Relevant bibliographies by topics / Computer Vision Pattern Recognition

Contents

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

Academic literature on the topic 'Computer Vision Pattern Recognition'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Computer Vision Pattern Recognition.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Computer Vision Pattern Recognition"

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Computer Vision Pattern Recognition"

1

PAOLANTI, MARINA. "Pattern Recognition for challenging Computer Vision Applications." Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/252904.

Full text
Abstract:
La Pattern Recognition è lo studio di come le macchine osservano l'ambiente, imparano a distinguere i pattern di interesse dal loro background e prendono decisioni valide e ragionevoli sulle categorie di modelli. Oggi l'applicazione degli algoritmi e delle tecniche di Pattern Recognition è trasversale. Con i recenti progressi nella computer vision, abbiamo la capacità di estrarre dati multimediali per ottenere informazioni preziose su ciò che sta accadendo nel mondo. Partendo da questa premessa, questa tesi affronta il tema dello sviluppo di sistemi di Pattern Recognition per applicazioni reali come la biologia, il retail, la sorveglianza, social media intelligence e i beni culturali. L'obiettivo principale è sviluppare applicazioni di computer vision in cui la Pattern Recognition è il nucleo centrale della loro progettazione, a partire dai metodi generali, che possono essere sfruttati in più campi di ricerca, per poi passare a metodi e tecniche che affrontano problemi specifici. Di fronte a molti tipi di dati, come immagini, dati biologici e traiettorie, una difficoltà fondamentale è trovare rappresentazioni vettoriali rilevanti. Per la progettazione del sistema di riconoscimento dei modelli vengono eseguiti i seguenti passaggi: raccolta dati, estrazione delle caratteristiche, approccio di apprendimento personalizzato e analisi e valutazione comparativa. Per una valutazione completa delle prestazioni, è di grande importanza collezionare un dataset specifico perché i metodi di progettazione che sono adattati a un problema non funzionano correttamente su altri tipi di problemi. I metodi su misura, adottati per lo sviluppo delle applicazioni proposte, hanno dimostrato di essere in grado di estrarre caratteristiche statistiche complesse e di imparare in modo efficiente le loro rappresentazioni, permettendogli di generalizzare bene attraverso una vasta gamma di compiti di visione computerizzata.<br>Pattern Recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the patterns categories. Nowadays, the application of Pattern Recognition algorithms and techniques is ubiquitous and transversal. With the recent advances in computer vision, we now have the ability to mine such massive visual data to obtain valuable insight about what is happening in the world. The availability of affordable and high resolution sensors (e.g., RGB-D cameras, microphones and scanners) and data sharing have resulted in huge repositories of digitized documents (text, speech, image and video). Starting from such a premise, this thesis addresses the topic of developing next generation Pattern Recognition systems for real applications such as Biology, Retail, Surveillance, Social Media Intelligence and Digital Cultural Heritage. The main goal is to develop computer vision applications in which Pattern Recognition is the key core in their design, starting from general methods, that can be exploited in more fields, and then passing to methods and techniques addressing specific problems. The privileged focus is on up-to-date applications of Pattern Recognition techniques to real-world problems, and on interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods. The final ambition is to spur new research lines, especially within interdisciplinary research scenarios. Faced with many types of data, such as images, biological data and trajectories, a key difficulty was to nd relevant vectorial representations. While this problem had been often handled in an ad-hoc way by domain experts, it has proved useful to learn these representations directly from data, and Machine Learning algorithms, statistical methods and Deep Learning techniques have been particularly successful. The representations are then based on compositions of simple parameterized processing units, the depth coming from the large number of such compositions. It was desirable to develop new, efficient data representation or feature learning/indexing techniques, which can achieve promising performance in the related tasks. The overarching goal of this work consists of presenting a pipeline to select the model that best explains the given observations; nevertheless, it does not prioritize in memory and time complexity when matching models to observations. For the Pattern Recognition system design, the following steps are performed: data collection, features extraction, tailored learning approach and comparative analysis and assessment. The proposed applications open up a wealth of novel and important opportunities for the machine vision community. The newly dataset collected as well as the complex areas taken into exam, make the research challenging. In fact, it is crucial to evaluate the performance of state of the art methods to demonstrate their strength and weakness and help identify future research for designing more robust algorithms. For comprehensive performance evaluation, it is of great importance developing a library and benchmark to gauge the state of the art because the methods design that are tuned to a specic problem do not work properly on other problems. Furthermore, the dataset selection is needed from different application domains in order to offer the user the opportunity to prove the broad validity of methods. Intensive attention has been drawn to the exploration of tailored learning models and algorithms, and their extension to more application areas. The tailored methods, adopted for the development of the proposed applications, have shown to be capable of extracting complex statistical features and efficiently learning their representations, allowing it to generalize well across a wide variety of computer vision tasks, including image classication, text recognition and so on.
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Millman, Michael Peter. "Computer vision for yarn quality inspection." Thesis, Loughborough University, 2000. https://dspace.lboro.ac.uk/2134/34196.

Full text
Abstract:
Structural parameters that determine yarn quality include evenness, hairiness and twist. This thesis applies machine vision techniques to yarn inspection, to determine these parameters in a non-contact manner. Due to the increased costs of such a solution over conventional sensors, the thesis takes a wide look at, and where necessary develops, the potential uses of machine vision for several key aspects of yarn inspection at both low and high speed configurations. Initially, the optimum optical / imaging conditions for yarn imaging are determined by investigating the various factors which degrade a yarn image. The depth of field requirement for imaging yarns is analysed, and various solutions are discussed critically including apodisation, wave front encoding and mechanical guidance. A solution using glass plate guides is proposed, and tested in prototype. The plates enable the correct hair lengths to be seen in the image for long hairs, and also prevent damaging effects on the hairiness definition due to yarn vibration and yarn rotation. The optical system parameters and resolution limits of the yarn image when using guide plates are derived and optimised. The thesis then looks at methods of enhancing the yarn image, using various illumination methods, and incoherent and coherent dark-field imaging.
APA, Harvard, Vancouver, ISO, and other styles
6

Tordoff, Ben. "Active control of zoom for computer vision." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270752.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

Moore, Darnell Janssen. "Vision-based recognition of actions using context." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/16346.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Tsai, Ming-Jong. "A new technique for 3-D computer vision." Thesis, University of Liverpool, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240784.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Christmas, W. J. "Structural matching in computer vision using probabilistic reasoning." Thesis, University of Surrey, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308472.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Computer Vision Pattern Recognition"

1

Ma, Huimin, Liang Wang, Changshui Zhang, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88007-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Ma, Huimin, Liang Wang, Changshui Zhang, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88004-0.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ma, Huimin, Liang Wang, Changshui Zhang, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88013-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ma, Huimin, Liang Wang, Changshui Zhang, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88010-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Lin, Zhouchen, Liang Wang, Jian Yang, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31654-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lin, Zhouchen, Liang Wang, Jian Yang, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Lin, Zhouchen, Liang Wang, Jian Yang, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Peng, Yuxin, Qingshan Liu, Huchuan Lu, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Peng, Yuxin, Qingshan Liu, Huchuan Lu, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60636-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Peng, Yuxin, Qingshan Liu, Huchuan Lu, et al., eds. Pattern Recognition and Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60639-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Computer Vision Pattern Recognition"

1

Grabisch, Michel, Hung T. Nguyen, and Elbert A. Walker. "Pattern Recognition and Computer Vision." In Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8449-4_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Bermudez-Cameo, Jesus, Alberto Badias-Herbera, Manuel Guerrero-Viu, Gonzalo Lopez-Nicolas, and Jose J. Guerrero. "RGB-D Computer Vision Techniques for Simulated Prosthetic Vision." In Pattern Recognition and Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_47.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ma, Yifan, Xuefeng Liang, Xiaoyu Lin, and Guanghui Shi. "Stable Visual Pattern Mining via Pattern Probability Distribution." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8543-2_23.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Marais, Marc, and Dane Brown. "Golf Swing Sequencing Using Computer Vision." In Pattern Recognition and Image Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04881-4_28.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Bhuyan, Manas Kamal. "Fundamental Pattern Recognition Concepts." In Computer Vision and Image Processing. CRC Press, 2019. http://dx.doi.org/10.1201/9781351248396-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Gori, Marco. "What’s Wrong with Computer Vision?" In Artificial Neural Networks in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99978-4_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ma, Huimin, Liang Wang, Changshui Zhang, et al. "Correction to: Pattern Recognition and Computer Vision." In Pattern Recognition and Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88007-1_55.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wang, Hongyu, Pengpeng Qiang, Hongye Tan, and Jingchang Hu. "Enhancing Image Comprehension for Computer Science Visual Question Answering." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8429-9_39.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Das, Apurba. "Psycho-visual pattern recognition: Computer Vision." In Guide to Signals and Patterns in Image Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14172-5_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Alexandre, Luís A. "3D Computer Vision: From Points to Concepts." In Pattern Recognition: Applications and Methods. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27677-9_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Computer Vision Pattern Recognition"

1

Akintan, Oreofeoluwa A., and Daniel D. Uyeh. "Can we determine water activity in heterogeneous materials: a computer vision approach." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3053225.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Duraisamy, Prakash, James Van Haneghan, William Blackwell, Stephen C. Jackson, Murugesan G, and Tamilselvan K.S. "Classroom engagement evaluation using computer vision techniques." In Pattern Recognition and Tracking XXX, edited by Mohammad S. Alam. SPIE, 2019. http://dx.doi.org/10.1117/12.2519266.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

"Computer vision and pattern recognition: EAIT 2012." In 2012 Third International Conference on Emerging Applications of Information Technology (EAIT). IEEE, 2012. http://dx.doi.org/10.1109/eait.2012.6407882.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zong, Xiaoyu, Zhang Chunsen, and Guo Bingxuan. "A method of non-contact reading code based on computer vision." In Pattern Recognition and Computer Vision, edited by Zhiguo Cao, Yuehuang Wang, and Chao Cai. SPIE, 2018. http://dx.doi.org/10.1117/12.2284788.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Jonathan, Andreas Pangestu Lim, Paoline, Gede Putra Kusuma, and Amalia Zahra. "Facial Emotion Recognition Using Computer Vision." In 2018 Indonesian Association for Pattern Recognition International Conference (INAPR). IEEE, 2018. http://dx.doi.org/10.1109/inapr.2018.8626999.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Chiu, Kevin, and Ramesh Raskar. "Computer vision on tap." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5204229.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

"Workshop on Embedded Computer Vision." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.382959.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Weixing Wang and Lei Li. "Pattern Recognition and Computer vision for Mineral Froth." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.918.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Du, L. "A unified object-oriented toolkit for discrete contextual computer vision." In IEE Colloquium on Pattern Recognition. IEE, 1997. http://dx.doi.org/10.1049/ic:19970126.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Wang, Zhihui, Jinlin Li, and Minjun Wang. "Research on three-dimensional reconstruction method based on binocular vision." In Pattern Recognition and Computer Vision, edited by Zhiguo Cao, Yuehuang Wang, and Chao Cai. SPIE, 2018. http://dx.doi.org/10.1117/12.2283522.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Computer Vision Pattern Recognition"

1

Wright, John, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas Huang, and Shuicheng Yan. Sparse Representation for Computer Vision and Pattern Recognition. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada513248.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

Full text
Abstract:
We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, comparing it against standard CNNs, CNN-KAN hybrids, and ICB variants. KANICE consistently outperformed baseline models, achieving 99.35% accuracy on MNIST and 90.05% on the SVHN dataset. Furthermore, we introduce KANICE-mini, a compact variant designed for efficiency. A comprehensive ablation study demonstrates that KANICE-mini achieves comparable performance to KANICE with significantly fewer parameters. KANICE-mini reached 90.00% accuracy on SVHN with 2,337,828 parameters, compared to KAN-ICE’s 25,432,000. This study highlights the potential of KAN-based architectures in balancing performance and computational efficiency in image classification tasks. Our work contributes to research in adaptive neural networks, integrates mathematical theorems into deep learning architectures, and explores the trade-offs between model complexity and performance, advancing computer vision and pattern recognition. The source code for this paper is publicly accessible through our GitHub repository (https://github.com/mferdaus/kanice).
APA, Harvard, Vancouver, ISO, and other styles
4

Rodriguez, Simon, Autumn Toney, and Melissa Flagg. Patent Landscape for Computer Vision: United States and China. Center for Security and Emerging Technology, 2020. http://dx.doi.org/10.51593/20200054.

Full text
Abstract:
China’s surge in artificial intelligence development has been fueled, in large part, by advances in computer vision, the AI subdomain that makes powerful facial recognition technologies possible. This data brief compares U.S. and Chinese computer vision patent data to illustrate the different approaches each country takes to AI development.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Bajcsy, Ruzena. A Query Driven Computer Vision System: A Paradigm for Hierarchical Control Strategies during the Recognition Process of Three-Dimensional Visually Perceived Objects. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada185507.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

Full text
Abstract:
Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmarks, while others bring out more detail. We use 68 facial markup, which is a common format for many datasets. Cloud computing creates all the necessary conditions for the successful implementation of even the most complex tasks. We created a web application using the Django framework, Python language, OpenCv and Dlib libraries to recognize faces in the image. The purpose of our work is to create a software system for face recognition in the photo and identify wrinkles on the face. The algorithm for determining the presence and location of various types of wrinkles and determining their geometric determination on the face is programmed.
APA, Harvard, Vancouver, ISO, and other styles
8

Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45902.

Full text
Abstract:
The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming shortwave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.
APA, Harvard, Vancouver, ISO, and other styles
9

Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

Full text
Abstract:
Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adaptive XR environments. Additionally, edge computing and federated learning enhance processing efficiency and privacy in decentralized XR applications, while blockchain and quantum-resistant cryptography secure transactions and digital assets in the metaverse. The study explores the role of AI-enhanced security, deepfake detection, and privacy-preserving AI techniques to mitigate risks associated with AI-driven XR. Case studies in healthcare, smart cities, industrial training, and gaming illustrate real-world applications and future research directions in neuromorphic computing, brain-computer interfaces (BCI), and ethical AI governance in immersive environments. This research lays the foundation for next-generation AI-integrated XR ecosystems, ensuring seamless, secure, and scalable digital experiences. Keywords: Extended Reality (XR), Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), Artificial Intelligence (AI), Neural Rendering, Spatial Computing, Deep Learning, 5G Networks, Cloud Computing, Edge Computing, Federated Learning, Blockchain, Cybersecurity, Brain-Computer Interfaces (BCI), Quantum Computing, Privacy-Preserving AI, Human-Computer Interaction, Metaverse.
APA, Harvard, Vancouver, ISO, and other styles
10

Pasupuleti, Murali Krishna. Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv525.

Full text
Abstract:
Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences. The convergence of these technologies fosters hybrid intelligence, where AI amplifies human potential rather than replacing it. This research explores AI-augmented cognition, quantum-enhanced simulations, and AI-driven spatial computing, addressing ethical, security, and societal implications of human-machine synergy. By integrating decentralized AI governance, privacy-preserving AI techniques, and brain-computer interfaces, this study outlines a scalable framework for next-generation augmented intelligence applications in healthcare, enterprise intelligence, scientific discovery, and immersive learning. The future of AHI lies in hybrid intelligence systems that co-evolve with human cognition, ensuring responsible and transparent AI augmentation to unlock new frontiers in human potential. Keywords: Augmented Human Intelligence, Generative AI, Quantum Computing, Extended Reality, XR, AI-driven Cognition, Hybrid Intelligence, Brain-Computer Interfaces, AI Ethics, AI-enhanced Learning, Spatial Computing, Quantum AI, Immersive AI, Human-AI Collaboration, Ethical AI Frameworks.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Computer Vision Pattern Recognition' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography