From Humans To Computers Cognition Through Visual Perception

This book considers computer vision to be an integral part of the artificial intelligence system. The core of the book is an analysis of possible approaches to the creation of artificial vision systems, which simulate human visual perception. Much attention is paid to the latest achievements in visual psychology and physiology, the description of the functional and structural organization of the human perception mechanism, the peculiarities of artistic perception and the expression of reality. Computer vision models based on these data are investigated. They include the processes of external data analysis, internal environmental model synthesis, and the generating of behavioristic responses based on external and internal models comparison. Computer vision system evolution resulting from environmental effects is also considered. A unique feature of this book is the authors' use of black and white, and colour prints of traditional and contemporary Russian art to illustrate their principal theses. In doing so, they introduce the reader to a particularly Russian view of the world.

Pattern Recognitions by Humans and Machines, Volume 2: Visual Perceptions covers aspects of research on visual perception. The book discusses visual form perception, figure-ground organization, and the spatial and temporal responses of the visual system; eye movements; and visual pattern perception. The text also describes a computer vision model based on psychophysical experiments; perspectives from brain theory and artificial intelligence; and the capacity to extract shape properties and spatial relations among objects and objects' parts. Knowledge-mediated perception is also considered. Psychologists and people involved in the study of visual perceptions will find the book useful.

This book provides an introduction to human visual perception suitable for readers studying or working in the fields of computer graphics and visualization, cognitive science, and visual neuroscience. It focuses on how computer graphics images are generated, rather than solely on the organization of the visual system itself; therefore, the text pro

Recent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.

This book considers computer vision to be an integral part of the artificial intelligence system. The core of the book is an analysis of possible approaches to the creation of artificial vision systems, which simulate human visual perception. Much attention is paid to the latest achievements in visual psychology and physiology, the description of the functional and structural organization of the human perception mechanism, the peculiarities of artistic perception and the expression of reality. Computer vision models based on these data are investigated. They include the processes of external data analysis, internal environmental model synthesis, and the generating of behavioristic responses based on external and internal models comparison. Computer vision system evolution resulting from environmental effects is also considered. A unique feature of this book is the authors' use of black and white, and colour prints of traditional and contemporary Russian art to illustrate their principal theses. In doing so, they introduce the reader to a particularly Russian view of the world.

The following are the proceedings of the Third International Workshop on Human and Machine Perception held in Pavia, Italy, on September 14 -17, 1998. This edition has been under the auspices of two Institutions: the Cybernetic and Biophysics Group (GNCB) of the Italian National Research Council (CNR), the Centro Interdipartimentale di Scienze Cognitive, of Pavia University and the Centro Interdipartimentale Tecnologie della Conoscenza, of Palermo University. A wide spectrum of topics is covered in this series, ranging from computer perception to psychology and physiology of perception, for analysing and comparing biological and artificial approaches. The theme of this workshop was focused on "Emergence, Attention and Creativity". The workshop structure consisted of five modules each one composed of two invited lectures (dealing with solutions in nature and machines respectively) and a panel discussion. The lectures focused on presenting the state-of-the-art and outlining open questions. In particular, they sought to stress links, suggesting possible synergies between different cultural areas. The panel discussion was conceived as a forum for an open debate, briefly introduced by each panellist, and mainly aimed at deeper investigation for the different approaches and strictly related topics. The panellists were asked to prepare a few statements on hot-points, which were delivered in advance to the participants as a guide for a qualified discussion.

Shimon Ullman focuses on the processes of high-level vision that deal with the interpretation and use of what is seen in the image. In this book, Shimon Ullman focuses on the processes of high-level vision that deal with the interpretation and use of what is seen in the image. In particular, he examines two major problems. The first, object recognition and classification, involves recognizing objects despite large variations in appearance caused by changes in viewing position, illumination, occlusion, and object shape. The second, visual cognition, involves the extraction of shape properties and spatial relations in the course of performing visual tasks such as object manipulation, planning movements in the environment, or interpreting graphical material such as diagrams, graphs and maps. The book first takes up object recognition and develops a novel approach to the recognition of three-dimensional objects. It then studies a number of related issues in high-level vision, including object classification, scene segmentation, and visual cognition. Using computational considerations discussed throughout the book, along with psychophysical and biological data, the final chapter proposes a model for the general flow of information in the visual cortex. Understanding vision is a key problem in the brain sciences, human cognition, and artificial intelligence. Because of the interdisciplinary nature of the theories developed in this work, High-Level Vision will be of interest to readers in all three of these fields.

According to Putnam to talk of “facts” without specifying the language to be used is to talk of nothing; “object” itself has many uses and as we creatively invent new uses of words “we find that we can speak of ‘objects’that were not ‘values of any variable’in 1 any language we previously spoke” . The notion of object becomes, then, like the notion of reference, a sort of open land, an unknown territory. The exploration of this land - pears to be constrained by use and invention. But, we may wonder, is it possible to guide invention and control use? In what way, in particular, is it possible, at the level of na- ral language, to link together program expressions and natural evolution? To give an answer to these onerous questions we should immediately point out that cognition (as well as natural language) has to be considered first of all as a peculiar fu- tion of active biosystems and that it results from complex interactions between the - ganism and its surroundings. “In the moment an organism perceives an object of wh- ever kind, it immediately begins to ‘interpret’this object in order to react properly to it . . . It is not necessary for the monkey to perceive the tree in itself. . . What counts is sur- 2 vival” .