Relevant bibliographies by topics / Image processing. Computer vision

Contents

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

Academic literature on the topic 'Image processing. Computer vision'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Image processing. Computer vision"

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Mason, Scott. "Computer vision and image processing." ISPRS Journal of Photogrammetry and Remote Sensing 48, no. 2 (1993): 24–25. http://dx.doi.org/10.1016/0924-2716(93)90037-n.

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Pedrycz, Witold. "Computer vision and image processing." Fuzzy Sets and Systems 42, no. 3 (1991): 400. http://dx.doi.org/10.1016/0165-0114(91)90121-6.

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Defée, Irek. "Computer vision and image processing." Signal Processing 24, no. 2 (1991): 241. http://dx.doi.org/10.1016/0165-1684(91)90135-6.

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Nakajima, Masayuki, Shiniti Murakami, Katsuyuki Shinohara, Kunio Kondo, Kazumasa Enami, and Takehiro Kurono. "Image Processing and Computer Vision." Journal of the Institute of Television Engineers of Japan 48, no. 7 (1994): 828–33. http://dx.doi.org/10.3169/itej1978.48.828.

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Rakhimov, Bakhtiyar Saidovich, Feroza Bakhtiyarovna Rakhimova, Sabokhat Kabulovna Sobirova, Furkat Odilbekovich Kuryazov, and Dilnoza Boltabaevna Abdirimova. "Review And Analysis Of Computer Vision Algorithms." American Journal of Applied sciences 03, no. 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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Horace H-S, Ip. "Digital image processing and computer vision." Image and Vision Computing 8, no. 3 (1990): 254. http://dx.doi.org/10.1016/0262-8856(90)90079-k.

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Wiley, Victor, and Thomas Lucas. "Computer Vision and Image Processing: A Paper Review." International Journal of Artificial Intelligence Research 2, no. 1 (2018): 22. http://dx.doi.org/10.29099/ijair.v2i1.42.

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Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information, understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance.
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Gawande, Mohini. "Image Detection System Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 5356–62. http://dx.doi.org/10.22214/ijraset.2021.36190.

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The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efficiently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.
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Abdulhamid, Mohanad, and Lwanga Wanjira. "Image Processing Techniques Based Crowd Size Estimation." Radioelectronics. Nanosystems. Information Technologies 12, no. 3 (2020): 407–14. http://dx.doi.org/10.17725/rensit.2020.12.407.

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Image processing algorithms are the basis for image computer analysis and machine Vision. Employing a theoretical foundation, image algebra, and powerful development tools, Visual C++, Visual Fortran, Visual Basic, and Visual Java, high-level and efficient computer vision techniques have been developed. This paper analyzes different image processing algorithms by classifying them in logical groups. In addition, specific methods are presented illustrating the application of such techniques to the real world images. In most cases more than one method is used. This allows a basis for comparison of different methods as advantageous features as well as negative characteristics of each technique is delineated. The main objective of this paper is to use image processing techniques to estimate the size of a crowd from a still photograph. The simulation results show that the different images have different efficiencies.
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Hartley, M. G. "Book Review: Computer Vision and Image Processing." International Journal of Electrical Engineering Education 28, no. 2 (1991): 143. http://dx.doi.org/10.1177/002072099102800208.

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Dissertations / Theses on the topic "Image processing. Computer vision"

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Hunt, Neil. "Tools for image processing and computer vision." Thesis, University of Aberdeen, 1990. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU025003.

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The thesis describes progress towards the construction of a seeing machine. Currently, we do not understand enough about the task to build more than the simplest computer vision systems; what is understood, however, is that tremendous processing power will surely be involved. I explore the pipelined architecture for vision computers, and I discuss how it can offer both powerful processing and flexibility. I describe a proposed family of VLSI chips based upon such an architecture, each chip performing a specific image processing task. The specialisation of each chip allows high performance to be achieved, and a common pixel interconnect interface on each chip allows them to be connected in arbitrary configurations in order to solve different kinds of computational problems. While such a family of processing components can be assembled in many different ways, a programmable computer offers certain advantages, in that it is possible to change the operation of such a machine very quickly, simply by substituting a different program. I describe a software design tool which attempts to secure the same kind of programmability advantage for exploring applications of the pipelined processors. This design tool simulates complete systems consisting of several of the proposed processing components, in a configuration described by a graphical schematic diagram. A novel time skew simulation technique developed for this application allows coarse grain simulation for efficiency, while preserving the fine grain timing details. Finally, I describe some experiments which have been performed using the tools discussed earlier, showing how the tools can be put to use to handle real problems.
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Arnab, Sylvester. "Python for speech/image processing and computer vision /." Leeds, 2001. http://www.leeds.ac.uk/library/counter2/compstmsc/20002001/arnab.doc.

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Crossley, Simon. "Robust temporal stereo computer vision." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327614.

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Fletcher, Gordon James. "Geometrical problems in computer vision." Thesis, University of Liverpool, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337166.

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Tonelli, Alfredo. "Image Processing e Computer Vision con Python e OpenCV." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20390/.

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Studio di uno degli ecosistemi software oggi maggiormente utilizzati per Image Processing e Computer Vision. Lo studio affrontato parte dalle discipline di Image Processing e Computer Vision, passando per le principali soluzioni software adottate, tra le quali spiccano il linguaggio di programmazione ad alto livello Python e la libreria di Computer Vision OpenCV, per terminare con esempi pratici di base utili per capire il funzionamento delle tecnologie illustrate.
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Liu, Kexue. "Nonlinear evolutionary PDEs in image processing and computer vision." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2034.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Liu, Xu. "Computer vision and image processing techniques for mobile applications." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8916.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Clarke, Kelvyn Craig Christopher. "Computer vision for the study of fluid flows." Thesis, University of Bristol, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336853.

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Mairal, Julien. "Sparse coding for machine learning, image processing and computer vision." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2010. http://tel.archives-ouvertes.fr/tel-00595312.

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We study in this thesis a particular machine learning approach to represent signals that that consists of modelling data as linear combinations of a few elements from a learned dictionary. It can be viewed as an extension of the classical wavelet framework, whose goal is to design such dictionaries (often orthonormal basis) that are adapted to natural signals. An important success of dictionary learning methods has been their ability to model natural image patches and the performance of image denoising algorithms that it has yielded. We address several open questions related to this framework: How to efficiently optimize the dictionary? How can the model be enriched by adding a structure to the dictionary? Can current image processing tools based on this method be further improved? How should one learn the dictionary when it is used for a different task than signal reconstruction? How can it be used for solving computer vision problems? We answer these questions with a multidisciplinarity approach, using tools from statistical machine learning, convex and stochastic optimization, image and signal processing, computer vision, but also optimization on graphs.
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Ali, Abdulamer T. "Computer vision aided road traffic analysis." Thesis, University of Bristol, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333953.

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Books on the topic "Image processing. Computer vision"

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Singh, Satish Kumar, Partha Roy, Balasubramanian Raman, and P. Nagabhushan, eds. Computer Vision and Image Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1103-2.

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Singh, Satish Kumar, Partha Roy, Balasubramanian Raman, and P. Nagabhushan, eds. Computer Vision and Image Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1086-8.

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Singh, Satish Kumar, Partha Roy, Balasubramanian Raman, and P. Nagabhushan, eds. Computer Vision and Image Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1092-9.

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Raman, Balasubramanian, Subrahmanyam Murala, Ananda Chowdhury, Abhinav Dhall, and Puneet Goyal, eds. Computer Vision and Image Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11346-8.

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Raman, Balasubramanian, Subrahmanyam Murala, Ananda Chowdhury, Abhinav Dhall, and Puneet Goyal, eds. Computer Vision and Image Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11349-9.

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Nain, Neeta, Santosh Kumar Vipparthi, and Balasubramanian Raman, eds. Computer Vision and Image Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4015-8.

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Nain, Neeta, Santosh Kumar Vipparthi, and Balasubramanian Raman, eds. Computer Vision and Image Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4018-9.

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Iyer, Brijesh, A. M. Rajurkar, and Venkat Gudivada, eds. Applied Computer Vision and Image Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4029-5.

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Kalra, Prem K., and Shmuel Peleg, eds. Computer Vision, Graphics and Image Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619.

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Mukherjee, Snehasis, Suvadip Mukherjee, Dipti Prasad Mukherjee, et al., eds. Computer Vision, Graphics, and Image Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68124-5.

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Book chapters on the topic "Image processing. Computer vision"

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Krig, Scott. "Image Pre-Processing." In Computer Vision Metrics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33762-3_2.

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Krig, Scott. "Image Pre-Processing." In Computer Vision Metrics. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-5930-5_2.

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Gupta, Shivam. "Computer Vision." In Advanced Sensing in Image Processing and IoT. CRC Press, 2022. http://dx.doi.org/10.1201/9781003221333-2.

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Rahman, Mansib. "Computer Vision & Image Processing." In Beginning Microsoft Kinect for Windows SDK 2.0. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2316-1_6.

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Bezdek, James C., James Keller, Raghu Krisnapuram, and Nikhil R. Pal. "Image Processing and Computer Vision." In Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer US, 1999. http://dx.doi.org/10.1007/0-387-24579-0_5.

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Bhuyan, Manas Kamal. "Applications of Computer Vision." In Computer Vision and Image Processing. CRC Press, 2019. http://dx.doi.org/10.1201/9781351248396-5.

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Umbaugh, Scott E. "Computer Vision Development Tools." In Digital Image Processing and Analysis, 4th ed. CRC Press, 2022. http://dx.doi.org/10.1201/9781003221135-2.

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Bhuyan, Manas Kamal. "Image Processing Concepts." In Computer Vision and Image Processing. CRC Press, 2019. http://dx.doi.org/10.1201/9781351248396-2.

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Koenderink, Jan J., and Andrea J. van Doorn. "Image Processing Done Right." In Computer Vision — ECCV 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47969-4_11.

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Umbaugh, Scott E. "Image Analysis and Computer Vision." In Digital Image Processing and Analysis, 4th ed. CRC Press, 2022. http://dx.doi.org/10.1201/9781003221135-3.

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Conference papers on the topic "Image processing. Computer vision"

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Wong, R. Y., and S. M. Lee. "Robotics With Computer Vision." In Applications if Digital Image Processing IX, edited by Andrew G. Tescher. SPIE, 1986. http://dx.doi.org/10.1117/12.976235.

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Shagalova, P. A., E. S. Sokolova, and S. N. Ryndov. "Automation of Blood Microscopy Image Processing." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-1157-1164.

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Automation of medical image processing aims to create a universal tool for the purposes of medical diagnostics in the field of microscopy of cells and tissues. The authors of the paper have collected large databases of various digital microscopy images that allow to automate the processes of medical research using computer vision technologies, improve the quality of image analysis and provide a set of diagnostic information for decision making. For this purpose, a software package with a user-friendly interface has been developed that allows visualizing the results of detection of microobjects in images, determining their number and size, calculating the values of universal numerical parameters of the detected objects, creating a distribution of numerical parameter values in the form of histograms and diagrams, displaying the results in a user-friendly form, and saving the analysis results for further research. It is possible to analyze the dynamics of biomedical processes using a set of images. The modular architecture of the developed software package allows to extend its functionality, add new modules to solve biomedical problems and visualize the results of image processing. The paper presents the results of image processing of blood microscopy to determine its parameters, which include the characteristics of erythrocytes, platelets, and blood cell aggregation processes. In addition, the automated image processing system is suitable for solving problems of microscopy image analysis in other application areas.
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Asif, Tanjimul Ahad, and Baidya Nath Saha. "Instagram Image Filtration with Computer Vision." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.22.

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Instagram is one of the famous and fast-growing media sharing platforms. Instagram allows users to share photos and videos with followers. There are plenty of ways to search for images on Instagram, but one of the most familiar ways is ’hashtag.’ Hashtag search enables the users to find the precise search result on Instagram. However, there are no rules for using the hashtag; that is why it often does not match the uploaded image, and for this reason, Users are unable to find the relevant search results. This research aims to filter any human face images on search results based on hashtags on Instagram. Our study extends the author’s [2] work by implementing image processing techniques that detect human faces and separate the identified images on search results based on hashtags using the face detection technique.
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Cai, Yiheng, Lansun Shen, Baoguo Wei, and Xinfeng Zhang. "Color reproduction in computer vision." In MIPPR 2005 SAR and Multispectral Image Processing, edited by Liangpei Zhang, Jianqing Zhang, and Mingsheng Liao. SPIE, 2005. http://dx.doi.org/10.1117/12.654938.

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O'Leary, Paul, and Matther Harker. "An Algebraic Framework for Discrete Basis Functions in Computer Vision." In Image Processing (ICVGIP). IEEE, 2008. http://dx.doi.org/10.1109/icvgip.2008.107.

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Birukov, Еlissey Dmitrievich, Boris Khaimovich Barladyan, Evgeny Yurievich Denisov, and Alexey Gennadievich Voloboy. "Implementation of High Dynamic Range Image Processing Module as a Part of Lighting Simulation Software System." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-288-296.

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This article describes approaches to implementation of software module for processing of high dynamic range images, as a part of lighting simulation and photorealistic computer graphics software. Lighting simulation software requiresits own image processing application because functionality of existing ones usually is not enough for lighting simulation analysis purposes. Processing and analysis of lighting simulation results usually requires the certain functionality that is absent in many image processing programs. The article describes an application developed by us which contains such functionality related to lighting simulation. At first, it is a dynamic range compression function for displaying images on computer monitors. Besides this, our application includes additional functionality for improving image quality, such as forced desaturation of brightest highlights on the images, and also image filtering for decreasing noise caused by stochastic methods used in simulation. For simulation result analysis it is required to transfer additional data (besides image itself) to the processing module. Such layer-based data structure of high dynamic range image files generated by lighting simulations software is described.
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"OTOLITH IMAGE ANALYSIS BY COMPUTER VISION." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001067204900497.

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Ma, Ruyi. "Image processing technology based on computer vision algorithm." In 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2022. http://dx.doi.org/10.1109/icpeca53709.2022.9719261.

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Ziou, Djemel, and Madjid Allili. "Image model: new perspective for image processing and computer vision." In Electronic Imaging 2004, edited by Charles A. Bouman and Eric L. Miller. SPIE, 2004. http://dx.doi.org/10.1117/12.527132.

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Molley, Perry A. "Computer vision challenges and technologies for agile manufacturing." In Visual Communications and Image Processing '96, edited by Rashid Ansari and Mark J. T. Smith. SPIE, 1996. http://dx.doi.org/10.1117/12.233237.

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Reports on the topic "Image processing. Computer vision"

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Schoening, Timm. OceanCV. GEOMAR, 2022. http://dx.doi.org/10.3289/sw_5_2022.

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OceanCV provides computer vision algorithms and tools for underwater image analysis. This includes image processing, pattern recognition, machine learning and geometric algorithms but also functionality for navigation data processing, data provenance etc.
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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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McClure, Donald E. Image Acquisition and Processing Equipment for Machine Vision. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada253902.

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Kakarla, Sujata, J. Wakeley, and A. S. Maida. Using Computer Vision Techniques to Locate Objects in an Image. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada199835.

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Stiller, Peter. Algebraic Geometry and Computational Algebraic Geometry for Image Database Indexing, Image Recognition, And Computer Vision. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada384588.

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Hardy, B., A. D'Entremont, B. Garcia-Diaz, et al. PROCESS IMAGE ANALYSIS USING BIG DATA, MACHINE LEARNING, AND COMPUTER VISION. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1568782.

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HARDY, BRUCE, ANNA D'ENTREMONT, MICHAEL MARTINEZ-RODRIGUEZ, et al. PROCESS IMAGE ANALYSIS USING BIG DATA, MACHINE LEARNING, AND COMPUTER VISION. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1676412.

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Verly, J. G. Selected Publications in Image Understanding and Computer Vision from 1974 to 1983. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada156196.

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Kozlowski, Pawel, Yong Kim, Brian Haines, et al. Use of Computer Vision for Analysis of Image Data Sets from High Temperature Plasma Experiments. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1772402.

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Xia, Jessie Q. Image Processing and Computer Aided Diagnosis in Computed Tomography of the Breast. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada488205.

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