Invariances in Human Information Processing examines and identifies processing universals and how they are implemented in elementary judgemental processes. This edited collection offers evidence that these universals can be extracted and identified from observing law-like principles in perception, cognition, and action. Addressing memory operations, development, and conceptual learning, this book considers basic and complex meso- and makro-stages of information processing. Chapter authors provide theoretical accounts of cognitive processing that may offer tools for identification of functional components in brain activity in cognitive neuroscience
Invariances in Human Information Processing examines and identifies processing universals and how they are implemented in elementary judgemental processes. This edited collection offers evidence that these universals can be extracted and identified from observing law-like principles in perception, cognition, and action. Addressing memory operations, development, and conceptual learning, this book considers basic and complex meso- and makro-stages of information processing. Chapter authors provide theoretical accounts of cognitive processing that may offer tools for identification of functional components in brain activity in cognitive neuroscience
Invariances in Human Information Processing examines and identifies processing universals and how they are implemented in elementary judgemental processes. This edited collection offers evidence that these universals can be extracted and identified from observing law-like principles in perception, cognition, and action. Addressing memory operations, development, and conceptual learning, this book considers basic and complex meso- and makro-stages of information processing. Chapter authors provide theoretical accounts of cognitive processing that may offer tools for identification of functional components in brain activity in cognitive neuroscience
This book constitutes the proceedings of the 10th Pacific Rim Conference on Multimedia, held in Bangkok, Thailand during December 15-18, 2009. The papers presented in the volume were carefully reviewed and selected from 171 submissions. The topics covered are exploring large-scale videos:automatic content genre classification, repair, enhancement and authentication, human behavior classification and recognition, image and video coding perceptual quality improvement, image annotation, retrieval, and classification, object detection and tracking, networking technologies, audio processing, 3DTV and mulit-view video, image watermarking, multimedia document search and retrieval, intelligent multimedia security and forensics, multimedia content management, image analysis and matching, coding, advanced image processing techniques, multimedia compressioin and optimization, multimedia security rights and management.
Originally published in 1978, the papers in this book derive from a 1976 meeting sponsored by the Social Science Research Council to discuss the nature and principles of category formation. It is organized in three sections: real-world categories, the cognitive processes underlying categorization, and the nature of representation. Part I examines different structural aspects of real-world categories: folk biological taxonomies, within and between category structures for material objects, and some categories in a language that codes the world in a visual–gestural mode. All three chapters in Part I assume category processors who are able to perform at least three cognitive functions: They can judge similarity between stimuli; they can perceive and process the attributes of a stimulus; and they can learn. Part II presents analyses of these three cognitive functions. All discussion of psychological structures and processes lead eventually to the issue of representation, and Part III examines representational assumptions underlying the earlier discussions. Today it can be read and enjoyed in its historical context.
The Psychophysics of Learning presents a learning system design approach that is formulated by the strategies and techniques the brain uses to process external information and make sense of that information to the learning ecology of all learners.
Facilitates a rapprochement between psychology and physics. Brings measurement and mathematics into the study of the mind. This detailed and engaging account fills a deep gap in the history of psychology.
In this two volume festschrift, contributors explore the theoretical developments (Volume I) and applications (Volume II) in traditional cognitive psychology domains, and model other areas of human performance that benefit from rigorous mathematical approaches. It brings together former classmates, students and colleagues of Dr. James T. Townsend, a pioneering researcher in the field since the early 1960s, to provide a current overview of mathematical modeling in psychology. Townsend’s research critically emphasized a need for rigor in the practice of cognitive modeling, and for providing mathematical definition and structure to ill-defined psychological topics. The research captured demonstrates how the interplay of theory and application, bridged by rigorous mathematics, can move cognitive modeling forward.
The second, revised edition of this book was suggested by the impressive sales of the first edition. Fortunately this enabled us to incorporate new important results that had just been obtained. The ASSOM (Adaptive-Subspace SOM) is a new architecture in which invariant-feature detectors emerge in an unsupervised learning process. Its basic principle was already introduced in the first edition, but the motiva tion and theoretical discussion in the second edition is more thorough and consequent. New material has been added to Sect. 5.9 and this section has been rewritten totally. Correspondingly, Sect. 1.4, which deals with adaptive subspace classifiers in general and constitutes the prerequisite for the ASSOM principle, has also been extended and rewritten totally. Another new SOM development is the WEBSOM, a two-layer architecture intended for the organization of very large collections of full-text documents such as those found in the Internet and World Wide Web. This architecture was published after the first edition came out. The idea and results seemed to be so important that the new Sect. 7.8 has now been added to the second edition. Another addition that contains new results is Sect. 3.15, which describes the acceleration in the computing of very large SOMs. It was also felt that Chap. 7, which deals with 80M applications, had to be extended.
While the field of computer vision drives many of today’s digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color in computer vision, this book provides a complete set of tools for working with color in the field of image understanding. Based on the authors’ intense collaboration for more than a decade and drawing on the latest thinking in the field of computer science, the book integrates topics from color science and computer vision, clearly linking theories, techniques, machine learning, and applications. The fundamental basics, sample applications, and downloadable versions of the software and data sets are also included. Clear, thorough, and practical, Color in Computer Vision explains: Computer vision, including color-driven algorithms and quantitative results of various state-of-the-art methods Color science topics such as color systems, color reflection mechanisms, color invariance, and color constancy Digital image processing, including edge detection, feature extraction, image segmentation, and image transformations Signal processing techniques for the development of both image processing and machine learning Robotics and artificial intelligence, including such topics as supervised learning and classifiers for object and scene categorization Researchers and professionals in computer science, computer vision, color science, electrical engineering, and signal processing will learn how to implement color in computer vision applications and gain insight into future developments in this dynamic and expanding field.
