This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Attention and Performance in Computational Vision, WAPCV 2004, held in Prague, Czech Republic in May 2004. The 16 revised full papers presented together with an invited paper were carefully selected during two rounds of reviewing and improvement. The papers are organized in topical sections on attention in object and scene recognition, architectures for sequential attention, biologically plausible models for attention, and applications of attentive vision.

This monograph presents a complete computational system for visual attention and object detection. VOCUS (Visual Object detection with a Computational attention System) represents a major step forward on integrating data-driven and model-driven information into a single framework. Additionally, the volume contains an extensive review of the literature on visual attention, detailed evaluations of VOCUS in different settings, and applications of the system.

The derivation, exposition, and justification of the Selective Tuning model of vision and attention. Although William James declared in 1890, "Everyone knows what attention is," today there are many different and sometimes opposing views on the subject. This fragmented theoretical landscape may be because most of the theories and models of attention offer explanations in natural language or in a pictorial manner rather than providing a quantitative and unambiguous statement of the theory. They focus on the manifestations of attention instead of its rationale. In this book, John Tsotsos develops a formal model of visual attention with the goal of providing a theoretical explanation for why humans (and animals) must have the capacity to attend. He takes a unique approach to the theory, using the full breadth of the language of computation—rather than simply the language of mathematics—as the formal means of description. The result, the Selective Tuning model of vision and attention, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. The overarching conclusion is that human vision is based on a general purpose processor that can be dynamically tuned to the task and the scene viewed on a moment-by-moment basis. Tsotsos offers a comprehensive, up-to-date overview of attention theories and models and a full description of the Selective Tuning model, confining the formal elements to two chapters and two appendixes. The text is accompanied by more than 100 illustrations in black and white and color; additional color illustrations and movies are available on the book's Web site.

Visual attention is a relatively new area of study combining a number of disciplines: artificial neural networks, artificial intelligence, vision science and psychology. The aim is to build computational models similar to human vision in order to solve tough problems for many potential applications including object recognition, unmanned vehicle navigation, and image and video coding and processing. In this book, the authors provide an up to date and highly applied introduction to the topic of visual attention, aiding researchers in creating powerful computer vision systems. Areas covered include the significance of vision research, psychology and computer vision, existing computational visual attention models, and the authors' contributions on visual attention models, and applications in various image and video processing tasks. This book is geared for graduates students and researchers in neural networks, image processing, machine learning, computer vision, and other areas of biologically inspired model building and applications. The book can also be used by practicing engineers looking for techniques involving the application of image coding, video processing, machine vision and brain-like robots to real-world systems. Other students and researchers with interdisciplinary interests will also find this book appealing. Provides a key knowledge boost to developers of image processing applications Is unique in emphasizing the practical utility of attention mechanisms Includes a number of real-world examples that readers can implement in their own work: robot navigation and object selection image and video quality assessment image and video coding Provides codes for users to apply in practical attentional models and mechanisms

The derivation, exposition, and justification of the Selective Tuning model of vision and attention.

Vision is the most important of the five human senses, since it provides over 85% of the information our brain receives from the external world. Its main goal is to interpret and to interact with the environments we are living in. In everyday life, humans are capable of perceiving thousands of objects, identifying hundreds of faces, recognizing numerous traffic signs, and appreciating beauty effortlessly. The ease with which humans achieve these tasks is in no way due to the simplicity of the tasks but is a proof of the high degree of development of our vision system.

Robotics and autonomous systems can aid disabled individuals in daily living or make a workplace more productive, but these tools are only as effective as the technology behind them. Robotic systems must be able to accurately identify and act upon elements in their environment to be effective in performing their duties. Innovative Research in Attention Modeling and Computer Vision Applications explores the latest research in image processing and pattern recognition for use in robotic real-time cryptography and surveillance applications. This book provides researchers, students, academicians, software designers, and application developers with next-generation insight into the use of computer vision technologies in a variety of industries and endeavors. This premier reference work includes chapters on topics ranging from biometric and facial recognition technologies, to digital image and video watermarking, among many others.