Relevant bibliographies by topics / Visual Computing (VIC)

Academic literature on the topic 'Visual Computing (VIC)'

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Published: 14 March 2023

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Journal articles on the topic "Visual Computing (VIC)"

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Koch, C., H. T. Wang, and B. Mathur. "Computing motion in the primate's visual system." Journal of Experimental Biology 146, no. 1 (September 1, 1989): 115–39. http://dx.doi.org/10.1242/jeb.146.1.115.

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Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We will show how one well-known gradient-based computer algorithm for estimating visual motion can be implemented within the primate's visual system. This relaxation algorithm computes the optical flow field by minimizing a variational functional of a form commonly encountered in early vision, and is performed in two steps. In the first stage, local motion is computed, while in the second stage spatial integration occurs. Neurons in the second stage represent the optical flow field via a population-coding scheme, such that the vector sum of all neurons at each location codes for the direction and magnitude of the velocity at that location. The resulting network maps onto the magnocellular pathway of the primate visual system, in particular onto cells in the primary visual cortex (V1) as well as onto cells in the middle temporal area (MT). Our algorithm mimics a number of psychophysical phenomena and illusions (perception of coherent plaids, motion capture, motion coherence) as well as electrophysiological recordings. Thus, a single unifying principle ‘the final optical flow should be as smooth as possible’ (except at isolated motion discontinuities) explains a large number of phenomena and links single-cell behavior with perception and computational theory.
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Xue-Ming, Li, Li Fang-Hua, and Fan Hai-Fu. "A revised version of the program VEC (visual computing in electron crystallography)." Chinese Physics B 18, no. 6 (June 2009): 2459–63. http://dx.doi.org/10.1088/1674-1056/18/6/056.

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Li, XiaoYong, QinYang Yu, Yong Zhang, JinWei Dai, and BaoCai Yin. "Visual Analytic Method for Students’ Association via Modularity Optimization." Applied Sciences 10, no. 8 (April 18, 2020): 2813. http://dx.doi.org/10.3390/app10082813.

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Students spend most of their time living and studying on campus, especially in Asia, and they form various types of associations in addition to those with classmates and roommates. It is necessary for university authorities to master these types of associations, so as to provide appropriate services, such as psychological guidance and academic advice. With the rapid development of the “smart campus,” many kinds of student behavior data are recorded, which provides an unprecedented opportunity to deeply analyze students’ associations. In this paper, we propose a visual analytic method to construct students’ association networks by computing the similarity of their behavior data. We discover student communities using the popular Louvain (or BGLL) algorithm, which can extract community structures based on modularity optimization. Using various visualization charts, we visualized associations among students so as to intuitively express them. We evaluated our method using the real behavior data of undergraduates in a university in Beijing. The experimental results indicate that this method is effective and intuitive for student association analysis.
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Hokkanen, J. E. "Visual simulations, artificial animals and virtual ecosystems." Journal of Experimental Biology 202, no. 23 (December 1, 1999): 3477–84. http://dx.doi.org/10.1242/jeb.202.23.3477.

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This review is about a field that does not traditionally belong to biological sciences. A branch of computer animation has its mission to create active self-powered objects living artificial lives in the theoretical biology zone. Selected work, of particular interest to biologists, is presented here. These works include animated simulations of legged locomotion, flexible-bodied animals swimming and crawling, artificial fish in virtual ecosystems, automated learning of swimming and the evolution of virtual creatures with respect to morphology, locomotion and behaviour. The corresponding animations are available for downloading via the Internet. I hope that watching these intriguing pieces of visual simulation will stimulate digitally oriented biologists to seize the interactive methods made possible by ever-increasing computing power.
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Naranjo, Diana M., José R. Prieto, Germán Moltó, and Amanda Calatrava. "A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing." Sensors 19, no. 13 (July 4, 2019): 2952. http://dx.doi.org/10.3390/s19132952.

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Cloud providers such as Amazon Web Services (AWS) stand out as useful platforms to teach distributed computing concepts as well as the development of Cloud-native scalable application architectures on real-world infrastructures. Instructors can benefit from high-level tools to track the progress of students during their learning paths on the Cloud, and this information can be disclosed via educational dashboards for students to understand their progress through the practical activities. To this aim, this paper introduces CloudTrail-Tracker, an open-source platform to obtain enhanced usage analytics from a shared AWS account. The tool provides the instructor with a visual dashboard that depicts the aggregated usage of resources by all the students during a certain time frame and the specific use of AWS for a specific student. To facilitate self-regulation of students, the dashboard also depicts the percentage of progress for each lab session and the pending actions by the student. The dashboard has been integrated in four Cloud subjects that use different learning methodologies (from face-to-face to online learning) and the students positively highlight the usefulness of the tool for Cloud instruction in AWS. This automated procurement of evidences of student activity on the Cloud results in close to real-time learning analytics useful both for semi-automated assessment and student self-awareness of their own training progress.
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KENDER, JOHN R. "VISUAL INTERFACES TO COMPUTERS: A SYSTEMS-ORIENTED FIRST COURSE IN RELIABLE CONTROL VIA IMAGERY ("VISUAL INTERFACES")." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 05 (August 2001): 869–84. http://dx.doi.org/10.1142/s0218001401001209.

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We present the rationale, description, and critique of a first course in image computing that is not a traditional computer vision principles-and-tools course. "Visual Interfaces to Computers" is instead complementary to standard Computer Vision, User Interface, and Graphics courses; in fact, VI:CV::UI:G. It is organized by case studies of working visual systems that use camera input for data or control information in service of higher user goals, such as GUI control, user identification, or automobile steering. Many CV scientific principles and engineering tools are therefore taught, as well as those of psychophysics, AI, and EE, but taught selectively and always within the context of total system design. Course content is derived from conference and journal articles and Ph.D. theses, augmented with video tapes and real-time web site demos. Students do two homework assignments, one to design a "visual combination lock", and one to parse an image into English. They also do a final paper or project of their own choosing, often in teams of two, and often with surprisingly deep results. The course is assisted by a custom C-based tool kit, "XILite", a user-friendly (and comparatively bug-free) modification of Sun's X-windows Image Library for our lab's camera-equipped Sun workstations. The course has been offered twice to a wide audience with good reviews.
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Jiang, Ming-xin, Xian-xian Luo, Tao Hai, Hai-yan Wang, Song Yang, and Ahmed N. Abdalla. "Visual Object Tracking in RGB-D Data via Genetic Feature Learning." Complexity 2019 (May 2, 2019): 1–8. http://dx.doi.org/10.1155/2019/4539410.

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Visual object tracking is a fundamental component in many computer vision applications. Extracting robust features of object is one of the most important steps in tracking. As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB-D tracking method based on genetic feature learning in this paper. Our approach addresses feature learning as an optimization problem. As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance. At the same time, unlike handcrafted feature and deep learning methods, GA can be employed to solve the problem of feature representation without prior knowledge, and it has no use for a large number of parameters to be learned. The candidate solution in RGB or depth modality is represented as an encoding of an image in GA, and genetic feature is learned through population initialization, fitness evaluation, selection, crossover, and mutation. The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed.
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Sun, Linfeng, Zhongrui Wang, Jinbao Jiang, Yeji Kim, Bomin Joo, Shoujun Zheng, Seungyeon Lee, Woo Jong Yu, Bai-Sun Kong, and Heejun Yang. "In-sensor reservoir computing for language learning via two-dimensional memristors." Science Advances 7, no. 20 (May 2021): eabg1455. http://dx.doi.org/10.1126/sciadv.abg1455.

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The dynamic processing of optoelectronic signals carrying temporal and sequential information is critical to various machine learning applications including language processing and computer vision. Despite extensive efforts to emulate the visual cortex of human brain, large energy/time overhead and extra hardware costs are incurred by the physically separated sensing, memory, and processing units. The challenge is further intensified by the tedious training of conventional recurrent neural networks for edge deployment. Here, we report in-sensor reservoir computing for language learning. High dimensionality, nonlinearity, and fading memory for the in-sensor reservoir were achieved via two-dimensional memristors based on tin sulfide (SnS), uniquely having dual-type defect states associated with Sn and S vacancies. Our in-sensor reservoir computing demonstrates an accuracy of 91% to classify short sentences of language, thus shedding light on a low training cost and the real-time solution for processing temporal and sequential signals for machine learning applications at the edge.
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Stokes, John F., and Marlene A. Devine. "Mirp: A Wearable Tool for Evaluating Effectiveness of Information Display." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 6 (September 2005): 728–31. http://dx.doi.org/10.1177/154193120504900602.

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Traditional approaches to HMI design focus on the use of visual displays and manual inputs, but these do not take advantage of the full range of means by which humans can perceive and interact with their environment. For wearable computing systems, the selection of modalities depends greatly on the proper consideration of human cognitive capabilities. The Multimodal Interface Research Platform (MIRP) is a wearable platform for evaluating task-relevant human performance by presenting information using three modalities: Visual (via head mounted display), Auditory (via earphones), and Haptic (via four vibrating actuators on the shoulders). Within the context of a predetermined task scenario, MIRP is able to monitor and record the user's interactions with the system and collect reaction time and a coarse accuracy determination of whether a message was understood. This enables observations about simple reaction time with respect to different alert/message modalities, as well as inferences about their understandability.
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Gong, Dawei, Zhiheng He, Xiaolong Ye, and Ziyun Fang. "Visual Saliency Detection for Over-Temperature Regions in 3D Space via Dual-Source Images." Sensors 20, no. 12 (June 17, 2020): 3414. http://dx.doi.org/10.3390/s20123414.

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To allow mobile robots to visually observe the temperature of equipment in complex industrial environments and work on temperature anomalies in time, it is necessary to accurately find the coordinates of temperature anomalies and obtain information on the surrounding obstacles. This paper proposes a visual saliency detection method for hypertemperature in three-dimensional space through dual-source images. The key novelty of this method is that it can achieve accurate salient object detection without relying on high-performance hardware equipment. First, the redundant point clouds are removed through adaptive sampling to reduce the computational memory. Second, the original images are merged with infrared images and the dense point clouds are surface-mapped to visually display the temperature of the reconstructed surface and use infrared imaging characteristics to detect the plane coordinates of temperature anomalies. Finally, transformation mapping is coordinated according to the pose relationship to obtain the spatial position. Experimental results show that this method not only displays the temperature of the device directly but also accurately obtains the spatial coordinates of the heat source without relying on a high-performance computing platform.
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Dissertations / Theses on the topic "Visual Computing (VIC)"

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Hernandez, Cherstin, and Maya Kronman. "Feedback på din hemmaträning : En analys av designförslag för ett korrigerande verktyg via projektion." Thesis, Högskolan Kristianstad, Fakulteten för ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-22002.

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I denna uppsats jämförs designförslag för visuell korrigerande feedback via projektor i hemmatränings miljö för att se vilken design och vilka design kombinationer som skulle vara användbart och tydligast att förstå för en användare. I och med covid 19 har det lett till att det är mer aktuellt att träna hemifrån istället för på gym för att minska smittrisken. Men i och med att det ofta inte finns samma stödjande verktyg hemma som på gym (ex. speglar och instruktörer) så kan det leda till att övningar utförs fel och kan leda till skador. Tidigt inne i utforskandet av digitala verktyg för hemmaträning kunde man se en kunskapslucka. Forskning och förslag finns runt området men få verktyg för just hemmaträning. Genom att använda tidigare forskning och tidigare kurslitteratur som bas till designförslagen så skapades det tre olika designförslag. De förslagen användes i kvalitativa intervjuer för att bedöma och kritisera de förslagen som presenterades. Efter intervjuerna evaluerades designförslagen för att landa i ett användbart och tydligt designförslag av visuell korrigerande feedback via projektor.
In this paper, design for visual corrective feedback through projection in at-home-exercising environments is compared to see what design and what design combinations would be usable and most understandable. With the Covid 19 pandemic it has become more actual to start exercising from home instead of going to gyms and increase the infection risk of the virus. However, with most homes not having all the helpful tools that the gyms might have (eg. mirrors and instructors), leads to practitioners not exercising the correct way which in turn might lead to the practitioners receiving injuries rather than the desired results. Early on in the research on digital tools for at home exercising, a knowledge gap came to view. Research and drafts do exist but solutions for at home exercising were rare to nonexistent. With the help of previous research and course literature as a base for design, three proposals were made. Those proposals were used in qualitative interviews to judge and critique the presented proposals. After the interviews, the presented design proposals were reevaluated to give a usable and understandable design proposal of visual corrective feedback through projection.
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Borke, Lukas. "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18307.

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Mit der wachsenden Popularität von GitHub, dem größten Online-Anbieter von Programm-Quellcode und der größten Kollaborationsplattform der Welt, hat es sich zu einer Big-Data-Ressource entfaltet, die eine Vielfalt von Open-Source-Repositorien (OSR) anbietet. Gegenwärtig gibt es auf GitHub mehr als eine Million Organisationen, darunter solche wie Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly und viele mehr. GitHub verfügt über eine umfassende REST API, die es Forschern ermöglicht, wertvolle Informationen über die Entwicklungszyklen von Software und Forschung abzurufen. Unsere Arbeit verfolgt zwei Hauptziele: (I) ein automatisches OSR-Kategorisierungssystem für Data Science Teams und Softwareentwickler zu ermöglichen, das Entdeckbarkeit, Technologietransfer und Koexistenz fördert. (II) Visuelle Daten-Exploration und thematisch strukturierte Navigation innerhalb von GitHub-Organisationen für reproduzierbare Kooperationsforschung und Web-Applikationen zu etablieren. Um Mehrwert aus Big Data zu generieren, ist die Speicherung und Verarbeitung der Datensemantik und Metadaten essenziell. Ferner ist die Wahl eines geeigneten Text Mining (TM) Modells von Bedeutung. Die dynamische Kalibrierung der Metadaten-Konfigurationen, TM Modelle (VSM, GVSM, LSA), Clustering-Methoden und Clustering-Qualitätsindizes wird als "Smart Clusterization" abgekürzt. Data-Driven Documents (D3) und Three.js (3D) sind JavaScript-Bibliotheken, um dynamische, interaktive Datenvisualisierung zu erzeugen. Beide Techniken erlauben Visuelles Data Mining (VDM) in Webbrowsern, und werden als D3-3D abgekürzt. Latent Semantic Analysis (LSA) misst semantische Information durch Kontingenzanalyse des Textkorpus. Ihre Eigenschaften und Anwendbarkeit für Big-Data-Analytik werden demonstriert. "Smart clusterization", kombiniert mit den dynamischen VDM-Möglichkeiten von D3-3D, wird unter dem Begriff "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA" zusammengefasst.
With the growing popularity of GitHub, the largest host of source code and collaboration platform in the world, it has evolved to a Big Data resource offering a variety of Open Source repositories (OSR). At present, there are more than one million organizations on GitHub, among them Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly and many more. GitHub provides an extensive REST API, which enables scientists to retrieve valuable information about the software and research development life cycles. Our research pursues two main objectives: (I) provide an automatic OSR categorization system for data science teams and software developers promoting discoverability, technology transfer and coexistence; (II) establish visual data exploration and topic driven navigation of GitHub organizations for collaborative reproducible research and web deployment. To transform Big Data into value, in other words into Smart Data, storing and processing of the data semantics and metadata is essential. Further, the choice of an adequate text mining (TM) model is important. The dynamic calibration of metadata configurations, TM models (VSM, GVSM, LSA), clustering methods and clustering quality indices will be shortened as "smart clusterization". Data-Driven Documents (D3) and Three.js (3D) are JavaScript libraries for producing dynamic, interactive data visualizations, featuring hardware acceleration for rendering complex 2D or 3D computer animations of large data sets. Both techniques enable visual data mining (VDM) in web browsers, and will be abbreviated as D3-3D. Latent Semantic Analysis (LSA) measures semantic information through co-occurrence analysis in the text corpus. Its properties and applicability for Big Data analytics will be demonstrated. "Smart clusterization" combined with the dynamic VDM capabilities of D3-3D will be summarized under the term "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA".
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CHIANG, LIN HSING, and 林鋅江. "Design Of The Web Based Visual Framing System Base On JavaScript via the Cloud Computing Concept." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/68451495001883737356.

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碩士
國立臺北教育大學
數位科技設計學系(含玩具與遊戲設計碩士班)
101
With the progress of the times and technological advancement, information transmission and integration of applications is an indispensable part of modern life. The thought of "over the network people can quickly obtain the information they need", is no longer satisfactory. People want to not only get information over the network, but also to process and integrate information to get solutions at faster speed and lower cost. Scholars and the computing industry are making efforts toward such needs. And the concept of "cloud computing" is formed in order to achieve this goal. This thesis is a research on the technology of cloud computing that is "capable of making drawing with browser, and without any extra component". The research is based on the concept of cloud computing and the use of the JavaScript as well as the Ajax as development core. The main focus is on the creation of a visualized online framing system which helps users do their frame works without the need to install any software (ex. Microsoft Word, Adobe Dreamweaver and so on…) and to attach any extra components (ex. Adobe Flash, Microsoft Silverlight, ActiveX and so on…) to their browser. Just only with the browser users can do their works which can only be done with multiple software in the past. And this is an integration of multiple functions in a single web interface.
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Book chapters on the topic "Visual Computing (VIC)"

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Luigini, A., G. A. Massari, S. Vattano, C. Pellegatta, and F. Luce. "Visual Culture and Cultural Heritage: ViC-CH a Synthesis Between Digital Representation and Heritage Experience." In Advances in Intelligent Systems and Computing, 288–302. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12240-9_32.

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Kotarski, Wiesław, Krzysztof Gdawiec, and Agnieszka Lisowska. "Polynomiography via Ishikawa and Mann Iterations." In Advances in Visual Computing, 305–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33179-4_30.

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Galasso, Fabio, and Joan Lasenby. "Shape from Texture Via Fourier Analysis." In Advances in Visual Computing, 803–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89639-5_77.

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Yang, Xingwei, Nagesh Adluru, Longin Jan Latecki, Xiang Bai, and Zygmunt Pizlo. "Symmetry of Shapes Via Self-similarity." In Advances in Visual Computing, 561–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89646-3_55.

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Ebrahimpour, Mohammad K., and David C. Noelle. "Fast Object Localization via Sensitivity Analysis." In Advances in Visual Computing, 209–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33723-0_17.

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Fan, Jianping, Hangzai Luo, Aoying Zhou, and Daniel A. Keim. "Personalized News Video Recommendation Via Interactive Exploration." In Advances in Visual Computing, 380–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89646-3_37.

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Miller, Corey A., and Thomas J. Walls. "Passive 3D Scene Reconstruction via Hyperspectral Imagery." In Advances in Visual Computing, 413–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14249-4_39.

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Ma, Yunqian, S. B. Damelin, O. Masoud, and N. Papanikolopoulos. "Activity Recognition Via Classification Constrained Diffusion Maps." In Advances in Visual Computing, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11919476_1.

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Wang, Tinghuai, Guangming Wang, Kuan Eeik Tan, and Donghui Tan. "Hyperspectral Image Classification via Pyramid Graph Reasoning." In Advances in Visual Computing, 707–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64556-4_55.

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Daptardar, Ajay H., and James A. Storer. "Content-Based Image Retrieval Via Vector Quantization." In Advances in Visual Computing, 502–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11595755_61.

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Conference papers on the topic "Visual Computing (VIC)"

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Sun, Zhongqian, Hongxun Yao, Shengping Zhang, and Xin Sun. "Robust visual tracking via context objects computing." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116564.

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Gebejes, Ana, Miguel Angel Martinez Domingo, Ville Heikkinen, and Ivana Tomic. "Reflectance recovery for coated printed color samples via multiangular RGB camera measurements." In 2013 Colour and Visual Computing Symposium (CVCS). IEEE, 2013. http://dx.doi.org/10.1109/cvcs.2013.6626287.

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Bottoni, P., P. Quattrocchi, and D. Ventriglia. "Constraining Concrete Syntax via Metamodel Information." In Visual Languages and Human-Centric Computing (VL/HCC'06). IEEE, 2006. http://dx.doi.org/10.1109/vlhcc.2006.17.

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Sidorov, Oleksii. "Novel Approach to Uniformization of a Color Space via Generic Deep Learning-Based Transformation." In 2018 Colour and Visual Computing Symposium (CVCS). IEEE, 2018. http://dx.doi.org/10.1109/cvcs.2018.8496586.

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Duan Xiping, Liu Jiafeng, and Tang Xianglong. "Visual tracking via weighted sparse representation." In 2014 International Conference on Intelligent Computing and Internet of Things (ICIT). IEEE, 2015. http://dx.doi.org/10.1109/icaiot.2015.7111543.

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Zhou, Kang, Chi Guo, and Huyin Zhang. "Visual Navigation via Reinforcement Learning and Relational Reasoning." In 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI). IEEE, 2021. http://dx.doi.org/10.1109/swc50871.2021.00027.

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Кучерова, Кристина, Kristina Kucherova, Сергей Мещеряков, Sergey Mescheryakov, Дмитрий Щемелинин, and Dmitriy Schemelinin. "Analysis and visualization of big data in globally distributed cloud computing monitoring systems." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-1-74-77.

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Quality of IT Services (QoS), providing across all globally distributed regions via Internet, use modern cloud computing IT technologies having big data flow and, therefore, is actual. This paper briefly describes the methods of analysis and visualization of monitoring big data based on Key Performance Indicators (KPIs) of a cloud computing IT system using real world example of globally distributed infrastructure of the International IT Company. Implementation of proposed methods of visual analytics in worldwide leading IT companies – RingCentral (USA) and Zabbix (Latvia), – allowed improving of QoS and availability of IT services up to a worldwide level of 99.999% in 24/7 mode. Implementation of new solutions in IT companies is confirmed by corresponding documents and by publications in PhD and DSc thesis of the coauthors.
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Desimone, Robert. "Neuromechanisms of visual perception and attention in monkeys." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.tut1.

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In spite of the fact that the computing architecture of the brain is massively parallel, our ability to process incoming sensory information in parallel is surprisingly limited. It is not possible, for example, to recognize within the same instant more than one or two objects in a crowded scene. Thus, much of the information impinging on the retina must be filtered out centrally, a process usually termed attention. To understand how this comes about, we have recorded from neurons in visual areas of monkey extrastriate cortex known to underlie object recognition. We have found that selective attention serves to remove irrelevant information from the receptive fields of extrastriate neurons and sharpen their selectivity for visual features such as orientation or color. These effects of attention may explain both why we have little awareness of unattended stimuli, and why our resolution of spatial location and visual features is improved inside the focus of attention. Our behavioral studies in monkeys suggest that attention controls visual processing via signals from the lateral pulvinar nucleus of the thalamus.
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Diprose, J. P. "End user robot programming via visual languages." In 2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2011). IEEE, 2011. http://dx.doi.org/10.1109/vlhcc.2011.6070406.

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Li, Wei, and Meng Ding. "Visual tracking via sparse coding and spectral residual." In 2017 International Conference on Progress in Informatics and Computing (PIC). IEEE, 2017. http://dx.doi.org/10.1109/pic.2017.8359536.

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