Results for 'Computer vision'

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  1. Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis.Davide Conigliaro, Celine Hudelot, Roberta Ferrario & Daniele Porello - 2017 - In Vittorio Murino, Marco Cristani, Shishir Shah & Silvio Savarese, Group and Crowd Behavior for Computer Vision, 1st Edition. pp. 297-319.
    In this paper, building on these previous works, we propose to go deeper into the understanding of crowd behavior by proposing an approach which integrates ontologi- cal models of crowd behavior and dedicated computer vision algorithms, with the aim of recognizing some targeted complex events happening in the playground from the observation of the spectator crowd behavior. In order to do that, we first propose an ontology encoding available knowledge on spectator crowd behavior, built as a spe- cialization (...)
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  2. Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis.Daniele Porello, Celine Hudelot, Davide Conigliaro & Roberta Ferrario - 2017 - In Vittorio Murino, Marco Cristani, Shishir Shah & Silvio Savarese, Group and Crowd Behavior for Computer Vision, 1st Edition. pp. 297-319.
    In this paper, building on these previous works, we propose to go deeper into the understanding of crowd behavior by proposing an approach which integrates ontologi- cal models of crowd behavior and dedicated computer vision algorithms, with the aim of recognizing some targeted complex events happening in the playground from the observation of the spectator crowd behavior. In order to do that, we first propose an ontology encoding available knowledge on spectator crowd behavior, built as a spe- cialization (...)
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  3.  49
    Computer vision, human senses, and language of art.Lev Manovich - 2021 - AI and Society 36 (4):1145-1152.
    What is the most important reason for using Computer Vision methods in humanities research? In this article, I argue that the use of numerical representation and data analysis methods offers a new language for describing cultural artifacts, experiences and dynamics. The human languages such as English or Russian that developed rather recently in human evolution are not good at capturing analog properties of human sensorial and cultural experiences. These limitations become particularly worrying if we want to compare thousands, (...)
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  4.  26
    The ethics of computer vision: An overview in terms of power.Rosalie A. Waelen - 2023 - AI and Ethics 1.
    Computer vision is a subfeld of artifcial intelligence, aimed at making computers see. Computer vision tools enable a system or device to automatically analyze, interpret, and respond to images and videos. Computer vision tasks range from object detection and tracking, to the recognition of people’s faces and emotional states. While the ethics of AI in general has received signifcant attention, and the ethics of facial recognition (a computer vision application) too, little of (...)
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  5. Computer Vision II-Generic 3-D Modeling for Content Analysis of Court-Net Sports Sequences.Jungong Han, Dirk Farin & Peter Hn De With - 2006 - In O. Stock & M. Schaerf, Lecture Notes In Computer Science. Springer Verlag. pp. 279-288.
     
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  6.  2
    Developing computer vision and machine learning strategies to unlock government-created records.Greg Jansen & Richard Marciano - forthcoming - AI and Society:1-17.
    This paper outlines the development of a proof-of-concept workflow using machine learning and computer vision techniques to unlock the data within digitized handwritten US Census forms from the 1950s. The 1950s US Census includes over 6.5 million page images and was only recently made available to the public on April 1, 2022, following a 72-year access restriction period. Our project uses computational treatments to assist researchers in their efforts to recover and preserve the history of the erased Sacramento (...)
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  7. Computer vision for artists and designers: pedagogic tools and techniques for novice programmers. [REVIEW]Golan Levin - 2006 - AI and Society 20 (4):462-482.
    This article attempts to demystify computer vision for novice programmers through a survey of new applications in the arts, system design considerations, and contemporary tools. It introduces the concept and gives a brief history of computer vision within interactive art from Myron Kruger to the present. Basic techniques of computer vision such as detecting motion and object tracking are discussed in addition to various software applications created for exploring the topic. As an example, the (...)
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  8.  17
    Computer vision.Michael Brady - 1982 - Artificial Intelligence 19 (1):7-16.
  9.  39
    Urban-semantic computer vision: a framework for contextual understanding of people in urban spaces.Anthony Vanky & Ri Le - 2023 - AI and Society 38 (3):1193-1207.
    Increasing computational power and improving deep learning methods have made computer vision technologies pervasively common in urban environments. Their applications in policing, traffic management, and documenting public spaces are increasingly common (Ridgeway 2018, Coifman et al. 1998, Sun et al. 2020). Despite the often-discussed biases in the algorithms' training and unequally borne benefits (Khosla et al. 2012), almost all applications similarly reduce urban experiences to simplistic, reductive, and mechanistic measures. There is a lack of context, depth, and specificity (...)
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  10.  92
    Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests.Franck Nguyen, Selim M. Barhli, Daniel Pino Muñoz & David Ryckelynck - 2018 - Complexity 2018:1-10.
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  11.  89
    Optimization and simplicity: Computational vision and biological explanation.Daniel J. Gilman - 1996 - Synthese 107 (3):293 - 323.
    David Marr's theory of vision has been a rich source of inspiration, fascination and confusion. I will suggest that some of this confusion can be traced to discrepancies between the way Marr developed his theory in practice and the way he suggested such a theory ought to be developed in his explicit metatheoretical remarks. I will address claims that Marr's theory may be seen as an optimizing theory, along with the attendant suggestion that optimizing assumptions may be inappropriate for (...)
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  12.  11
    Biological and Computer Vision.Gabriel Kreiman - 2021 - Cambridge University Press.
    Imagine a world where machines can see and understand the world the way humans do. Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars that detect pedestrians, and algorithms that suggest diagnoses from clinical images, among many other applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing. This book introduces the neuroscientific study of neuronal computations in visual cortex alongside of (...)
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  13.  12
    Living on Digital Flatlands: Assemblies of Computer Vision.Alex Reid - unknown
    Drawing on radical media archeology and assemblage theory, this article investigates assemblages of computer vision as they construct new spatiotemporal relations and new capacities for seeing and acting.
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  14.  18
    How nervous am I? How computer vision succeeds and humans fail in interpreting state anxiety from dynamic facial behaviour.Mithras Kuipers, Mitchel Kappen & Marnix Naber - 2023 - Cognition and Emotion 37 (6):1105-1115.
    For human interaction, it is important to understand what emotional state others are in. Especially the observation of faces aids us in putting behaviours into context and gives insight into emotions and mental states of others. Detecting whether someone is nervous, a form of state anxiety, is such an example as it reveals a person’s familiarity and contentment with the circumstances. With recent developments in computer vision we developed behavioural nervousness models to show which time-varying facial cues reveal (...)
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  15.  23
    Human Motion Gesture Recognition Based on Computer Vision.Rui Ma, Zhendong Zhang & Enqing Chen - 2021 - Complexity 2021:1-11.
    Human motion gesture recognition is the most challenging research direction in the field of computer vision, and it is widely used in human-computer interaction, intelligent monitoring, virtual reality, human behaviour analysis, and other fields. This paper proposes a new type of deep convolutional generation confrontation network to recognize human motion pose. This method uses a deep convolutional stacked hourglass network to accurately extract the location of key joint points on the image. The generation and identification part of (...)
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  16.  36
    The system of autono‑mobility: computer vision and urban complexity—reflections on artificial intelligence at urban scale.Fabio Iapaolo - 2023 - AI and Society 38 (3):1111-1122.
    Focused on city-scale automation, and using self-driving cars (SDCs) as a case study, this article reflects on the role of AI—and in particular, computer vision systems used for mapping and navigation—as a catalyst for urban transformation. Urban research commonly presents AI and cities as having a one-way cause-and-effect relationship, giving undue weight to AI’s impact on cities and overlooking the role of cities in shaping AI. Working at the intersection of data science and social research, this paper aims (...)
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  17.  32
    ViSpa (Vision Spaces): A computer-vision-based representation system for individual images and concept prototypes, with large-scale evaluation.Fritz Günther, Marco Marelli, Sam Tureski & Marco Alessandro Petilli - 2023 - Psychological Review 130 (4):896-934.
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  18.  22
    Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision.Luo Dai - 2021 - Complexity 2021:1-10.
    Computer vision is widely used in manufacturing, sports, medical diagnosis, and other fields. In this article, a multifeature fusion error action expression method based on silhouette and optical flow information is proposed to overcome the shortcomings in the effectiveness of a single error action expression method based on the fusion of features for human body error action recognition. We analyse and discuss the human error action recognition method based on the idea of template matching to analyse the key (...)
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  19.  12
    The psychology of computer vision.Azriel Rosenfeld - 1976 - Artificial Intelligence 7 (3):279-282.
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  20.  22
    From Pixels to Principles: A Decade of Progress and Landscape in Trustworthy Computer Vision.Kexin Huang, Yan Teng, Yang Chen & Yingchun Wang - 2024 - Science and Engineering Ethics 30 (3):1-21.
    The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field (...)
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  21. Galton Reloaded: Computer Vision and Machinic Eugenics.Giselle Beiguelman - 2023 - In Giselle Beiguelman, Melody Devries, Magdalena Tyżlik-Carver & Winnie Soon, Boundary images. Minneapolis: University of Minnesota Press.
     
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  22.  57
    Artificial intelligence and institutional critique 2.0: unexpected ways of seeing with computer vision.Gabriel Pereira & Bruno Moreschi - 2021 - AI and Society 36 (4):1201-1223.
    During 2018, as part of a research project funded by the Deviant Practice Grant, artist Bruno Moreschi and digital media researcher Gabriel Pereira worked with the Van Abbemuseum collection (Eindhoven, NL), reading their artworks through commercial image-recognition (computer vision) artificial intelligences from leading tech companies. The main takeaways were: somewhat as expected, AI is constructed through a capitalist and product-focused reading of the world (values that are embedded in this sociotechnical system); and that this process of using AI (...)
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  23.  14
    Introduction to the special volume on computer vision.Narendra Ahuja & Radu Horaud - 1995 - Artificial Intelligence 78 (1-2):1-3.
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  24. An Analog VLSI Chip for Low-Level Computer Vision.Kenneth J. Janik, Shih-Lien Lu & Ben Lee - 1996 - Esda 1996: Expert Systems and Ai; Neural Networks 7:211.
  25.  12
    Measuring Art, Counting Pixels? The Collaboration of Art History and Computer Vision Oscillates Between Quantitative and Hermeneutic Methods.Peter Bell & Björn Ommer - 2022 - In Marcel Schweiker, Joachim Hass, Anna Novokhatko & Roxana Halbleib, Measurement and Understanding in Science and Humanities: Interdisciplinary Approaches. Springer Fachmedien Wiesbaden. pp. 191-200.
    The project “Artificial and Artistic Vision. Computer Vision and Art History in Practical-Methodical Cooperation” is interdisciplinary by definition and also in its personnel composition and combines the humanities, engineering, and natural sciences. Together, prototypes and methodological approaches to an automatic vision that assists art history are being developed in the form of basic research.
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  26.  15
    Object-based visual attention for computer vision.Yaoru Sun & Robert Fisher - 2003 - Artificial Intelligence 146 (1):77-123.
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  27.  33
    Vision without Frames: A Semiotic Paradigm of Event Based Computer Vision[REVIEW]Ryad Benosman - 2010 - Biosemiotics 3 (1):1-16.
    Conventional imagers and almost all vision processes use and rely on theories that are based on the principle of static image-frames. A frame is a 2D matrix that represents the spatial locations of intensities of a scene projected on the imager. The notion of a frame itself is so embedded in machine vision, that it is usually taken for granted that this is how biological systems store light information. This paper presents a biosinpired event-based image formation principle, which (...)
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  28.  24
    Environmental landscape design and planning system based on computer vision and deep learning.Xiubo Chen - 2023 - Journal of Intelligent Systems 32 (1).
    Environmental landscaping is known to build, plan, and manage landscapes that consider the ecology of a site and produce gardens that benefit both people and the rest of the ecosystem. Landscaping and the environment are combined in landscape design planning to provide holistic answers to complex issues. Seeding native species and eradicating alien species are just a few ways humans influence the region’s ecosystem. Landscape architecture is the design of landscapes, urban areas, or gardens and their modification. It comprises the (...)
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  29.  19
    CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions.Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez, Massimo Caccia, Qi She, Yu Chen, Quentin Jodelet, Ruiping Wang, Zheda Mai, David Vazquez, German I. Parisi, Nikhil Churamani, Marc Pickett, Issam Laradji & Davide Maltoni - 2022 - Artificial Intelligence 303 (C):103635.
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  30.  30
    One face, millions of faces: Computer vision as hyperobject.Sheung Yiu - 2021 - Philosophy of Photography 12 (1):71-91.
    Borrowing Timothy Morton’s notion of hyperobject, this article explores questions of network and scale in generative adversarial networks (GAN) images. In this context, the term network refers to the omnipresence of algorithmic images today and their significant impact on our lives. Such images are massively distributed in time and space beyond any sensible human-scale. Scale, in this context, denotes the relations between different operational layers of algorithmic images, such as the pictorial layer in contrast to the data layer. An algorithmic (...)
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  31. Part IV-Representation and Inference-14 Cognitive Vision: Integrating Symbolic Qualitative Representations with Computer Vision.A. G. Cohn, D. C. Hogg, B. Bennett, V. Devin, A. Galata, D. R. Magee, C. Needham & P. Santos - 2006 - In O. Stock & M. Schaerf, Lecture Notes In Computer Science. Springer Verlag. pp. 221-246.
     
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  32.  17
    Preface—The changing shape of computer vision.Michael Brady - 1981 - Artificial Intelligence 17 (1-3):1-15.
  33.  67
    Can Computational Goals Inform Theories of Vision?Barton L. Anderson - 2015 - Topics in Cognitive Science 7 (2):274-286.
    One of the most lasting contributions of Marr's posthumous book is his articulation of the different “levels of analysis” that are needed to understand vision. Although a variety of work has examined how these different levels are related, there is comparatively little examination of the assumptions on which his proposed levels rest, or the plausibility of the approach Marr articulated given those assumptions. Marr placed particular significance on computational level theory, which specifies the “goal” of a computation, its appropriateness (...)
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  34.  27
    Computational Approaches to Comics Analysis.Jochen Laubrock & Alexander Dunst - 2020 - Topics in Cognitive Science 12 (1):274-310.
    Comics are complex multimodal documents that make for intriguing materials to analyze with computer vision and computational linguistics. This review summarizes the growing developments in computational modeling which have been progressing to analyze visual narratives across their various substructures.
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  35. Content, computation, and individualism in vision theory.Keith Butler - 1996 - Analysis 56 (3):146-154.
  36. Vision and Image Processing (I)-Computer Aided Classification of Mammographic Tissue Using Independent Component Analysis and Support Vector Machines.Athanasios Koutras, Ioanna Christoyianni, George Georgoulas & Evangelos Dermatas - 2006 - In O. Stock & M. Schaerf, Lecture Notes In Computer Science. Springer Verlag. pp. 568-577.
     
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  37.  8
    B. Jähne, H. Haussecker, and P. Geissler, eds., Handbook of Computer Vision and Applications. 1. Sensors and Imaging. 2. Signal Processing and Pattern Recognition. 3. Systems and Applications. [REVIEW]Azriel Rosenfeld - 2000 - Artificial Intelligence 120 (2):271-273.
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  38.  97
    Individualism and Marr’s Computational Theory of Vision.Keith Butler - 1996 - Mind and Language 11 (4):313-37.
    A great deal of philosophical work has addressed the question of whether Man’s computational theory of early vision is individualistic. Burge and Davies have argued that, according to Marr’s theory, visual states are individuated non-individualistically. Segal has denied that Marr’s theory has these non-individualistic implications. More recently, Shapiro has argued that the entire debate has been misguided. I argue that Shapiro is mistaken in a fairly deep way, attention to which allows us to raise and clarify several important issues (...)
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  39.  72
    Computational biology and the limits of shared vision.Annamaria Carusi - 2011 - Perspectives on Science 19 (3):300-336.
    Since the 1980s, several studies of visual perception have persuasively argued that important aspects of human vision are best accounted for not by recourse to inner mental representations but rather through socially observable actions and behaviors (e.g. Lynch 1985, Latour 1986, Lynch 1990, Goodwin 1994, Goodwin 1997, Sharrock & Coulter 1998). While there are clearly physiological mechanisms required for vision, psychological accounts of perception in terms of inner mental representations have been dislodged from their position as the basic (...)
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  40. Supervenience and computational explanation in vision theory.Peter Morton - 1993 - Philosophy of Science 60 (1):86-99.
    According to Marr's theory of vision, computational processes of early vision rely for their success on certain "natural constraints" in the physical environment. I examine the implications of this feature of Marr's theory for the question whether psychological states supervene on neural states. It is reasonable to hold that Marr's theory is nonindividualistic in that, given the role of natural constraints, distinct computational theories of the same neural processes may be justified in different environments. But to avoid trivializing (...)
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  41.  86
    Unsupervised statistical learning in vision: computational principles, biological evidence.Shimon Edelman - unknown
    Unsupervised statistical learning is the standard setting for the development of the only advanced visual system that is both highly sophisticated and versatile, and extensively studied: that of monkeys and humans. In this extended abstract, we invoke philosophical observations, computational arguments, behavioral data and neurobiological findings to explain why computer vision researchers should care about (1) unsupervised learning, (2) statistical inference, and (3) the visual brain. We then outline a neuromorphic approach to structural primitive learning motivated by these (...)
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  42. Computer Models On Mind: Computational Approaches In Theoretical Psychology.Margaret A. Boden - 1988 - Cambridge University Press.
    What is the mind? How does it work? How does it influence behavior? Some psychologists hope to answer such questions in terms of concepts drawn from computer science and artificial intelligence. They test their theories by modeling mental processes in computers. This book shows how computer models are used to study many psychological phenomena--including vision, language, reasoning, and learning. It also shows that computer modeling involves differing theoretical approaches. Computational psychologists disagree about some basic questions. For (...)
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  43. Content, computation and externalism.Oron Shagrir - 2001 - Mind 110 (438):369-400.
    The paper presents an extended argument for the claim that mental content impacts the computational individuation of a cognitive system (section 2). The argument starts with the observation that a cognitive system may simultaneously implement a variety of different syntactic structures, but that the computational identity of a cognitive system is given by only one of these implemented syntactic structures. It is then asked what are the features that determine which of implemented syntactic structures is the computational structure of the (...)
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  44.  17
    Computer science and information vision of the world from the standpoint of the principle of materialistic monism.Nikolai Andreevich Popov - 2022 - Философия И Культура 2:47-72.
    The subject of this study is the problem of the failure of attempts by the scientific community to come to a common understanding of what exactly information can be as something encoded into material structures and moved along with them. At the same time, the following aspects of this problem are considered in detail: what is the immediate cause of the information problem; what are the objective and subjective prerequisites for its appearance; why the unresolved nature of this problem does (...)
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  45. Marr’s Computational Theory of Vision.Patricia Kitcher - 1988 - Philosophy of Science 55 (March):1-24.
    David Marr's theory of vision has been widely cited by philosophers and psychologists. I have three projects in this paper. First, I try to offer a perspicuous characterization of Marr's theory. Next, I consider the implications of Marr's work for some currently popular philosophies of psychology, specifically, the "hegemony of neurophysiology view", the theories of Jerry Fodor, Daniel Dennett, and Stephen Stich, and the view that perception is permeated by belief. In the last section, I consider what the phenomenon (...)
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  46. Neural Computation of Surface Border Ownership and Relative Surface Depth from Ambiguous Contrast Inputs.Birgitta Dresp-Langley & Stephen Grossberg - 2016 - Frontiers in Psychology 7.
    The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the continuous (...)
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  47.  2
    The Limits of Responsibilization? Responsibility Boundary-Work Through Visions in the Case of Neuromorphic Computing.Philipp Neudert, Mareike Smolka & Stefan Böschen - forthcoming - Minerva:1-28.
    Visions and imaginaries have been longstanding research topics in Science and Technology Studies. Visions of sociotechnical change often ascribe responsibility for achieving the desired change to specific actors. However, there is little research on how visions create, change, and preserve responsibilities in the present. Drawing on Vision Assessment, we present a case-study on visions of neuromorphic computing in NeuroSys, a research and innovation cluster located in the Aachen region in Germany, which develops brain-inspired computing technology, also known as neuromorphic (...)
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  48. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. By David Marr. [REVIEW]Malcolm Acock - 1985 - Modern Schoolman 62 (2):141-142.
  49.  10
    Visions of Computer Science.Erol Gelenbe, Samson Abramsky & Vladimiro Sassone (eds.) - 2008 - British Computer Society.
  50.  21
    A Computational Model of Human Colour Vision for Film Restoration.Alessandro Rizzi, Luca Armellin, Beatrice Sarti & Alice Plutino - 2022 - Gestalt Theory 44 (1-2):175-182.
    Even today, film restoration is a challenge, because it involves multidisciplinary competences: from analogue film inspection and conservation to digitisation and image enhancement. In this context, thanks to the high manageability of digital files, the film restoration workflow often follows a digitisation step, which presents many approximations and issues that are often ignored. In this work, we propose an alternative approach to the issues commonly encountered in film restoration aiming at restoring the original colour appearance, through models of human colour (...)
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