Relevant bibliographies by topics / Methods of image processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Methods of image processing'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Methods of image processing"

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Mohamed Y Abdallah, Yousif, Mohamed MO Yousef, and Eltayeb W Eltayeb. "Automated Enhancement of Myocardium Images using Image Processing Methods." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 557–64. https://doi.org/10.21275/sr21709185141.

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Kotsiubivska, Kateryna, and Viktoria Tymoshenko. "Mathematical Methods of Image Processing." Digital Platform: Information Technologies in Sociocultural Sphere 2, no. 1 (2019): 41–54. https://doi.org/10.31866/2617-796x.2.1.2019.175653.

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The purpose of the study is to study the specificity of image encoding by spline interpolation, and the equation of the indicated method with other mathematical methods of encoding and image processing. Research methods. The mathematical and algorithmic models and methods of solving the problem of smoothing on the basis of spline approximation, as well as the possibility of using an appropriate mathematical apparatus for encoding and image processing. The novelty of the research is the isolation of the compression algorithm of images based on the methods of spline approximation. This
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Shambetova, Burul, and Mekia Shigute Gaso. "ANALYSIS OF EDGE DETECTION METHODS IN IMAGE PROCESSING." Alatoo Academic Studies 23, no. 2 (2023): 519–26. http://dx.doi.org/10.17015/aas.2023.232.50.

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The primary goal of computer vision is to interpret the contents of an image, which can be achieved through image segmentation. This technique involves dividing an image into meaningful regions based on the intended application. By detecting and outlining the edges of objects, we can identify them within the image. Edges refer to the boundaries between objects and the background, as well as the boundaries between overlapping objects. Through image segmentation, we can separate the image from the background and extract valuable information. Edge detection is a crucial step in image segmentation
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Davies, W. S. "Digital image processing methods." Optics and Lasers in Engineering 21, no. 4 (1994): 250–51. http://dx.doi.org/10.1016/0143-8166(94)90076-0.

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DJEBALI, M., M. MELKEMI, K. MELKEMI, and N. SAPIDIS. "COIFLET BASED METHODS FOR RANGE IMAGE PROCESSING." International Journal of Image and Graphics 07, no. 02 (2007): 321–51. http://dx.doi.org/10.1142/s0219467807002672.

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In industry applications, the range images are generally huge points arrays and are additively noised. They usually represent surfaces of 3D objects and are used for reverse engineering process in CAD/CAM domains. To compute the geometrical model of each surface present in the range image, we denoise and sub-sample the raw range data. Denoising allows us to avoid the adverse effects of the noise on the obtained result. Sub-sampling the raw range data leads to a low image processing overheads like those of segmentation process. Based on interpolation properties of particular wavelets named coif
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Patil, Varsha, Deepali Sale, and M. A. Joshi. "Image Fusion Methods and Quality Assessment Parameters." Asian Journal of Engineering and Applied Technology 2, no. 1 (2013): 40–45. http://dx.doi.org/10.51983/ajeat-2013.2.1.643.

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Image processing techniques primarily focus upon enhancing the quality of an image or a set of images and to derive the maximum information from them. Image Fusion is such a technique of producing a superior quality image from a set of available images. It is the process of combining relevant information from two or more images into a single image wherein the resulting image will be more informative and complete than any of the input images. A lot of research is being done in this field encompassing areas of Computer Vision, Automatic object detection, Image processing, parallel and distribute
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Kotsiubivska, Kateryna, and Viktoria Tymoshenko. "Mathematical Methods of Image Processing." Digital Platform: Information Technologies in Sociocultural Sphere 2, no. 1 (2019): 41–54. http://dx.doi.org/10.31866/2617-796x.2.1.2019.175653.

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Radescu, Teodor-Adrian, and Arpad Gellert. "Fog Detection through Image Processing Methods." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 13, no. 1 (2023): 28–37. http://dx.doi.org/10.2478/ijasitels-2023-0004.

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Abstract This paper presents a fog detection algorithm, highlighting the significance of continued exploration in fog identification through image processing techniques. The advancement and application of this algorithm can significantly benefit various domains, including road safety, environmental monitoring, navigation, security, surveillance, and improving existing systems’ performance. The evaluation performed on test images have shown an accuracy of 72%, a precision of 94%, a recall of 57% and an F1 score of 0.71. The proposed algorithm clearly outperformed some existing fog detection met
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Bakhshi, Ali, Kobra Hajizadeh, Mohammad Reza Tanhayi, and Reza Jamshidi. "Diabetic retinopathy diagnosis using image processing methods." Advances in Obesity, Weight Management & Control 11, no. 5 (2022): 132–34. http://dx.doi.org/10.15406/aowmc.2022.12.00375.

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Diabetes mellitus is common disease nowadays which could cause blindness. Earlier detection of diabetes signs from retina fundus images could help predicting and preventing the damages. Image processing methods could process the matrix data of pictures as blood vessel segmentation and exudate detection. In this research, the CLAHE algorithm with morphological transformations are used to blood vessel segmentation and determination of the Hessian matrix of images are utilized to detect the exudate blobs
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Chulichkov, Alexey I., and Dmitriy A. Balakin. "Measurement Reduction Methods for Processing Tomographic Images." Sensors 23, no. 2 (2023): 563. http://dx.doi.org/10.3390/s23020563.

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The importance of development of new methods for reconstruction of an object image given its sinogram and some additional information about the object stems from the possibility of artifact presence in the reconstructed image, or its insufficient sharpness when the used additional information does not hold. The problem of recovering artifact-free images of the studied object from tomography data is considered in the framework of the theory of computer-aided measuring systems. Methods for solving it are developed. They are based on narrowing the class of possible images using less artifact-indu
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Dissertations / Theses on the topic "Methods of image processing"

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Alexander, Simon. "Multiscale Methods in Image Modelling and Image Processing." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/1179.

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The field of modelling and processing of 'images' has fairly recently become important, even crucial, to areas of science, medicine, and engineering. The inevitable explosion of imaging modalities and approaches stemming from this fact has become a rich source of mathematical applications. <br /><br /> 'Imaging' is quite broad, and suffers somewhat from this broadness. The general question of 'what is an image?' or perhaps 'what is a natural image?' turns out to be difficult to address. To make real headway one may need to strongly constrain the class of images being conside
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Kokkinou, Eleni. "Image processing methods in digital autoradiography." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/844272/.

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Autoradiography is a common method in biomedical research for detecting and measuring biodistributions of labelled biomolecules within a specimen. The conventional method is based on using film or film-emulsions for the image acquisition. Although film autoradiography is still in widespread use, there are several disadvantages such as long exposure times, lack of sensitivity, non-linear response of the film and limited dynamic range that encouraged the development of digital autoradiographic systems. Most of the current digital imaging systems have demonstrated excellent performance as far as
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Chang, William. "Representation Theoretical Methods in Image Processing." Scholarship @ Claremont, 2004. https://scholarship.claremont.edu/hmc_theses/160.

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Image processing refers to the various operations performed on pictures that are digitally stored as an aggregate of pixels. One can enhance or degrade the quality of an image, artistically transform the image, or even find or recognize objects within the image. This paper is concerned with image processing, but in a very mathematical perspective, involving representation theory. The approach traces back to Cooley and Tukey’s seminal paper on the Fast Fourier Transform (FFT) algorithm (1965). Recently, there has been a resurgence in investigating algebraic generalizations of this original algo
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Rao, Tandhoni. "Noncausal methods and models for image." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/13344.

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Hellier, Pierre. "Image processing methods for 3D intraoperative ultrasound." Habilitation à diriger des recherches, Université Rennes 1, 2010. http://tel.archives-ouvertes.fr/tel-00536133.

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Ce document constitue une synth`ese de travaux de recherche en vue de l'obten- tion du diplˆome d'habilitation `a diriger les recherches. A la suite ce cette in- troduction r ́edig ́ee en franc ̧ais, le reste de ce document sera en anglais. Je suis actuellement charg ́e de recherches INRIA au centre de Rennes Bretagne Atlantique. J'ai rejoint en Septembre 2001 l' ́equipe Vista dirig ́ee par Patrick Bouthemy, puis l' ́equipe Visages dirig ́ee par Christian Barillot en Janvier 2004. Depuis Janvier 2010, je travaille dans l' ́equipe-projet Serpico dirig ́ee par Charles Kervrann dont l'objet est l
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Zhang, Huaizhong. "Developing Bayesian methods for biomedical image processing." Thesis, University of Ulster, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553883.

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The broad objective of this thesis is the development of application-specific, computation ally efficient image processing approaches in the light of Bayesian theory when applied to Biomedical Imagery. Due to its rigorous theoretical background and precise formula expression, the Bayesian method has been successfully applied in image processing over the years. Based on the investigation of relationships between various optimization criteria within the Bayesian framework, the main contributions of this thesis lie in the development of four different, but interrelated, image processing approache
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Nam, David Clarence. "Novel image processing methods for cellular micrographs." Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.681506.

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Interdisciplinary research plays an ever-increasing role in advancing the state-of-the art in bioimage analysis. Advances in biomedical imaging will give biologists the tools to analyse large numbers of electron and light microscopy images, quickly and accurately. Currently, many biologists analyse images manually. This is not reproducible and susceptible to subjective bias. Image segmentation and registration are two fundamental techniques, which are frequently applied to microscopy images. Cellular images contain many complex structures, and to be able to get accurate information from these
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Liu, Pan. "Variational and PDE Methods for Image Processing." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/945.

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Sallee, Philip Andrew. "Statistical methods for image and signal processing /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2004. http://uclibs.org/PID/11984.

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Gray, Michael Stewart. "Unsupervised statistical methods for processing of image sequences /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9901442.

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Books on the topic "Methods of image processing"

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R, Dougherty Edward, ed. Digital image processing methods. M. Dekker, 1994.

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Carvalho, Vitor Hugo. Image processing: Methods, applications, and challenges. Nova Science Publishers, 2011.

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Oberti, Frederic. Image processing using parallel processing methods: Wavelet approach. UMIST, 1997.

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Lenz, Reiner. Group Theoretical Methods in Image Processing. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52290-5.

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Lenz, Reiner. Group theoretical methods in image processing. Springer-Verlag, 1990.

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M, Blackledge J., and Institute of Mathematics and Its Applications., eds. Image processing: Mathematical methods and applications. Clarendon Press, 1997.

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Rastislav, Lukac, and Plataniotis Konstantinos N, eds. Color image processing: Methods and applications. CRC/Taylor & Francis, 2007.

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Donat-Peter, Häder, ed. Image analysis: Methods and applications. 2nd ed. CRC Press, 2000.

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Burger, Wilhelm. Principles of Digital Image Processing: Advanced Methods. Springer London, 2013.

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Giardina, Charles R. Morphological methods in image and signal processing. Prentice-Hall, 1988.

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Book chapters on the topic "Methods of image processing"

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Peng, Hong, and Jun Wang. "Image Processing." In Computational Intelligence Methods and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5280-5_6.

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Klemm, Anna, and Kota Miura. "Batch Processing Methods in ImageJ." In Bioimage Data Analysis Workflows ‒ Advanced Components and Methods. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-76394-7_2.

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AbstractIn this chapter you will learn how to execute a workflow on not only one image but on several images in ImageJ – a technique that is called “Batch Processing”. Various ways of doing this are possible in the Fiji distribution of ImageJ, and the characteristics of each and how-to are explained.
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Yoon, Seung-Chul, and Bosoon Park. "Hyperspectral Image Processing Methods." In Food Engineering Series. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2836-1_4.

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Kumar, Amit, and Fahimuddin Shaik. "Image Processing Methods Utilized." In Image Processing in Diabetic Related Causes. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-624-9_3.

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Arheit, Marcel, Daniel Castaño-Díez, Raphaël Thierry, Bryant R. Gipson, Xiangyan Zeng, and Henning Stahlberg. "Image Processing of 2D Crystal Images." In Methods in Molecular Biology. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-176-9_10.

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Miller, J. W. V., and M. Shridhar. "Methods for Illumination-Invariant Image Processing." In Image Technology. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-58288-2_14.

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Shapira, Yair. "Applications in Image Processing." In Numerical Methods and Algorithms. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3726-4_6.

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Couprie, Camille, Laurent Najman, and Hugues Talbot. "Seeded Segmentation Methods for Medical Image Analysis." In Medical Image Processing. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9779-1_3.

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Gilboa, Guy. "Variational Methods in Image Processing." In Nonlinear Eigenproblems in Image Processing and Computer Vision. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75847-3_2.

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Tanaka, Nobuo. "What are Image Processing Methods?" In Electron Nano-Imaging. Springer Japan, 2017. http://dx.doi.org/10.1007/978-4-431-56502-4_21.

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Conference papers on the topic "Methods of image processing"

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Zhang, Mohan, and Yanwu Wang. "Research on Infrared Sequence Image Processing Methods." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10958167.

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Konstantinov, Yu A., F. L. Barkov, and A. I. Krivosheev. "Increasing BOTDA precision using correlation image processing methods." In 2024 International Conference Laser Optics (ICLO). IEEE, 2024. http://dx.doi.org/10.1109/iclo59702.2024.10624329.

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Overmeyer, Austin. "A Rotor Tip Vortex Tracing Algorithm for Image Post-Processing." In Vertical Flight Society 71st Annual Forum & Technology Display. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10278.

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A neurite tracing algorithm, originally developed for medical image processing, was used to trace the location of the rotor tip vortex in density gradient flow visualization images. The tracing algorithm was applied to several representative test images to form case studies. The accuracy of the tracing algorithm was compared to two current methods including a manual point and click method and a cross-correlation template method. It is shown that the neurite tracing algorithm can reduce the post-processing time to trace the vortex by a factor of 10 to 15 without compromising the accuracy of the
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Elbakary, Mohamed I., and Khan Iftekharuddin. "COVID-19 detection using image analysis methods on CT images." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2581667.

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"Modern satellite image processing methods." In 2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2016. http://dx.doi.org/10.1109/isetc.2016.7781124.

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Slavikova, P., M. Mudrova, and A. Prochazka. "Image segmentation methods use on alloy images processing." In 2011 15th IEEE International Conference on Intelligent Engineering Systems (INES). IEEE, 2011. http://dx.doi.org/10.1109/ines.2011.5954753.

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Ghodake, Yogesh Shankar, Dipak V. Bhosale, and Sushil S. Kulkarni. "Image Processing: Methods, Techniques, Applications Review." In National Conference on Relevance of Engineering and Science for Environment and Society. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.118.30.

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Nowadays, image processing became very important especially in real-time where the results of real-time image processing failures can be severe; therefore, the study and research in methods of real-time image processing are of extreme significance. The main contribution of this paper is to provide an overview of the current state of real-time image processing research (Applications), the relevant techniques, and methods.
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Nath, Vishwesh, Kurt G. Schilling, Allison E. Hainline, et al. "SHARD: spherical harmonic-based robust outlier detection for HARDI methods." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293727.

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Kunze, Holger, Florian Kordon, Andreas Maier, and Katharina Breininger. "Direct and indirect image rotation estimation methods of orthopedic x-ray images." In Image Processing, edited by Ivana Išgum and Olivier Colliot. SPIE, 2022. http://dx.doi.org/10.1117/12.2606045.

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Alyoshin, D., А. Demianenko, and A. Solodovnik. "ABOUT DIGITAL PROCESSING OF ASTRONOMICAL IMAGES USING HISTOGRAM PROCESSING METHODS." In Proceedings of the XXVIII International Scientific and Practical Conference. RS Global Sp. z O.O., 2021. http://dx.doi.org/10.31435/rsglobal_conf/25042021/7524.

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Photos of extended objects are crucial for astronomers, as they contain enough detailed information about the celestial bodies that it is quite difficult to extract visually. Most of the information available for analyzing these objects begins with studying them with telescopes or satellites. Unfortunately, the quality of astronomical images is usually very poor compared to other real images, and this is due to the technical and physical features associated with the process of obtaining them. This increases the percentage of noise and makes it more difficult to directly use standard methods on
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Reports on the topic "Methods of image processing"

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DeVore, Ronald A. Advanced Wavelet Methods for Image and Signal Processing. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada417316.

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Healey, Glenn. Advanced Methods for Representing and Processing Hyperspectral Image Data. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada581465.

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Cheng, Qiuming. Spatially and geographically weighted multivariate analysis methods for mineral image processing. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0169.

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Acton, Scott T., and Nilanjan Ray. Clutter-Reducing, Scalable Image Processing Methods for Target Acquisition and Tracking. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada424575.

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Willsky, Alan S. Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada425745.

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Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

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Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.
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Shrestha, Tanuja, Mir A. Matin, Vishwas Chitale, and Samuel Thomas. Exploring the potential of deep learning for classifying camera trap data: A case study from Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), 2023. http://dx.doi.org/10.53055/icimod.1016.

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Data from camera trap networks provide crucial information on various important aspects of wildlife presence, movement, and behaviour. However, manual processing of large volumes of images captured is time and resource intensive. This study explores three different approaches of deep learning methods to detect and classify images of key animal species collected from the ICIMOD Knowledge Park at Godavari, Nepal. It shows that transfer learning with ImageNet pretrained models (A1) can be used to detect animal species with minimal model training and testing. These methods when scaled up offer tre
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Mazari, Mehran, Yahaira Nava-Gonzalez, Ly Jacky Nhiayi, and Mohamad Saleh. Smart Highway Construction Site Monitoring Using Artificial Intelligence. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2336.

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Construction is a large sector of the economy and plays a significant role in creating economic growth and national development,and construction of transportation infrastructure is critical. This project developed a method to detect, classify, monitor, and track objects during the construction, maintenance, and rehabilitation of transportation infrastructure by using artificial intelligence and a deep learning approach. This study evaluated the performance of AI and deep learning algorithms to compare their performance in detecting and classifying the equipment in various construction scenes.
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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose t
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no
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