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Journal articles on the topic 'Computer Vision Pattern Recognition'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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1

Zheng, Nanning, George Loizou, Xiaoyi Jiang, Xuguang Lan, and Xuelong Li. "Computer vision and pattern recognition." International Journal of Computer Mathematics 84, no. 9 (2007): 1265–66. http://dx.doi.org/10.1080/00207160701303912.

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2

Astolfi, Gilberto, Fábio Prestes Cesar Rezende, João Vitor De Andrade Porto, Edson Takashi Matsubara, and Hemerson Pistori. "Syntactic Pattern Recognition in Computer Vision." ACM Computing Surveys 54, no. 3 (2021): 1–35. http://dx.doi.org/10.1145/3447241.

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Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks.

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Akata, Zeynep, Andreas Geiger, and Torsten Sattler. "Computer Vision and Pattern Recognition 2020." International Journal of Computer Vision 129, no. 12 (2021): 3169–70. http://dx.doi.org/10.1007/s11263-021-01522-3.

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Murino, Vittorio, and Andrea Trucco. "Underwater Computer Vision and Pattern Recognition." Computer Vision and Image Understanding 79, no. 1 (2000): 1–3. http://dx.doi.org/10.1006/cviu.2000.0852.

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5

Lee, Minhoon, Hobin Kim, Mikyeong Moon, and Seung-Min Park. "Computer-Vision-Based Advanced Optical Music Recognition System." Journal of Computational and Theoretical Nanoscience 18, no. 5 (2021): 1345–51. http://dx.doi.org/10.1166/jctn.2021.9626.

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Computer vision is an artificial intelligence technology that studies techniques for extracting information from images. Several studies have been performed to identify and edit music scores using computer vision. This study proposes a system to identify musical notes and print arranged music. Music is produced by general rules; consequently, the components of music have specific patterns. There are four approaches in pattern recognition that can be used classify images using patterns. Our proposed method of identifying music sheets is as follows. Several pretreatment processes (image binary, noise and staff elimination, image resizing) are performed to aid the identification. The components of the music sheet are identified by statistical pattern recognition. Applying an artificial intelligence model (Markov chain) to extracted music data aids in arranging the data. From applying the pattern recognition technique, a recognition rate of 100% was shown for music sheets of low complexity. The components included in the recognition rate are signs, notes, and beats. However, there was a low recognition rate for some music sheet and can be addressed by adding a classification to the navigation process. To increase the recognition rate of the music sheet with intermediate complexity, it is necessary to refine the pre-processing process and pattern recognition algorithm. We will also apply neural network-based models to the arrangement process.

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Lee, Minhoon, Hobin Kim, Mikyeong Moon, and Seung-Min Park. "Computer-Vision-Based Advanced Optical Music Recognition System." Journal of Computational and Theoretical Nanoscience 18, no. 5 (2021): 1345–51. http://dx.doi.org/10.1166/jctn.2021.9626.

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Computer vision is an artificial intelligence technology that studies techniques for extracting information from images. Several studies have been performed to identify and edit music scores using computer vision. This study proposes a system to identify musical notes and print arranged music. Music is produced by general rules; consequently, the components of music have specific patterns. There are four approaches in pattern recognition that can be used classify images using patterns. Our proposed method of identifying music sheets is as follows. Several pretreatment processes (image binary, noise and staff elimination, image resizing) are performed to aid the identification. The components of the music sheet are identified by statistical pattern recognition. Applying an artificial intelligence model (Markov chain) to extracted music data aids in arranging the data. From applying the pattern recognition technique, a recognition rate of 100% was shown for music sheets of low complexity. The components included in the recognition rate are signs, notes, and beats. However, there was a low recognition rate for some music sheet and can be addressed by adding a classification to the navigation process. To increase the recognition rate of the music sheet with intermediate complexity, it is necessary to refine the pre-processing process and pattern recognition algorithm. We will also apply neural network-based models to the arrangement process.

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7

DEGUCHI, Koichiro. "Introduction to Pattern Recognition and Computer Vision." Interdisciplinary Information Sciences 17, no. 2 (2011): 49–129. http://dx.doi.org/10.4036/iis.2011.49.

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8

Brill, Michael H. "Computer Vision and Pattern Recognition: CVPR 92." Color Research & Application 17, no. 6 (1992): 426–27. http://dx.doi.org/10.1002/col.5080170616.

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9

Vodyanitskyi, V., and V. Yuskovych-Zhukovska. "ADAPTIVE VISION AI." Automation of technological and business processes 16, no. 4 (2024): 73–81. https://doi.org/10.15673/atbp.v16i4.3013.

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Abstract. As of today, computer vision systems are continuously developing and systematically improving. Machines see visual content in the form of numbers, in which each pixel represents its own piece of information. Computer vision, as a component of artificial intelligence, allows machines to see, observe and understand everything. It enables computer systems to obtain useful information from digital images, video, visual data and perform programmed actions. Computer vision technologies rely on pattern recognition, machine learning, and neural networks to allow computers to break down images, interpret data, and identify features. Tracking moving objects and their identification is a difficult task, as it requires the accuracy of pattern recognition. An untrained computer vision algorithm is unable to understand the relationship between the shapes in the image and the objects. Therefore, the algorithm must be trained. The paper considers models that are trained on a high-performance computing cluster with GPU support. The developed open source software allows detection, tracking and recognition of blurry moving objects with the help of artificial intelligence that adapts to any video camera. A significant increase in accuracy is achieved thanks to machine learning.

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10

Wright, John, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas S. Huang, and Shuicheng Yan. "Sparse Representation for Computer Vision and Pattern Recognition." Proceedings of the IEEE 98, no. 6 (2010): 1031–44. http://dx.doi.org/10.1109/jproc.2010.2044470.

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Rodrigues, B. U., A. S. Soares, R. M. Costa, et al. "A feasibility cachaca type recognition using computer vision and pattern recognition." Computers and Electronics in Agriculture 123 (April 2016): 410–14. http://dx.doi.org/10.1016/j.compag.2016.03.020.

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12

Yu, Huang. "Facial expression recognition with computer vision." Applied and Computational Engineering 37, no. 1 (2024): 74–80. http://dx.doi.org/10.54254/2755-2721/37/20230473.

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Facial Expression Recognition (FER) is a specialized field within the domains of computer vision and pattern recognition, which is dedicated to the automated identification and examination of facial expressions. Facial expression recognition (FER) has attracted considerable scholarly interest in recent years owing to its diverse array of applications and its potential ramifications across multiple disciplines, such as psychology, human-computer interaction, marketing, and security systems. The objective of this study is to present a thorough examination of the scholarly progression of FER, elucidating the significant achievements, approaches, and obstacles encountered by researchers in this domain. The study presents a selection of databases that are appropriate for Facial Expression Recognition (FER) and conducts a comparative analysis of these databases. The primary methodologies are examined, and recommendations are provided for each stage. In conclusion, this research presents several suggestions for addressing both obstacles and potential in future research endeavors.

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Patel, Dhyan. "Computer Vision and Image Segmentation." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 915–25. http://dx.doi.org/10.22214/ijraset.2024.58479.

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Abstract: Image segmentation is a critical step in image processing, computer vision, and pattern recognition, which involves dividing an image into different regions or segments. Image segmentation plays an essential role in many applications, such as object recognition, medical image analysis, autonomous driving, and robotics. This paper aims to provide an overview of image segmentation techniques, including traditional and deep learning-based approaches. The paper also discusses the challenges associated with image segmentation, such as noise, illumination variations, and occlusions. Finally, the paper provides a brief discussion on the evaluation metrics used to assess the performance of image segmentation algorithms.

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Haddou Bouazza, Sara, and Jihad Haddou Bouazza. "Cancer Classification Using Pattern Recognition and Computer Vision Techniques." ITM Web of Conferences 69 (2024): 02002. https://doi.org/10.1051/itmconf/20246902002.

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The rapid advancement of DNA microarray technology has significantly contributed to the classification of various cancers, particularly leukemia. However, the high-dimensional nature of gene expression data presents challenges such as data noise and irrelevant features, leading to reduced prediction accuracy. This study proposes a novel Hybrid Filter-Wrapper Gene Selection (HFWGS) method that integrates filter-based techniques (Signal-to-Noise Ratio, Correlation Coefficient, and ReliefF) with wrapper-based approaches to enhance feature selection for leukemia classification. Additionally, a Hybrid Statistical-Gene Voting (HSGV) approach was implemented to further refine classification accuracy. A comparative analysis of classifiers, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA), demonstrated that the HFWGS method consistently improved classification performance, achieving 100% accuracy with a reduced subset of genes. The proposed methods provide an efficient framework for optimizing gene selection and improving diagnostic accuracy in leukemia, paving the way for more targeted therapeutic interventions.

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15

Gomez, Luis, Luis Alvarez, Julio Jacobo-Berlles, and Marta Mejail. "Special issue on computer vision applying pattern recognition techniques." Pattern Recognition 47, no. 1 (2014): 9–11. http://dx.doi.org/10.1016/j.patcog.2013.08.015.

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Martínez, Francisco, Ariel Carrasco, Joaquín Salas, and Gabriella Sanniti di Baja. "Pattern recognition applications in computer vision and image analysis." Pattern Recognition 48, no. 4 (2015): 1025–26. http://dx.doi.org/10.1016/j.patcog.2014.10.024.

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17

Silva, Luciano, Sudeep Sarkar, Carla Dal Sasso Freitas, and Roberto Scopigno. "SIBGRAPI 25th: Advances in Pattern Recognition and Computer Vision." Pattern Recognition Letters 39 (April 2014): 1. http://dx.doi.org/10.1016/j.patrec.2013.10.029.

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18

Chellappa, Rama. "The changing fortunes of pattern recognition and computer vision." Image and Vision Computing 55 (November 2016): 3–5. http://dx.doi.org/10.1016/j.imavis.2016.04.005.

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19

Wang, Peng. "Research on Sports Training Action Recognition Based on Deep Learning." Scientific Programming 2021 (June 29, 2021): 1–8. http://dx.doi.org/10.1155/2021/3396878.

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With the rapid development of science and technology in today’s society, various industries are pursuing information digitization and intelligence, and pattern recognition and computer vision are also constantly carrying out technological innovation. Computer vision is to let computers, cameras, and other machines receive information like human beings, analyze and process their semantic information, and make coping strategies. As an important research direction in the field of computer vision, human motion recognition has new solutions with the gradual rise of deep learning. Human motion recognition technology has a high market value, and it has broad application prospects in the fields of intelligent monitoring, motion analysis, human-computer interaction, and medical monitoring. This paper mainly studies the recognition of sports training action based on deep learning algorithm. Experimental work has been carried out in order to show the validity of the proposed research.

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20

Kovashka, Adriana, Olga Russakovsky, Li Fei-Fei, and Kristen Grauman. "Crowdsourcing in Computer Vision." Foundations and Trends® in Computer Graphics and Vision 10, no. 3 (2016): 177–243. http://dx.doi.org/10.1561/0600000071.

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21

Meghana, K. S. "Face Sketch Recognition Using Computer Vision." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 2005–9. http://dx.doi.org/10.22214/ijraset.2021.36806.

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Now-a-days need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric techniques is face recognition, since face is the convenient way used by the people to identify each-other. Understanding how humans recognize face sketches drawn by artists is of significant value to both criminal investigators and forensic researchers in Computer Vision. However, studies say that hand-drawn face sketches are still very limited in terms of artists and number of sketches because after any incident a forensic artist prepares a victim’s sketches on behalf of the description provided by an eyewitness. Sometimes suspect uses special mask to hide some common features of faces like nose, eyes, lips, face-color etc. but the outliner features of face biometrics one could never hide. Here we concentrate on some specific facial geometric feature which could be used to calculate some ratio of similarities from the template photograph database against the forensic sketches. The project describes the design of a system for face sketch recognition by a computer vision approach like Discrete Cosine Transform (DCT), Local Binary Pattern Histogram (LBPH) algorithm and a supervised machine learning model called Support Vector Machine (SVM) for face recognition. Tkinter is the standard GUI library for Python. Python when combined with Tkinter provides a fast and easy way to create GUI applications. Tkinter provides a powerful object-oriented interface to the Tk GUI toolkit.

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22

Dutta Majumder, D. "Pattern recognition, image processing and computer vision in fifth generation computer systems." Sadhana 9, no. 2 (1986): 139–56. http://dx.doi.org/10.1007/bf02747523.

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23

Hancock, E. "Editorial: IET Computer Vision." IET Computer Vision 1, no. 1 (2007): 1. http://dx.doi.org/10.1049/iet-cvi:20079011.

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24

Lampert, Christoph H. "Kernel Methods in Computer Vision." Foundations and Trends® in Computer Graphics and Vision 4, no. 3 (2007): 193–285. http://dx.doi.org/10.1561/0600000027.

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25

Orr, MJL, and RB Fisher. "Geometric reasoning for computer vision." Image and Vision Computing 5, no. 3 (1987): 233–38. http://dx.doi.org/10.1016/0262-8856(87)90054-0.

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26

Levialdi, Slefano. "Parallel architectures and computer vision." Image and Vision Computing 8, no. 2 (1990): 171. http://dx.doi.org/10.1016/0262-8856(90)90038-7.

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Hunt, BR. "Computer vision: a first course." Image and Vision Computing 8, no. 2 (1990): 171. http://dx.doi.org/10.1016/0262-8856(90)90039-8.

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28

Smith, Melvyn L., and Lyndon N. Smith. "Computer vision applications – Special issue." Image and Vision Computing 25, no. 7 (2007): 1035–36. http://dx.doi.org/10.1016/j.imavis.2007.04.001.

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29

Liu, Yanxi. "Computational Symmetry in Computer Vision and Computer Graphics." Foundations and Trends® in Computer Graphics and Vision 5, no. 1-2 (2008): 1–195. http://dx.doi.org/10.1561/0600000008.

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30

Tanimoto, Steven L. "Connecting Middle School Mathematics to Computer Vision and Pattern Recognition." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 08 (1998): 1053–70. http://dx.doi.org/10.1142/s0218001498000592.

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The subject matter of computer vision and pattern recognition can play a useful role in the education of mathematics for students in middle school. New standards in education call for new content relevant to students' lives, and new pedagogical methods involving construction, group work, discovery, and the use of new technology. The project "Mathematics Experiences Through Image Processing" at the University of Washington has developed software and learning activities that enable middle school and high school students to use mathematical tools and concepts to explore some exciting ideas of image processing. This paper describes these materials and discusses how the ideas of computer vision and pattern recognition can be integrated into the curriculum. Not only do we use 2D topics such as digital geometry and edge detection, but also 3D topics such as surface construction and stereogram generation.

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Jung, Cláudio Rosito. "Image processing, computer vision and pattern recognition in Latin America." Pattern Recognition Letters 32, no. 1 (2011): 1–2. http://dx.doi.org/10.1016/j.patrec.2010.09.015.

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32

Cagnoni, Stefano, and Mengjie Zhang. "Foreword: special issue on evolutionary computer vision and pattern recognition." Evolutionary Intelligence 9, no. 3 (2016): 53–54. http://dx.doi.org/10.1007/s12065-016-0142-5.

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33

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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Mr., Dinesh D.V. "Image Processing: A critical view of computer vision." IJAPR Journal, UGC Care Listed Journal 3, no. 2 (2023): 160–63. https://doi.org/10.5281/zenodo.8118702.

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<strong>Computer vision has been deliberate by many viewpoints. It develops from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The extensive usage has attracted many scholars to integrate with many disciplines and fields. This article provides a survey of the recent technologies and theoretical concept explaining the development of computer vision mainly related to image processing using different areas of their field application. Computer vision supports scholars to analyse images and video to obtain necessary information, understand information on events or descriptions, and beautiful pattern. It used the method of multi- range application domain with massive data analysis. This article contributes to 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.</strong>

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35

Farinella, Giovanni Maria, Marco Leo, Gerard G. Medioni, and Mohan Trivedi. "Learning and recognition for assistive computer vision." Pattern Recognition Letters 137 (September 2020): 1–2. http://dx.doi.org/10.1016/j.patrec.2019.11.006.

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36

Jolion, J. "Computer Vision Methodologies." Computer Vision and Image Understanding 59, no. 1 (1994): 53–71. http://dx.doi.org/10.1006/cviu.1994.1004.

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37

Pandey, Mrs Arjoo. "Computer Vision." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 510–14. http://dx.doi.org/10.22214/ijraset.2023.54701.

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Abstract: Computer vision is a field of artificial intelligence that focuses on enabling computers to understand and interpret visual information from images or videos. It involves developing algorithms and techniques to extract meaningful insights, patterns, and knowledge from visual data, mimicking human visual perception capabilities. The abstract of computer vision encompasses a range of fundamental tasks and objectives, including: Image Classification: Classifying images into predefined categories or classes, such as distinguishing between different objects, animals, or scenes. Object Detection and Recognition: Locating and identifying specific objects within an image or video, often through the use of bounding boxes or pixel-level segmentation. Semantic Segmentation: Assigning semantic labels to each pixel in an image to distinguish between different objects or regions.

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38

LI, Z. C., Y. Y. TANG, T. D. BUI, and C. Y. SUEN. "SHAPE TRANSFORMATION MODELS AND THEIR APPLICATIONS IN PATTERN RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (1990): 65–94. http://dx.doi.org/10.1142/s021800149000006x.

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This paper presents linear and bilinear shape transformations including basic transformations, analyzes their geometric properties, and provides computer algorithms. The shape transformations can be used to simplify the recognition of Roman letters, Chinese characters and other pictorial patterns by normalizing their shapes to the standard forms. Important theoretical analyses have been performed to illustrate that the linear and bilinear transformations are applicable to computer recognition of digitized patterns. A number of pictorial examples have been computed to confirm the analyses and conclusions made.

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39

Colbry, D., D. Cherba, and J. Luchini. "Pattern Recognition for Classification and Matching of Car Tires." Tire Science and Technology 33, no. 1 (2005): 2–17. http://dx.doi.org/10.2346/1.2186784.

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Abstract Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. Our work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. Our method begins with a selection of tire tread images obtained from multiple sources (including manufacturers' literature, Web site images, and Tire Guides, Inc.), which are preprocessed and normalized using Two-Dimensional Fast Fourier Transforms (2D-FFT). The results of this preprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating “rib” and “lug” features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.

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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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Sandini, Giulio. "2nd European conference on computer vision." Image and Vision Computing 10, no. 10 (1992): 642. http://dx.doi.org/10.1016/0262-8856(92)90008-q.

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Boissenin, M., J. Wedekind, A. N. Selvan, B. P. Amavasai, F. Caparrelli, and J. R. Travis. "Computer vision methods for optical microscopes." Image and Vision Computing 25, no. 7 (2007): 1107–16. http://dx.doi.org/10.1016/j.imavis.2006.03.009.

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Remagnino, P., and G. L. Foresti. "Computer vision methods for ambient intelligence." Image and Vision Computing 27, no. 10 (2009): 1419–20. http://dx.doi.org/10.1016/j.imavis.2009.04.009.

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44

Chelali, Fatma Zohra, and Amar Djeradi. "Face Recognition Using MLP and RBF Neural Network with Gabor and Discrete Wavelet Transform Characterization: A Comparative Study." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/523603.

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Face recognition has received a great attention from a lot of researchers in computer vision, pattern recognition, and human machine computer interfaces in recent years. Designing a face recognition system is a complex task due to the wide variety of illumination, pose, and facial expression. A lot of approaches have been developed to find the optimal space in which face feature descriptors are well distinguished and separated. Face representation using Gabor features and discrete wavelet has attracted considerable attention in computer vision and image processing. We describe in this paper a face recognition system using artificial neural networks like multilayer perceptron (MLP) and radial basis function (RBF) where Gabor and discrete wavelet based feature extraction methods are proposed for the extraction of features from facial images using two facial databases: the ORL and computer vision. Good recognition rate was obtained using Gabor and DWT parameterization with MLP classifier applied for computer vision dataset.

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Moss, Randy H., William V. Stoecker, Shi-Jen Lin, et al. "Skin cancer recognition by computer vision." Computerized Medical Imaging and Graphics 13, no. 1 (1989): 31–36. http://dx.doi.org/10.1016/0895-6111(89)90076-1.

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46

Maxwell, Bruce A. "Teaching Computer Vision to Computer Scientists." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 08 (1998): 1035–51. http://dx.doi.org/10.1142/s0218001498000580.

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Computer vision is a broad-based field of computer science that requires students to understand and integrate knowledge from numerous disciplines. Computer science (CS) majors, however, do not necessarily have an interdisciplinary background. In the rush to integrate, we can forget, or fail to plan for the fact that our students may not possess a broad undergraduate education. To explore the appropriateness of our education materials, this paper begins with a discussion of what we can expect CS majors to know and how we can use that knowledge to make a computer vision course a more enriching experience. The paper then provides a review of a number of the currently available computer vision textbooks. These texts differ significantly in their coverage, scope, approach, and audience. This comparative review shows that, while there are an increasing number of good textbooks available, there is still a need for new educational materials. In particular, the field would benefit from both an undergraduate computer vision text aimed at computer scientists and from a text with a stronger focus on color computer vision and its applications.

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47

Alexandrov, D. V. "Overview of Face Recognition Algorithms for Person Identification." Programmnaya Ingeneria 13, no. 7 (2022): 331–43. http://dx.doi.org/10.17587/prin.13.331-343.

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Trends in computer vision and pattern recognition and capabilities of modern computers contributed to a considerable amount of research of these areas application in facial recognition systems. The purpose of this paper is to investigate the most significant methods of face recognition. In the first two sections of current paper, the methods of face recognition and identification are presented. The analysis of these methods covers the most important features of the pattern recognition area. An application of groups of methods is considered for different purposes. This paper contains comments for capabilities of algorithms under observation. The third section reveals result of the algorithms testing using real-world datasets and examples.

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48

Gendy, Wen, and Dularia Patel. "Advancements in Computer Vision: A Comprehensive Survey of Image Processing and Interdisciplinary Applications." Academic Journal of Science and Technology 13, no. 2 (2024): 28–34. https://doi.org/10.54097/5e1cqw59.

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Computer vision and image processing are rapidly evolving fields with broad applications across numerous domains, including healthcare, autonomous driving, surveillance, and entertainment. These fields have transformed from simple data recording techniques into sophisticated systems that incorporate digital image processing, pattern recognition, machine learning, and computer graphics. This evolution has prompted interdisciplinary interest, pushed the technology’s boundaries and expanded its practical uses. This paper offers a comprehensive survey of recent advancements in computer vision, focusing on image processing and its applications across various fields. It delves into the theoretical foundations and technologies that make computer vision a valuable tool for interpreting images and videos, extracting relevant information, recognizing patterns, and understanding events. The ability of computer vision to analyze large datasets across multiple application domains makes it instrumental in tasks such as object identification, facial recognition, scene understanding, and even real-time action prediction. This versatility has established computer vision as a key driver of data-driven insights in both scientific and commercial sectors. The study categorizes computer vision into four main areas: image processing, object recognition, machine learning, and computer graphics. Each of these categories is essential to the functionality of modern computer vision systems. Image processing involves techniques for enhancing image quality and extracting important features. Object recognition and machine learning enable the identification of specific elements within images and allow systems to learn from large datasets, enhancing accuracy over time. Computer graphics, on the other hand, aid in visualizing and interpreting processed data. By offering insights into the latest techniques and evaluating their performance, this survey highlights the current state of computer vision while shedding light on future trends. Computer vision’s expanding utility across various fields underscores its critical role in driving interdisciplinary innovation and addressing complex challenges.

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Kumar, Sumit, and Dr Shivani Dubey. "Object Recognition Using Pattern Analysis." International Research Journal of Computer Science 11, no. 01 (2024): 11–15. http://dx.doi.org/10.26562/irjcs.2024.v1101.03.

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The proposed research paper describes an object System of recognition based on human perception, which is achieved by feature extraction (components) and pattern analysis. Both strategies are applied to each subpart. Feature extraction is done using shaping and pattern analysis is achieved by creating statistical bins for each pattern of vehicle subparts. Each bin contains general and specific measures, and each measure has a certain weight that contributes to the analysis of each bin to decide on similarity or dissimilarity. Object recognition is an important task in computer vision, with applications in robotics, driverless cars, medical image analysis, and many other applications. This article explores the concept of product analysis using benchmarks, highlighting key concepts, methods and recent developments.

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Wang, Chenzhen, and Xinglin Li. "The Application of Pattern Recognition System in Design Field Based on Aesthetic Principles." Computational Intelligence and Neuroscience 2022 (May 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/8581900.

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The design system based on aesthetic principles is the most representative in the field of design and has a certain significance for the research and construction of design aesthetics and the development of design education. Therefore, this paper studies the application of pattern recognition system in the field of design based on aesthetic principles and designs a new type of aesthetic principle design system based on pattern recognition in computer vision. This paper proposes pattern similarity measurement and image preprocessing technology to improve the traditional aesthetic principle design system through pattern recognition and then further refine the research of the whole system through histogram equalization and gamma correction. Finally, the MNIST dataset experiment is used to verify the effect of multicolumn convolutional neural network pattern recognition on the aesthetic principle design system. The questionnaire survey experiment in this article and the traditional comparative experiment show that 76% of the public are very satisfied with this design system based on the aesthetic principles of pattern recognition in computer vision. Also, the improved aesthetic principle system scores as high as 90–95 points.

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