for the quantification of the organization of the t-tubule system. We developed a plugin for the ImageJ/Fiji image analysis platform developed by the National Institutes of Health. This plugin (TTorg) analyzes raw confocal microscopy images. Analysis options include the whole image, specific regions of the image (cropping), and z-axis analysis of the same image. Batch analysis of a series of images with identical criteria is also one of the options. There is no need to either reorientate any specimen to the horizontal or to do a thresholding of the image to perform analysis. TTorg includes a synthetic “myocyte-like” image generator to test the plugin's efficiency in the user's own experimental conditions. This plugin was validated on synthetic images for different simulated cell characteristics and acquisition parameters. TTorg was able to detect significant differences between the organization of the t-tubule systems in experimental data of mouse ventricular myocytes isolated from wild-type and dystrophin-deficient mice. TTorg is freely distributed, and its source code is available. It provides a reliable, easy-to-use, automatic, and unbiased measurement of t-tubule organization in a wide variety of experimental conditions.

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Soobhany, Ahmad Ryad, Richard Leary, and KP Lam. "On the Performance of Li’s Unsupervised Image Classifier and the Optimal Cropping Position of Images for Forensic Investigations." International Journal of Digital Crime and Forensics 3, no. 1 (January 2011): 1–13. http://dx.doi.org/10.4018/jdcf.2011010101.

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Images from digital imaging devices are prevalent in society. The signatures of these images can be extracted as sensor pattern noise (SPN) and classified according to their source devices. In this paper, the authors assess the reliability of an unsupervised classifier for forensic investigation of digital images recovered from storage devices and to identify the best position for cropping the images before processing. Cross validation was performed on the classifier to assess the error rate and determine the effect of the size of the sample space and the classifier trainer on the performance of the classifier. Moreover, the authors find that the effect of saturation and subsequently the contamination of the SPN in the images affected performance negatively. To alleviate the negative performance, the authors identify the areas of images where less contamination occurs to perform cropping.

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Hong, Suk-Ju, Il Nam, Sang-Yeon Kim, Eungchan Kim, Chang-Hyup Lee, Sebeom Ahn, Il-Kwon Park, and Ghiseok Kim. "Automatic Pest Counting from Pheromone Trap Images Using Deep Learning Object Detectors for Matsucoccus thunbergianae Monitoring." Insects 12, no. 4 (April 12, 2021): 342. http://dx.doi.org/10.3390/insects12040342.

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The black pine bast scale, M. thunbergianae, is a major insect pest of black pine and causes serious environmental and economic losses in forests. Therefore, it is essential to monitor the occurrence and population of M. thunbergianae, and a monitoring method using a pheromone trap is commonly employed. Because the counting of insects performed by humans in these pheromone traps is labor intensive and time consuming, this study proposes automated deep learning counting algorithms using pheromone trap images. The pheromone traps collected in the field were photographed in the laboratory, and the images were used for training, validation, and testing of the detection models. In addition, the image cropping method was applied for the successful detection of small objects in the image, considering the small size of M. thunbergianae in trap images. The detection and counting performance were evaluated and compared for a total of 16 models under eight model conditions and two cropping conditions, and a counting accuracy of 95% or more was shown in most models. This result shows that the artificial intelligence-based pest counting method proposed in this study is suitable for constant and accurate monitoring of insect pests.

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Lee, Jung-San, and Bo Li. "Self-Recognized Image Protection Technique that Resists Large-Scale Cropping." IEEE MultiMedia 21, no. 1 (2014): 60–73. http://dx.doi.org/10.1109/mmul.2014.14.

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Song, Ruoning, Long Zhang, Chuang Zhu, Jun Liu, Jie Yang, and Tong Zhang. "Thyroid Nodule Ultrasound Image Classification Through Hybrid Feature Cropping Network." IEEE Access 8 (2020): 64064–74. http://dx.doi.org/10.1109/access.2020.2982767.

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Li, Debang, Huikai Wu, Junge Zhang, and Kaiqi Huang. "Fast A3RL: Aesthetics-Aware Adversarial Reinforcement Learning for Image Cropping." IEEE Transactions on Image Processing 28, no. 10 (October 2019): 5105–20. http://dx.doi.org/10.1109/tip.2019.2914360.

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Zong, Tian-Rui, Yong Xiang, Suzan Elbadry, and Saeid Nahavandi. "Modified moment-based image watermarking method robust to cropping attack." International Journal of Automation and Computing 13, no. 3 (June 2016): 259–67. http://dx.doi.org/10.1007/s11633-015-0926-6.

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Christensen, Casper L., and Aneesh Vartakavi. "An Experience-Based Direct Generation Approach to Automatic Image Cropping." IEEE Access 9 (2021): 107600–107610. http://dx.doi.org/10.1109/access.2021.3100816.

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Wu, Jun Feng, Xian Qiang Lv, Wen Lian Yang, Ye Tao, Jing Zhang, and Song Yang. "Image Retrieval Based on Color Histogram of Saliency Map." Advanced Materials Research 989-994 (July 2014): 3552–55. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3552.

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With the development of the internet, more and more images appear in the internet. How to effectively retrieve the desired image is still an important problem. In the past, traditional color histogram is used image retrieval system, but color histograms lack spatial information and are sensitive to intensity variation, color distortion and cropping. As a result, images with similar histograms may have totally different semantics. So the spatial information should be included in color histogram. The color histogram based on saliency map approach is introduced to overcome the above limitations. In this paper, we present a robust image retrieval based on color histogram of saliency map. Firstly, in order to extract useful spatial information of each pixel, the steady saliency map of the images is extracted. Then, color histogram based on saliency map is introduced, and the similarity between color images is computed by using the color histogram of saliency map. Experimental results show that the proposed color image retrieval is more accurate and efficient in retrieving the user-interested images.

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Yuhandri, Yuhandri. "PERBANDINGAN METODE CROPPING PADA SEBUAH CITRA UNTUK PENGAMBILAN MOTIF TERTENTU PADA KAIN SONGKET SUMATERA BARAT." KOMTEKINFO 6, no. 1 (June 28, 2019): 95–105. http://dx.doi.org/10.35134/komtekinfo.v6i1.273.

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Pada saat proses pengolahan citra dimana kita hanya membutuhkan bagian tertentu saja dari sebuah citra sesuai kebutuhan yang disebut dengan Region of Interest (ROI), guna mendapatkan itu maka dalam pemrosesan dilakukan sebuah proses cropping. Cropping banyak dilakukan oleh para peneliti terutama yang meneliti pada bidang image processing guna untuk melakukan pengolahan data pada sebuah citra, hasil proses cropping pada sebuah citra biasanya dilakukan untuk memudahkan peneliti fokus pada sesuatu obyek yang diperlukan saja. Pada penelitian ini adalah melakukan perbandingan metode cropping yang sudah ada untuk mendapatkan suatu motif yang terdapat pada sebuah citra kain songket Sumatera Barat. Pada penelitian ini menggunakan metode cropping rectangle, square, circle, ellipse, polygon dan diuji dengan menggunakan bahasa pemrograman Matlab. Hasil perbandingan 5 metode cropping untuk pengambilan motif tertentu pada citra songket dengan 5 sampel citra songket yang berbeda, menunjukan bahwa hasil terbaik diperoleh dengan meggunakan metode polygon. Metode polygon dapat menjangkau titik koordinat tertentu pada sebuah citra songket, sehingga hasil cropping lebih baik dan motif lain yang ikut terbawa pada saat proses cropping dapat dikurangi.

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Fakhri Ab. Nasir, Ahmad, M. Nordin A Rahman, Nashriyah Mat, A. Rasid Mamat, and Ahmad Shahrizan Abdul Ghani. "Image Pre-Processing Algorithm for Ficus deltoidea Jack (Moraceae) Varietal Recognition: A Repeated Perpendicular Line Scanning Approach." International Journal of Engineering & Technology 7, no. 2.15 (April 6, 2018): 49. http://dx.doi.org/10.14419/ijet.v7i2.15.11211.

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Image pre-processing task is always the first crucial step in plant species recognition system which is responsible to keep precision of feature measurement process. Some of researchers have developed the image pre-processing algorithm to remove petiole section. However, the algorithm was developed using semi-automatic algorithm which is strongly believed to give an inaccurate feature measurement. In this paper, a new technique of automatic petiole section removal is proposed based on repeated perpendicular petiole length scanning concept. Four phases of petiole removal technique involved are: i) binary image enhancement, ii) boundary binary image contour tracing, iii) petiole section scanning, and iv) optimal image size retaining and cropping. The experiments are conducted using six varieties of Ficus deltoidea Jack (Moraceae) leaves. The experimental results indicate that the segmentation results are acceptably good since the digital leaf images have less than 1% of segmentation errors on several ground truth images.

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Ganschow, L., T. Thiele, N. Deckers, and R. Reulke. "CLASSIFICATION OF TREE SPECIES ON THE BASIS OF TREE BARK TEXTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1855–59. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1855-2019.

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<p><strong>Abstract.</strong> Forest inventory is an important topic in forestry and a digital solution which works on the basis of tree images is looked for. Implementing a system which automatically classifies tree species is the overall goal. In this paper the implementation of a convolutional neural net for solving this classification problem is executed and evaluated. The objective is creating a system which works well on unseen data and deriving guidelines and constraints to guarantee good accuracy results. Images including tree segmentation and the corresponding labels are provided as training data. The tree species classification takes the segmentation results of a stereo vision based image segmentation algorithm as input. The basic idea consists of cropping the tree images into quadratic boxes before feeding them into the neural net. First, each box is classified separately and then the results are evaluated to get a classification for the whole tree. Methods for result improvement include altering box size, using overlapping boxes, artificially enlarging the training set, pretraining and finetuning. Cropping a tree image into boxes of a specific size and accumulating the single results to get a classification of the whole tree leads to an accuracy of 96.7% provided that specific constraints like minimum box number and the projected size of the tree on image plane are considered. Finally, ways to further improve performance are pointed out.</p>

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KC, Kushal, Kaiguang Zhao, Matthew Romanko, and Sami Khanal. "Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing." Remote Sensing 13, no. 14 (July 8, 2021): 2689. http://dx.doi.org/10.3390/rs13142689.

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Cover cropping is a conservation practice that helps to alleviate soil health problems and reduce nutrient losses. Understanding the spatial variability in historic and current adoption of cover cropping practices and their impacts on soil, water, and nutrient dynamics at a landscape scale is an important step in determining and prioritizing areas in a watershed to effectively utilize this practice. However, such data are lacking. Our objective was to develop a spatial and temporal inventory of winter cover cropping practices in the Maumee River watershed using images collected by Landsat satellites (Landsat 5, 7 and 8) from 2008 to 2019 in Google Earth Engine (GEE) platform. Each year, satellite images collected during cover crop growing season (i.e., between October and April) were converted into two seasonal composites based on cover crop phenology. Using these composites, various image-based covariates were extracted for 628 ground-truth (field) data. By integrating ground-truth and image-based covariates, a cover crop classification model based on a random forest (RF) algorithm was developed, trained and validated in GEE platform. Our classification scheme differentiated four cover crop categories: Winter Hardy, Winter Kill, Spring Emergent, and No Cover. The overall classification accuracy was 75%, with a kappa coefficient of 0.63. The results showed that more than 50% of the corn-soybean areas in the Maumee River watershed were without winter crops during 2008–2019 period. It was found that 2019/2020 and 2009/2010 were the years with the largest and lowest cover crop areas, with 34% and 10% in the watershed, respectively. The total cover cropping area was then assessed in relation to fall precipitation and cumulative growing degree days (GDD). There was no apparent increasing trend in cover crop areas between 2008 and 2019, but the variability in cover crops areas was found to be related to higher accumulated GDD and fall precipitation. A detailed understanding of the spatial and temporal distribution of cover crops using GEE could help in promoting site-specific management practices to enhance their environmental benefits. This also has significance to policy makers and funding agencies as they could use the information to localize areas in need of interventions for supporting adoption of cover cropping practice.

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Abd, Dana Faiq. "Face Recognition Use Local Image Dataset and Correlation Technique." UHD Journal of Science and Technology 5, no. 2 (August 5, 2021): 26–31. http://dx.doi.org/10.21928/uhdjst.v5n2y2021.pp26-31.

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Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.

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Voruganti, Santhosh. "Digital Image Watermarking using Chaotic Encryption and Arnold Transform." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1825–35. http://dx.doi.org/10.22214/ijraset.2021.35417.

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Internet has caused an extraordinary increase in the transfer and sharing of digital data like text, videos, images, audio, etc. over it. However, with the advent of modern access technology, multimedia data is more prone to security risks as data can be modified or redistributed without prior permission. Chaotic encryption-based blind digital image watermarking technique applicable to both grayscale and colour images. Discrete cosine transform (DCT) is used before embedding the watermark in the host image. Arnold transform is used in addition to chaotic encryption to add double-layer security to the watermark. Three different variants of the proposed algorithm have been tested and analysed. The simulation results show that the proposed scheme is robust to most of the image processing operations like joint picture expert group compression, sharpening, cropping, and median filtering. To validate the efficiency of the proposed technique, the simulation results are compared with certain state-of-art techniques.

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GAO, TIEGANG, and QIAOLUN GU. "REVERSIBLE WATERMARKING ALGORITHM BASED ON WAVELET LIFTING SCHEME." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 04 (July 2008): 643–52. http://dx.doi.org/10.1142/s0219691308002550.

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Nowadays, digital watermarking algorithms are widely applied to ownership protection and tampering detection of digital images. In this paper, a new reversible watermarking algorithm based on wavelet lifting scheme is proposed. In the algorithm, the image is firstly divided into some no-overlapping blocks, and then the wavelet lifting scheme where on every block, is performed watermarking data is embedded into the image according to the attribute of the subband of every block. In order to guarantee the security of algorithm, chaotic system is used to shuffle the position of blocks. The interesting point and usefulness of the algorithm lies in the fact that the watermarked image can be exactly restored into the original image, and the watermarking is robust to cropping. The experimental results show the effectiveness of this scheme.

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Wang, Jun, Qianying Liu, Haotian Xie, Zhaogang Yang, and Hefeng Zhou. "Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks." Cancers 13, no. 4 (February 7, 2021): 661. http://dx.doi.org/10.3390/cancers13040661.

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(1) Purpose: To improve the capability of EfficientNet, including developing a cropping method called Random Center Cropping (RCC) to retain the original image resolution and significant features on the images’ center area, reducing the downsampling scale of EfficientNet to facilitate the small resolution images of RPCam datasets, and integrating attention and Feature Fusion (FF) mechanisms with EfficientNet to obtain features containing rich semantic information. (2) Methods: We adopt the Convolutional Neural Network (CNN) to detect and classify lymph node metastasis in breast cancer. (3) Results: Experiments illustrate that our methods significantly boost performance of basic CNN architectures, where the best-performed method achieves an accuracy of 97.96% ± 0.03% and an Area Under the Curve (AUC) of 99.68% ± 0.01% on RPCam datasets, respectively. (4) Conclusions: (1) To our limited knowledge, we are the only study to explore the power of EfficientNet on Metastatic Breast Cancer (MBC) classification, and elaborate experiments are conducted to compare the performance of EfficientNet with other state-of-the-art CNN models. It might provide inspiration for researchers who are interested in image-based diagnosis using Deep Learning (DL). (2) We design a novel data augmentation method named RCC to promote the data enrichment of small resolution datasets. (3) All of our four technological improvements boost the performance of the original EfficientNet.

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Takahashi, Ryo, Takashi Matsubara, and Kuniaki Uehara. "Data Augmentation Using Random Image Cropping and Patching for Deep CNNs." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 9 (September 2020): 2917–31. http://dx.doi.org/10.1109/tcsvt.2019.2935128.

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Fanfani, Marco, Massimo Iuliani, Fabio Bellavia, Carlo Colombo, and Alessandro Piva. "A vision-based fully automated approach to robust image cropping detection." Signal Processing: Image Communication 80 (February 2020): 115629. http://dx.doi.org/10.1016/j.image.2019.115629.

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Fylakis, Angelos, Anja Keskinarkaus, Juha Partala, Simo Saarakkala, and Tapio Seppänen. "Reversible Data Hiding in FTIR Microspectroscopy Images with Tamper Indication and Payload Error Correction." BioMed Research International 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/7584852.

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Fourier transform infrared (FTIR) microspectroscopy images contain information from the whole infrared spectrum used for microspectroscopic analyses. In combination with the FTIR image, visible light images are used to depict the area from which the FTIR spectral image was sampled. These two images are traditionally acquired as separate files. This paper proposes a histogram shifting-based data hiding technique to embed visible light images in FTIR spectral images producing single entities. The primary objective is to improve data management efficiency. Secondary objectives are confidentiality, availability, and reliability. Since the integrity of biomedical data is vital, the proposed method applies reversible data hiding. After extraction of the embedded data, the FTIR image is reversed to its original state. Furthermore, the proposed method applies authentication tags generated with keyed Hash-Based Message Authentication Codes (HMAC) to detect tampered or corrupted areas of FTIR images. The experimental results show that the FTIR spectral images carrying the payload maintain good perceptual fidelity and the payload can be reliably recovered even after bit flipping or cropping attacks. It has been also shown that extraction successfully removes all modifications caused by the payload. Finally, authentication tags successfully indicated tampered FTIR image areas.

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Liu, Jianhong, Wenquan Zhu, Clement Atzberger, Anzhou Zhao, Yaozhong Pan, and Xin Huang. "A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data." Remote Sensing 10, no. 8 (July 31, 2018): 1203. http://dx.doi.org/10.3390/rs10081203.

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Agricultural land use and cropping patterns are closely related to food production, soil degradation, water resource management, greenhouse gas emission, and regional climate alterations. Methods for reliable and cost-efficient mapping of cropping pattern, as well as their changes over space and time, are therefore urgently needed. To cope with this need, we developed a phenology-based method to map cropping patterns based on time-series of vegetation index data. The proposed method builds on the well-known ‘threshold model’ to retrieve phenological metrics. Values of four phenological parameters are used to identify crop seasons. Using a set of rules, the crop season information is translated into cropping pattern. To illustrate the method, cropping patterns were determined for three consecutive years (2008–2010) in the Henan province of China, where reliable validation data was available. Cropping patterns were derived using eight-day composite MODIS Enhanced Vegetation Index (EVI) data. Results show that the proposed method can achieve a satisfactory overall accuracy (~84%) in extracting cropping patterns. Interestingly, the accuracy obtained with our method based on MODIS EVI data was comparable with that from Landsat-5 TM image classification. We conclude that the proposed method for cropland and cropping pattern identification based on MODIS data offers a simple, yet reliable way to derive important land use information over large areas.

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Pan, Ruru, Weidong Gao, Wei Li, and Bugao Xu. "Image analysis for seam-puckering evaluation." Textile Research Journal 87, no. 20 (October 14, 2016): 2513–23. http://dx.doi.org/10.1177/0040517516673330.

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Seam puckering is often considered an undesirable wrinkling appearance along a seamline, and is a problem that concerns fabric, sewing machine and sewing thread manufacturers. Until now, the standardized evaluation method for seam-puckering grading is still a visual-based, subjective method. This research project was aimed at developing a computer-vision system for automatic seam-puckering evaluation to improve the consistency and efficiency of grading. Fabric seam images were captured by a customized image acquisition system, and the seam images and the optimal image parameters, such as length and width, were determined according to the results of human inspection. The seamline was located with edge detection and Hough transform techniques. After rotating and cropping the image, the projection profile was then obtained and smoothed with the locally scatter-plot smoothing (LOESS) algorithm. Five characteristic features were extracted from the smoothed profile. Finally, an artificial neural network classifier was created to realize the automatic assessment of the seam-puckering grade. The experimental results proved that the proposed system can achieve accurate seam-puckering grades, and has the potential to replace the current manual evaluation.

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Winarno, Edy, Imam Husni Al Amin, and Wiwien Hadikurniawati. "Asymmetrical Half-join Method on Dual Vision Face Recognition." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (December 1, 2017): 3411. http://dx.doi.org/10.11591/ijece.v7i6.pp3411-3420.

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This research proposes a model of face recognition using the method of joining two face images from left and right lens from a stereo vision camera namely half-join method. Half-join method is used in face image normalization processing. The proposed half-join method is a face images joining model, which is called asymmetrical half-join. In asymmetrical half-join method, a RoI (region of interest) of face image from left and right lenses are provided based on axis center of each eye in eye detection. The cropping of face image from asymmetrical half-join model has different width depends on eyes coordinate location. The proposed system shows that the asymmetrical half-join method can produce a better of face recognition rate. The experimental results show that the asymmetrical half-join method has a better recognition rate and computation time than single vision method and symmetrical half-join method.

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Journal articles on the topic 'Image cropping'

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