This volume is a post-event proceedings volume and contains selected papers based on the presentations given, and the lively discussions that ensued, during a seminar held in Dagstuhl Castle, Germany, in October 2003. Co-sponsored by ECVision, the cognitive vision network of excellence, it was organized to further strengthen cooperation between research groups from different countries working in the field of cognitive vision systems.
This volume is a post-event proceedings volume and contains selected papers based on the presentations given, and the lively discussions that ensued, during a seminar held in Dagstuhl Castle, Germany, in October 2003. Co-sponsored by ECVision, the cognitive vision network of excellence, it was organized to further strengthen cooperation between research groups from different countries working in the field of cognitive vision systems.
Use of visual information is used to augment our knowledge, decide on our actions, and keep track of our environment. Even with eyes closed, people can remember visual and spatial representations, manipulate them, and make decisions about them. The chapters in Volume 42 of Psychology of Learning and Motivation discuss the ways cognition interacts with visual processes and visual representations, with coverage of figure-ground assignment, spatial and visual working memory, object identification and visual search, spatial navigation, and visual attention.
Learn how to apply cognitive principles to the problems of computer vision Computational Models for Cognitive Vision formulates the computational models for the cognitive principles found in biological vision, and applies those models to computer vision tasks. Such principles include perceptual grouping, attention, visual quality and aesthetics, knowledge-based interpretation and learning, to name a few. The author’s ultimate goal is to provide a framework for creation of a machine vision system with the capability and versatility of the human vision. Written by Dr. Hiranmay Ghosh, the book takes readers through the basic principles and the computational models for cognitive vision, Bayesian reasoning for perception and cognition, and other related topics, before establishing the relationship of cognitive vision with the multi-disciplinary field broadly referred to as “artificial intelligence”. The principles are illustrated with diverse application examples in computer vision, such as computational photography, digital heritage and social robots. The author concludes with suggestions for future research and salient observations about the state of the field of cognitive vision. Other topics covered in the book include: · knowledge representation techniques · evolution of cognitive architectures · deep learning approaches for visual cognition Undergraduate students, graduate students, engineers, and researchers interested in cognitive vision will consider this an indispensable and practical resource in the development and study of computer vision.
The extremely rapid progress of science dealing with the design of new computer systems and the development of intelligent algorithmic solutions for solving c- plex problems has become apparent also in the field of computational intelligence and cognitive informatics methods. The progress of these new branches of inf- matics has only started a few years ago, but they are already making a very s- nificant contribution to the development of modern technologies, and also forming the foundations for future research on building an artificial brain and systems i- tating human thought processes. We are already able to build robots with basic machine intelligence, which can sometimes perform complex actions and also - erate by adapting to changing conditions of their surroundings. This very impr- sive development of intelligent systems is manifested in the creation of robotic devices which use artificial intelligence algorithms in their operations, mo- ments, when solving difficult problems or communicating with humans. It is also evidenced by the introduction of new methods of reasoning about and interpreting objects or events surrounding the system. One of the fields in which the need to deploy such modern solutions is obvious are cognitive vision systems used both in mobile robots and in computer systems which recognise or interpret the meaning of recorded signals or patterns.
Cognitive Computing for Human-Robot Interaction: Principles and Practices explores the efforts that should ultimately enable society to take advantage of the often-heralded potential of robots to provide economical and sustainable computing applications. This book discusses each of these applications, presents working implementations, and combines coherent and original deliberative architecture for human–robot interactions (HRI). Supported by experimental results, it shows how explicit knowledge management promises to be instrumental in building richer and more natural HRI, by pushing for pervasive, human-level semantics within the robot's deliberative system for sustainable computing applications. This book will be of special interest to academics, postgraduate students, and researchers working in the area of artificial intelligence and machine learning. Key features: - Introduces several new contributions to the representation and management of humans in autonomous robotic systems; - Explores the potential of cognitive computing, robots, and HRI to generate a deeper understanding and to provide a better contribution from robots to society; - Engages with the potential repercussions of cognitive computing and HRI in the real world. - Introduces several new contributions to the representation and management of humans in an autonomous robotic system - Explores cognitive computing, robots and HRI, presenting a more in-depth understanding to make robots better for society - Gives a challenging approach to those several repercussions of cognitive computing and HRI in the actual global scenario
Cognitive Systems and the Extended Mind surveys philosophical issues raised by the situated movement in cognitive science, that is, the treatment of cognitive phenomena as the joint products of brain, body, and environment.
Computer Vision is the most important key in developing autonomous navigation systems for interaction with the environment. It also leads us to marvel at the functioning of our own vision system. In this book we have collected the latest applications of vision research from around the world. It contains both the conventional research areas like mobile robot navigation and map building, and more recent applications such as, micro vision, etc.The fist seven chapters contain the newer applications of vision like micro vision, grasping using vision, behavior based perception, inspection of railways and humanitarian demining. The later chapters deal with applications of vision in mobile robot navigation, camera calibration, object detection in vision search, map building, etc.
This book constitutes the refereed proceedings of the Third International Conference on Computer Vision Systems, ICVS 2003, held in Graz, Austria, in April 2003. The 51 revised full papers presented were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on cognitive vision, philosophical issues in cognitive vision, cognitive vision and applications, computer vision architectures, performance evaluation, implementation methods, architecture and classical computer vision, and video annotation.
Why does the world look to us as it does? Generally speaking, this question has received two types of answers in the cognitive sciences in the past fifty or so years. According to the first, the world looks to us the way it does because we construct it to look as it does. According to the second, the world looks as it does primarily because of how the world is. In The Innocent Eye, Nico Orlandi defends a position that aligns with this second, world-centered tradition, but that also respects some of the insights of constructivism. Orlandi develops an embedded understanding of visual processing according to which, while visual percepts are representational states, the states and structures that precede the production of percepts are not representations. If we study the environmental contingencies in which vision occurs, and we properly distinguish functional states and features of the visual apparatus from representational states and features, we obtain an empirically more plausible, world-centered account. Orlandi shows that this account accords well with models of vision in perceptual psychology -- such as Natural Scene Statistics and Bayesian approaches to perception -- and outlines some of the ways in which it differs from recent 'enactive' approaches to vision. The main difference is that, although the embedded account recognizes the importance of movement for perception, it does not appeal to action to uncover the richness of visual stimulation. The upshot is that constructive models of vision ascribe mental representations too liberally, ultimately misunderstanding the notion. Orlandi offers a proposal for what mental representations are that, following insights from Brentano, James and a number of contemporary cognitive scientists, appeals to the notions of de-coupleability and absence to distinguish representations from mere tracking states.
