Artykuły w czasopismach: „Image analysis”

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Vijayalakshmi, A., i V. Girish. "Affordable image analysis using NIH Image/ImageJ". Indian Journal of Cancer 41, nr 1 (2004): 47. http://dx.doi.org/10.4103/0019-509x.12345.

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Legland, David, i Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab". F1000Research 10 (30.04.2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

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Schneider, Caroline A., Wayne S. Rasband i Kevin W. Eliceiri. "NIH Image to ImageJ: 25 years of image analysis". Nature Methods 9, nr 7 (28.06.2012): 671–75. http://dx.doi.org/10.1038/nmeth.2089.

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Deserno, T. M., H. P. Meinzer, T. Tolxdorff i H. Handels. "Image Analysis and Modeling in Medical Image Computing". Methods of Information in Medicine 51, nr 05 (2012): 395–97. http://dx.doi.org/10.1055/s-0038-1627047.

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Summary Background: Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. Objectives: In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Methods: Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Results: Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. Conclusions: The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.

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Friel, J. J., i E. B. Prestridge. "Image Analysis—Turning Images Into Data". Microscopy and Microanalysis 4, S2 (lipiec 1998): 58–59. http://dx.doi.org/10.1017/s1431927600020419.

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Image analysis is the process of quantifying some aspect of an image—its particle size distribution, for example. Manual methods were in use long before computers made image analysis much faster and more reproducible. Linear measurements of diameter, point counting to measure volume fraction, and intercept counting to determine grain size have been used for over 100 years. Automatic image analysis (AIA), however, can make more measurements, and even calculate derived measurements, such as aspect ratio or circularity. AIA of a specimen or micrograph, of course, is only as good as the contrast mechanism used, so the imaging signal must be chosen carefully to reveal to the computer what is to be measured. Obtaining sufficient contrast is often the limiting task.Once an imaging signal is chosen and the digital resolution set, the computer can analyze the image. The feature descriptors can be generic:

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Gurevich, Igor, i Vera Yashina. "Descriptive Image Analysis. Foundations and Descriptive Image Algebras". International Journal of Pattern Recognition and Artificial Intelligence 33, nr 11 (październik 2019): 1940018. http://dx.doi.org/10.1142/s0218001419400184.

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The paper is devoted to Descriptive Image Analysis (DA) — a leading line of the modern mathematical theory of image analysis. DA is a logically organized set of descriptive methods, mathematical objects, and models and representations aimed at analyzing and evaluating the information represented in the form of images, as well as for automating the extraction from images of knowledge and data needed for intelligent decision-making. The basic idea of DA consists of embedding all processes of analysis (processing, recognition, understanding) of images into an image formalization space and reducing it to (1) construction of models/representations/formalized descriptions of images; (2) construction of models/representations/formalized descriptions of transformations over models and representations of images. We briefly discuss the basic ideas, methodological principles, mathematical methods, objects, and components of DA and the basic results determining the current state of the art in the field. Image algebras (IA) are considered in the context of a unified language for describing mathematical objects and operations used in image analysis (the standard IA by Ritter and the descriptive IA by Gurevich).

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Lim, P. C., T. Kim, S. I. Na, K. D. Lee, H. Y. Ahn i J. Hong. "ANALYSIS OF UAV IMAGE QUALITY USING EDGE ANALYSIS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (19.09.2018): 359–64. http://dx.doi.org/10.5194/isprs-archives-xlii-4-359-2018.

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<p><strong>Abstract.</strong> UAVs (Unmanned aerial Vehicles) can acquire images easily without large cost. For this reason, use of UAV is spreading to diverse fields such as orthoimages and DEM/DSM production. The spatial resolution of images is usually expressed as a GSD (Ground Sampling Distance). The GSD from UAV has higher performance than other platforms such as satellites and aircraft because it shoot at low altitude. However, blurring and noise may occur on UAV images due to the weather and the stability of UAV. However, since the GSD from UAV cannot sufficiently meet the spatial resolving power of the actual image system, a criterion for determining the spatial resolution of image is needed. Therefore we emphasize that the quality of the image needs to be analysed. Actual performance indicators such as GRD (Ground Resolved Distance) and NIIRS (National Image Interpretability Rating Scales), which can be measured through image analysis, are representative examples of image quality interpretation. It is possible to extract NIIRS form image quality related parameters such as MTF (Modulation Transfer Function), RER (Relative Edge Response) and SNR (Signal to Noise Ratio). In this paper, we aim to apply the Edge analysis method to UAV and to analyse the result. The analysis result showed that while GSD and NIIRS were highly dependent to imaging altitude, GRD and image sharpness showed optimal altitude ranges. The exact optimal range varied between images taken at different weather conditions. While we need a further study, this may indicate that edge analysis may provide an optimal operational altitude range suitable for the sensors.</p>

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Rakhimov, Bakhtiyar Saidovich, Feroza Bakhtiyarovna Rakhimova, Sabokhat Kabulovna Sobirova, Furkat Odilbekovich Kuryazov i Dilnoza Boltabaevna Abdirimova. "Review And Analysis Of Computer Vision Algorithms". American Journal of Applied sciences 03, nr 05 (31.05.2021): 245–50. http://dx.doi.org/10.37547/tajas/volume03issue05-39.

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Computer vision as a scientific discipline refers to the theories and technologies for creating artificial systems that receive information from an image. Despite the fact that this discipline is quite young, its results have penetrated almost all areas of life. Computer vision is closely related to other practical fields like image processing, the input of which is two-dimensional images obtained from a camera or artificially created. This form of image transformation is aimed at noise suppression, filtering, color correction and image analysis, which allows you to directly obtain specific information from the processed image. This information may include searching for objects, keypoints, segments, and annexes;

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Sedlak, René, Andreas Welscher, Patrick Hannawald, Sabine Wüst, Rainer Lienhart i Michael Bittner. "Analysis of 2D airglow imager data with respect to dynamics using machine learning". Atmospheric Measurement Techniques 16, nr 12 (26.06.2023): 3141–53. http://dx.doi.org/10.5194/amt-16-3141-2023.

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Abstract. We demonstrate how machine learning can be easily applied to support the analysis of large quantities of excited hydroxyl (OH*) airglow imager data. We use a TCN (temporal convolutional network) classification algorithm to automatically pre-sort images into the three categories “dynamic” (images where small-scale motions like turbulence are likely to be found), “calm” (clear-sky images with weak airglow variations) and “cloudy” (cloudy images where no airglow analyses can be performed). The proposed approach is demonstrated using image data of FAIM 3 (Fast Airglow IMager), acquired at Oberpfaffenhofen, Germany, between 11 June 2019 and 25 February 2020, achieving a mean average precision of 0.82 in image classification. The attached video sequence demonstrates the classification abilities of the learned TCN. Within the dynamic category, we find a subset of 13 episodes of image series showing turbulence. As FAIM 3 exhibits a high spatial (23 m per pixel) and temporal (2.8 s per image) resolution, turbulence parameters can be derived to estimate the energy diffusion rate. Similarly to the results the authors found for another FAIM station (Sedlak et al., 2021), the values of the energy dissipation rate range from 0.03 to 3.18 W kg−1.

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Khan, Yaser Daanial, M. Khalid Mahmood, Daud Ahmad i Nasser M. Al-Zidi. "Content-Based Image Retrieval Using Gamma Distribution and Mixture Model". Journal of Function Spaces 2022 (5.05.2022): 1–10. http://dx.doi.org/10.1155/2022/8674038.

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Since the last decade, the complexity of multimedia data, specifically images, is emerging exponentially as millions of images are uploaded by users on daily basis. Searching for a relevant image from such a substantial amount of data is very hectic and resource-demanding. To cope with this issue, researchers are working on content-based image retrieval (CBIR) approaches. This article proposes an efficient and novel probabilistic technique as a solution for content-based image retrieval. The patterns formed by the glyph structure of an image are excavated to yield content representations. These representations are accumulatively used to form a distribution, whereas the characteristics of this distribution represent the semantic structure of the image. In the end, the mixture model for gamma distribution is applied and parameters are refined through maximum likelihood. Furthermore, a mechanism is devised to retrieve matching images having comparable distribution patterns. Experiments show not only that the proposed technique yields a comparable precision to other competitive techniques but it also demonstrates that it is sufficiently efficient with high performance compared as compared to the others and requires unsupervised training.

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Saxena, Khushboo, i Yogesh Kumar Gupta. "Analysis of Image Processing Strategies Dedicated to Underwater Scenarios". International Journal on Recent and Innovation Trends in Computing and Communication 11, nr 3s (18.03.2023): 253–58. http://dx.doi.org/10.17762/ijritcc.v11i3s.6232.

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Underwater images undergo quality degradation issues of an image, like blur image, poor contrast, non-uniform illumination etc. Therefore, to process these degraded images, image processing come into existence. In this paper, two important image processing methods namely Image restoration and Image enhancement are compared. This paper also discusses the quality measures parameters of image processing which will be helpful to see clear images.

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Babatunde, Akinbowale Nathaniel, Ebunayo Rachael Jimoh, Oladipupo Oshodi i Olujuwon Ayoseyi Alabi. "Performance analysis of gray code number system in image security". Jurnal Teknologi dan Sistem Komputer 7, nr 4 (5.09.2019): 141–46. http://dx.doi.org/10.14710/jtsiskom.7.4.2019.141-146.

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The encryption of digital images has become essential since it is vulnerable to interception while being transmitted or stored. A new image encryption algorithm to address the security challenges of traditional image encryption algorithms is presented in this research. The proposed scheme transforms the pixel information of an original image by taking into consideration the pixel location such that two neighboring pixels are processed via two separate algorithms. The proposed scheme utilized the Gray code number system. The experimental results and comparison shows the encrypted images were different from the original images. Also, pixel histogram revealed that the distribution of the plain images and their decrypted images have the same pixel histogram distributions, which means that there is a high correlation between the original images and decrypted images. The scheme also offers strong resistance to statistical attacks.

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ITO, Kunio. "Image analysis." Journal of Japan Institute of Light Metals 43, nr 1 (1993): 57–64. http://dx.doi.org/10.2464/jilm.43.57.

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Pavlidis, T. "Image Analysis". Annual Review of Computer Science 3, nr 1 (czerwiec 1988): 121–46. http://dx.doi.org/10.1146/annurev.cs.03.060188.001005.

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Owen, Ronald. "Image analysis". IEE Review 35, nr 2 (1989): 77. http://dx.doi.org/10.1049/ir:19890039.

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Cox, Stuart, Ann Mortmer i Paul Jackson. "Image analysis". Psychiatric Bulletin 18, nr 11 (listopad 1994): 705–6. http://dx.doi.org/10.1192/pb.18.11.705-a.

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Stephenson, Timothy J. "Image analysis". Journal of Pathology 166, nr 1 (styczeń 1992): 83–87. http://dx.doi.org/10.1002/path.1711660113.

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Yao, Chen, Yan Xia i Jiamin Zhu. "Image Enhancement by Frequency Analysis". MATEC Web of Conferences 228 (2018): 02008. http://dx.doi.org/10.1051/matecconf/201822802008.

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Because of lighting or the quality of CMOS/CCD, poor images are often gained, which greatly affect subjective observation. Image enhancement can improve the contrast of poor image. In our paper, we propose a new image enhancement algorithm based on frequency analysis. A central energy of FFT is utilized for computation of image enhancement factors. A linear mapping is used for image mapping. Finally, some experimental results are shown for illustration of our algorithm advantage.

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Geladi, Paul, Hans Grahn, Kim Esbensen i Ewert Bengtsson. "Image analysis in chemistry II. Multivariate image analysis". TrAC Trends in Analytical Chemistry 11, nr 3 (marzec 1992): 121–30. http://dx.doi.org/10.1016/0165-9936(92)85010-3.

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Darrouzet, F., J. De Keyser, P. M. E. Décréau, D. L. Gallagher, V. Pierrard, J. F. Lemaire, B. R. Sandel i in. "Analysis of plasmaspheric plumes: CLUSTER and IMAGE observations". Annales Geophysicae 24, nr 6 (3.07.2006): 1737–58. http://dx.doi.org/10.5194/angeo-24-1737-2006.

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Abstract. Plasmaspheric plumes have been routinely observed by CLUSTER and IMAGE. The CLUSTER mission provides high time resolution four-point measurements of the plasmasphere near perigee. Total electron density profiles have been derived from the electron plasma frequency identified by the WHISPER sounder supplemented, in-between soundings, by relative variations of the spacecraft potential measured by the electric field instrument EFW; ion velocity is also measured onboard these satellites. The EUV imager onboard the IMAGE spacecraft provides global images of the plasmasphere with a spatial resolution of 0.1 RE every 10 min; such images acquired near apogee from high above the pole show the geometry of plasmaspheric plumes, their evolution and motion. We present coordinated observations of three plume events and compare CLUSTER in-situ data with global images of the plasmasphere obtained by IMAGE. In particular, we study the geometry and the orientation of plasmaspheric plumes by using four-point analysis methods. We compare several aspects of plume motion as determined by different methods: (i) inner and outer plume boundary velocity calculated from time delays of this boundary as observed by the wave experiment WHISPER on the four spacecraft, (ii) drift velocity measured by the electron drift instrument EDI onboard CLUSTER and (iii) global velocity determined from successive EUV images. These different techniques consistently indicate that plasmaspheric plumes rotate around the Earth, with their foot fully co-rotating, but with their tip rotating slower and moving farther out.

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Puchades-Izaguirre, Yaquelin Puchades-Izaguirre, Mónica Tamayo-Isaac, Wilfre Abiche-Maceo, Reynaldo Rodríguez-Gross, María La O. Hechavarría i Mérida L. Rodríguez-Regal. "Assessing sugarcane brown rust resistance using Image analysis". Bionatura 6, nr 2 (15.05.2021): 1698–703. http://dx.doi.org/10.21931/rb/2021.06.02.6.

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Image analysis provides an accurate and precise method of pest evaluation. This work's objective was to compare the usefulness of the ImageJ® 1.43u image processor and visual estimation as methods to characterize brown rust lesions and estimate the resistance of new sugarcane cultivars. For this, leaves images of 10 cultivars were captured, and the parameters quantity, most regular size of the pustules, and leaf area affected were determined. The data were correlated with the eight control (standard) genotypes' evaluations to obtain a classification of disease resistance. The results showed that the software's determinations were the most accurate, although all the methods were reliable for rating the reaction to brown rust. Therefore, it is proposed to move away from visual disease assessment toward a system based on digital image analysis.

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Liang, Dong Tai. "Color Image Denoising Using Gaussian Multiscale Multivariate Image Analysis". Applied Mechanics and Materials 37-38 (listopad 2010): 248–52. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.248.

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Inspired by the human vision system, a new image representation and analysis model based on Gaussian multiscale multivariate image analysis (MIA) is proposed. The multiscale color texture representations for the original image are used to constitute the multivariate image, each channel of which represents a perceptual observation from different scales. Then the MIA decomposes this multivariate image into multiscale color texture perceptual features (the principal component score images). These score images could be interpreted as 1) the output of three color opponent channels: black versus white, red versus green and blue versus yellow, and 2) the edge information, and 3) higher-order Gaussian derivatives. Finally the color image denoising approach based on the models is presented. Experiments show that this denoising method against Gaussian filters significantly improves the denoising effect by preserving more edge information.

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You, Hongxia. "Modeling and Analysis of Multifocus Picture Division Algorithm Based on Deep Learning". Journal of Function Spaces 2022 (16.07.2022): 1–10. http://dx.doi.org/10.1155/2022/8326626.

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As a complex machine learning algorithm, deep learning can extract object shape information and more complex and advanced information in images by using a deep learning model. In order to solve some problems of deep learning in image feature extraction and classification, this paper designs a modeling method of multifocus image segmentation algorithm based on deep learning. The acceleration effect of FPGA (field programmable gate array) on deep learning and weight sharing is analyzed. By introducing deep learning, the trouble of determining the weight coefficient is eliminated, and the energy function is simplified. Therefore, the relevant parameters of multifocus image segmentation can be easily set, and better results can be obtained. The multifocus image segmentation algorithm based on deep learning can not only obtain closed and smooth segmentation curves but also adaptively deal with topology changes due to high segmentation accuracy and stable algorithm. The results show that the model effectively combines the local and global information of the image, so that the model has good robustness. The depth learning algorithm is used to calculate the average value of local inner and outer pixels of an image. Even for complex images, relatively simple contour curves can be obtained.

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Geladi, Paul, Svante Wold i Kim Esbensen. "Image analysis and chemical information in images". Analytica Chimica Acta 191 (1986): 473–80. http://dx.doi.org/10.1016/s0003-2670(00)86335-7.

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Karthikeyan, B. "Analysis of Image Segmentation for Radiographic Images". Indian Journal of Science and Technology 5, nr 11 (20.11.2012): 1–5. http://dx.doi.org/10.17485/ijst/2012/v5i11.9.

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Ewing, Robert P., i Robert Horton. "Quantitative Color Image Analysis of Agronomic Images". Agronomy Journal 91, nr 1 (styczeń 1999): 148–53. http://dx.doi.org/10.2134/agronj1999.00021962009100010023x.

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Shariff, Aabid, Joshua Kangas, Luis Pedro Coelho, Shannon Quinn i Robert F. Murphy. "Automated Image Analysis for High-Content Screening and Analysis". Journal of Biomolecular Screening 15, nr 7 (20.05.2010): 726–34. http://dx.doi.org/10.1177/1087057110370894.

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The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.

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Han, KyoungSoo, BooJoong Kang i Eul Gyu Im. "Malware Analysis Using Visualized Image Matrices". Scientific World Journal 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/132713.

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This paper proposes a novel malware visual analysis method that contains not only a visualization method to convert binary files into images, but also a similarity calculation method between these images. The proposed method generates RGB-colored pixels on image matrices using the opcode sequences extracted from malware samples and calculates the similarities for the image matrices. Particularly, our proposed methods are available for packed malware samples by applying them to the execution traces extracted through dynamic analysis. When the images are generated, we can reduce the overheads by extracting the opcode sequences only from the blocks that include the instructions related to staple behaviors such as functions and application programming interface (API) calls. In addition, we propose a technique that generates a representative image for each malware family in order to reduce the number of comparisons for the classification of unknown samples and the colored pixel information in the image matrices is used to calculate the similarities between the images. Our experimental results show that the image matrices of malware can effectively be used to classify malware families both statically and dynamically with accuracy of 0.9896 and 0.9732, respectively.

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Yu, Zhiyi. "Image deblurring: comparison and analysis". Journal of Physics: Conference Series 2634, nr 1 (1.11.2023): 012034. http://dx.doi.org/10.1088/1742-6596/2634/1/012034.

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Abstract Technological advancements and the advent of digital devices and media make images an important part of today’s social life. Image blurring is a common challenge that results from multiple factors such as object movement, camera shake, and raindrops, among others. Image deblurring has progressively become an important field of image restoration as directed by research findings. After research for more than five decades, significant research efforts have yielded useful technologies of image deblurring. This article provides an overview of the current knowledge on image deblurring technology by focusing on the classical methods and modern trends in the field. The article reviews the conventional methods and achievements made in past studies using evidence from 34 scholarly articles. The article also examines the application of algorithms in specific deblurring methodologies adopted in recent works. It covers the recent trend of learning-based models used to restore images and their effectiveness. They include Convolutional Neural Networks, Recurrent Neural Networks and Graph Convolutional Networks. Novel deep-learning deblurring techniques are also explored. Based on the findings, issues of concerns, opportunities and direction for future research are provided to advance image deblurring technologies.

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Pasqualin, Côme, François Gannier, Claire O. Malécot, Pierre Bredeloux i Véronique Maupoil. "Automatic quantitative analysis of t-tubule organization in cardiac myocytes using ImageJ". American Journal of Physiology-Cell Physiology 308, nr 3 (1.02.2015): 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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Chandrakala, M. "Image Analysis of Sauvola and Niblack Thresholding Techniques". International Journal for Research in Applied Science and Engineering Technology 9, nr VI (14.06.2021): 2353–57. http://dx.doi.org/10.22214/ijraset.2021.34569.

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Image segmentation is a critical problem in computer vision and other image processing applications. Image segmentation has become quite challenging over the years due to its widespread use in a variety of applications. Image thresholding is a popular image segmentation technique. The segmented image quality is determined by the techniques used to determine the threshold value.A locally adaptive thresholding method based on neighborhood processing is presented in this paper. The performance of locally thresholding methods like Niblack and Sauvola was demonstrated using real-world images, printed text, and handwritten text images. Threshold-based segmentation methods were investigated using misclassification error, MSE and PSNR. Experiments have shown that the Sauvola method outperforms real-world images, printed and handwritten text images in terms of misclassification error, PSNR, and MSE.

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Renukalatha, S., i K. V. Suresh. "A REVIEW ON BIOMEDICAL IMAGE ANALYSIS". Biomedical Engineering: Applications, Basis and Communications 30, nr 04 (sierpień 2018): 1830001. http://dx.doi.org/10.4015/s1016237218300018.

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Bio-medical image analysis is an interdisciplinary field which includes: biology, physics, medicine and engineering. It deals with application of image processing techniques to biological or medical problems. Medical images to be analyzed contain a lot of information regarding the anatomical structure under investigation to reveal valid diagnosis and thereby helping doctors to choose adequate therapy. Doctors usually analyse these medical images manually through visual interpretation. But visual analysis of these images by human observers is limited due to variation in interpersonal interpretations, fatigue errors, surrounding disturbances and moreover this kind of analysis is purely subjective. On the other hand, automated analysis of these images using computers with suitable techniques favours the objective analysis by an expert and thereby improving the diagnostic confidence and accuracy of analysis. This survey is a consolidation of the exhaustive literature records related to biomedical image analysis.

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Adhana, Defay Muda, Rivani Rivani i Chandra Hendriyani. "Analysis of Third Party Logistic Service in Indonesia". Image : Jurnal Riset Manajemen 11, nr 2 (17.10.2023): 226–32. http://dx.doi.org/10.17509/image.2023.021.

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The growth of the business world has encouraged the rapid development of the logistics business. To increase competitiveness, companies focus a lot on their core business in order to fulfill customers' desires and expectations, so the company started using Third Party Logistics (TPL) to increase the company's sustainability. The aim of this research is to find out how TPL services are in Indonesia. The research method used is qualitative research with a descriptive approach. The research results show that of the 8 best TPL companies in 2022, namely Kamadjaja, Agility, Ceva, DB Schenker, Siba Surya, Puninar, Pancaran, and Deliveree, all of them have implemented types of logistics services in the form of transportation, warehousing, custom service, freight finance service, IT Support, product support, and logistics management and all companies are included in the Customer Developer category where the company takes full care of their needs. For further research, the author suggests further research related to development into the next generation of logistics providers.

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Joopally, Vedarutvija, Akhil Kaundinya i Annapurna Rao. "Image Processing: Comparison and Analysis of Image Formats". International Journal for Research in Applied Science and Engineering Technology 11, nr 6 (30.06.2023): 1379–84. http://dx.doi.org/10.22214/ijraset.2023.53847.

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Abstract: As technology is growing drastically day by day, undoubtedly there is a rapid increase in demand for image processing in every field. Each day billions of images are clicked. Collecting and analyzing those images may give us amazing results in our real-time applications. There are many different applications of image processing in the areas of space, medical, traffic, and banking, such as planet discovery, brain tumor detection, fraud detection, and object detection, etc. The objective of this research is to compare images of different formats, different resolutions, and different sizes and analyze the differences between them. We have used open-source Python libraries, OpenCV, and Pillow which are helpful in the computer vision area. Our analysis found that working with the pillow library is more accurate than working with the OpenCV library.

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Tosi, Sébastien, Lídia Bardia, Maria Jose Filgueira, Alexandre Calon i Julien Colombelli. "LOBSTER: an environment to design bioimage analysis workflows for large and complex fluorescence microscopy data". Bioinformatics 36, nr 8 (20.12.2019): 2634–35. http://dx.doi.org/10.1093/bioinformatics/btz945.

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Abstract Summary Open source software such as ImageJ and CellProfiler greatly simplified the quantitative analysis of microscopy images but their applicability is limited by the size, dimensionality and complexity of the images under study. In contrast, software optimized for the needs of specific research projects can overcome these limitations, but they may be harder to find, set up and customize to different needs. Overall, the analysis of large, complex, microscopy images is hence still a critical bottleneck for many Life Scientists. We introduce LOBSTER (Little Objects Segmentation and Tracking Environment), an environment designed to help scientists design and customize image analysis workflows to accurately characterize biological objects from a broad range of fluorescence microscopy images, including large images exceeding workstation main memory. LOBSTER comes with a starting set of over 75 sample image analysis workflows and associated images stemming from state-of-the-art image-based research projects. Availability and implementation LOBSTER requires MATLAB (version ≥ 2015a), MATLAB Image processing toolbox, and MATLAB statistics and machine learning toolbox. Code source, online tutorials, video demonstrations, documentation and sample images are freely available from: https://sebastients.github.io. Supplementary information Supplementary data are available at Bioinformatics online.

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Vatresia, Arie, i Ferzha Putra Utama. "Morphogenesis Analysis for Digital Image Production with L-System". JUITA: Jurnal Informatika 9, nr 2 (30.11.2021): 153. http://dx.doi.org/10.30595/juita.v9i2.10394.

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The process of forming an image requires a correct color composition, location and distance between the lines to produce a good image. Human abilities in both creativity and high imagination are very limited, especially in forming new images by utilizing existing image patterns or images that resemble old images. Here we showed the implementation of L-System to generate new image generations with additional flame as a fire effect/glow on images for image transformation. This research used the L-System algorithm, Iterated Function System, and Voronoi Diagram to improve the result of image transformation. The results of this study indicated that mathematical calculations can be applied in the formation of images and the resulting images can be abstract and symmetrical. The next generation of images produced in this research can be in unlimited numbers as the generation of morphogenesis processes. The process of generating images is carried out randomly by merging the two existing images with morphogenesis analogy. The resulting images can be exported into jpg, png, and svg formats. Furthermore, this research showed that the implementation of the calculation for the variation reach the value of 99.48% while the image variation composition has a value of 99.29%.

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Sparavigna, Amelia Carolina. "Entropy in Image Analysis II". Entropy 22, nr 8 (15.08.2020): 898. http://dx.doi.org/10.3390/e22080898.

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Noble, J. Alison, Nassir Navab i H. Becher. "Ultrasonic image analysis and image-guided interventions". Interface Focus 1, nr 4 (15.06.2011): 673–85. http://dx.doi.org/10.1098/rsfs.2011.0025.

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The fields of medical image analysis and computer-aided interventions deal with reducing the large volume of digital images (X-ray, computed tomography, magnetic resonance imaging (MRI), positron emission tomography and ultrasound (US)) to more meaningful clinical information using software algorithms. US is a core imaging modality employed in these areas, both in its own right and used in conjunction with the other imaging modalities. It is receiving increased interest owing to the recent introduction of three-dimensional US, significant improvements in US image quality, and better understanding of how to design algorithms which exploit the unique strengths and properties of this real-time imaging modality. This article reviews the current state of art in US image analysis and its application in image-guided interventions. The article concludes by giving a perspective from clinical cardiology which is one of the most advanced areas of clinical application of US image analysis and describing some probable future trends in this important area of ultrasonic imaging research.

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Ushakov, K. D., i O. N. Kaneva. "COMPARATIVE ANALYSIS OF IMAGE MERGING ALGORITHMS". Applied Mathematics and Fundamental Informatics 9, nr 3 (2022): 53–59. http://dx.doi.org/10.25206/2311-4908-2022-9-3-53-59.

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The paper discusses algorithms for merging images by machine learning. Both statistical algorithms and machine learning algorithms are used in image merging. Fusion algorithms based on machine learning give a clearer and sharper image while preserving details. Pairs of images with different blurred areas were used to train the algorithm. The comparison of the developed algorithms by quality metrics is carried out.

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Deshpande, Aadit, Sundaresan Raman, Amber Dubey, Pradeep Susvar i Rajiv Raman. "An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization". PLOS ONE 18, nr 4 (21.04.2023): e0283929. http://dx.doi.org/10.1371/journal.pone.0283929.

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Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.

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Yu, Cheng Yi, Yi Ying Chang, Yen Chieh Ouyang, Shen Chuan Tai i Tzu Wei Yu. "Image Tracking and Analysis Algorithm by Independent Component Analysis". Applied Mechanics and Materials 44-47 (grudzień 2010): 1622–27. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1622.

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Along with digitizing and multimedia era, the image has not changed from the original entity into any changes can be dealt with digital preservation methods. Although the digital image capture technology means more and more developed, but there are still many variables affect the quality of an image. An image quality usually depends on the user's usage or changes in the natural environment. Due to the natural environment of the most common factors that influence is light, so an image of the brightness distribution over the target object caused by extreme hardly recognizable condition common. Therefore, we will use the independent component analysis of an input color images Red, Green, and Blue three Color Space to the main component analysis, in order to achieve the target tracking and analysis.

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V, Srujana, Chaithanya P, Ramesh B, Manoranjan S i Mahesh V. "Crop Analysis Using Image Processing". International Journal of Engineering Technology and Management Sciences 4, nr 3 (28.05.2020): 9–15. http://dx.doi.org/10.46647/ijetms.2020.v04i03.002.

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To detect the uniqueness and quantities of agriculture product images a new method is proposed using MATLAB software .In this paper we propose a method to increase the contrast level of a image with exponential low pass filter and histogram equalization technique. Next by using region props function we extract the binary features of the image, and then we calculated the number of targets in gray level image. This method can be easily applied in modern agriculture.

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Denev, Daniel. "ANALYSIS OF A STEGANOGRAPHIC METHOD FOR HIDING ONE IMAGE IN ANOTHER IMAGE USING MATLAB AND IMAGE PROCESSING TOOLBOX". Journal Scientific and Applied Research 21, nr 1 (15.11.2021): 80–86. http://dx.doi.org/10.46687/jsar.v21i1.324.

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Analysis of a Steganographic Method for Hiding an Image into a Cover Image Using MATLAB and Image Processing Toolbox: The paper presents an algorithm for hiding a 24-bit color image in bmp format in another 24-bit color image also in bmp format. The algorithm is based on the least-significant bit method and is implemented using MATLAB and Image Processing Toolbox. This algorithm can be applied for 100% reconstruction of the hidden image when four cover images are used.

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Rueden, Curtis T., Mark C. Hiner i Kevin W. Eliceiri. "ImageJ: Image Analysis Interoperability for the Next Generation of Biological Image Data". Microscopy and Microanalysis 22, S3 (lipiec 2016): 2066–67. http://dx.doi.org/10.1017/s143192761601117x.

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Saeed, Hadeel Amjed, Sumaya Hamad i Azmi Tawfik Hussain. "Analysis the digital images by using morphology operators". Indonesian Journal of Electrical Engineering and Computer Science 24, nr 3 (1.12.2021): 1654. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1654-1662.

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In this paper, we deal with morphology images that try to improve the use of images. On the one hand, the process is used to obtain the histogram of the image then converted it into a non-color image (gray scale). The next step is to perform the erosion, dilation, open and close operations on the images, how these methods have important effects, and how can be used on a variable number of images, and found the differences between them. These operations were applied on four different images, check images, four basic operations (dilation, erosion, open and close) for each image were performed. Then, retrieving process to the original state of the image (the colored copy) was applied. The results found that retrieving the original images is difficult, and there is the occurrence of some noises on the image when it was retrieved. Finally, conclusions of the work are presented.

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Hagni, Ann M. "Image analysis across length scales using automated image analysis". JOM 60, nr 4 (kwiecień 2008): 16. http://dx.doi.org/10.1007/s11837-008-0041-z.

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Deependra, Kumar Shukla, Bansal Abhishek i Singh Pawan. "Performance analysis of various copy-move forgery detection methods". i-manager's Journal on Digital Signal Processing 10, nr 2 (2022): 1. http://dx.doi.org/10.26634/jdp.10.2.19181.

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Analyzing digital images to reveal modifications is called image forensics. Digital images are now becoming incredibly popular due to the availability of several inexpensive image-capturing gadgets. These images are frequently altered, either unintentionally or intentionally, which causes the image to convey false information. Since digital images are frequently utilized as evidence in court proceedings, media, and for preserving visual records, approaches to detecting forgeries in these images should be designed. This paper thoroughly analyzes several image forgery detection strategies, including comparisons of the strategies, advantages, disadvantages, and experimental findings.

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Grande, James. "Image Analysis Effects using Image Compression". Microscopy and Microanalysis 9, S02 (21.07.2003): 754–55. http://dx.doi.org/10.1017/s1431927603443778.

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Paturel, G., R. Garnier, M. C. Marthinet, C. Petit i J. Rousseau. "An image database: Automatic image analysis". Vistas in Astronomy 40, nr 4 (styczeń 1996): 511–18. http://dx.doi.org/10.1016/s0083-6656(96)00036-0.

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Maharnisha, Gandla, Gandla Roopesh Kumar i R. Arunraj. "Satellite Image Registration and Image Fusion by using Principle Component Analysis". International Journal of Engineering & Technology 7, nr 2.19 (17.04.2018): 106. http://dx.doi.org/10.14419/ijet.v7i2.19.15063.

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This aims to fused image registration and image fusion used to spatial resolution images by principle component analysis method. Digital image processing requires either the full image or a part of image. It will be processed from the user’s point of view like the radius of object. Wavelet technique will improve the spatial resolution to produce spectral degradation in output image. To overcome the spectral degradation, PCA fusion method can be used. PCA uses curve which represent edges and extraction of the detailed information from the image.PAN and MS images are used by individual acquired low frequency approximate component and high frequency detail components in this PCA. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio and Root Mean Square Error are used. The advantages of using digital image processing are preservation of original data accuracy, flexibility and repeatability.

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