This both accessible and exhaustive book will help to improve modeling of attention and to inspire innovations in industry. It introduces the study of attention and focuses on attention modeling, addressing such themes as saliency models, signal detection and different types of signals, as well as real-life applications. The book is truly multi-disciplinary, collating work from psychology, neuroscience, engineering and computer science, amongst other disciplines. What is attention? We all pay attention every single moment of our lives. Attention is how the brain selects and prioritizes information. The study of attention has become incredibly complex and divided: this timely volume assists the reader by drawing together work on the computational aspects of attention from across the disciplines. Those working in the field as engineers will benefit from this book’s introduction to the psychological and biological approaches to attention, and neuroscientists can learn about engineering work on attention. The work features practical reviews and chapters that are quick and easy to read, as well as chapters which present deeper, more complex knowledge. Everyone whose work relates to human perception, to image, audio and video processing will find something of value in this book, from students to researchers and those in industry.

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

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

A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.

This book presents state-of-the-art computational attention models that have been successfully tested in diverse application areas and can build the foundation for artificial systems to efficiently explore, analyze, and understand natural scenes. It gives a comprehensive overview of the most recent computational attention models for processing visual and acoustic input. It covers the biological background of visual and auditory attention, as well as bottom-up and top-down attentional mechanisms and discusses various applications. In the first part new approaches for bottom-up visual and acoustic saliency models are presented and applied to the task of audio-visual scene exploration of a robot. In the second part the influence of top-down cues for attention modeling is investigated.

Consciously or unconsciously, humans always pay attention to a wide variety of stimuli. Attention is part of daily life and it is the first step to understanding. The proposed thesis deals with a computational approach to the human attentional mechanism and with its possible applications mainly in the field of computer vision. In a first stage, the text introduces a rarity-based three-level attention model handling monodimensional signals as well as images or video sequences. The concept of attention is defined as the transformation of a huge acquired unstructured data set into a smaller structured one while preserving the information: the attentional mechanism turns rough data into intelligence. Afterwards, several applications are described in the fields of machine vision, signal coding and enhancement, medical imaging, event detection and so on. These applications not only show the applicability of the proposed computational attention method, but they also support the idea that similarly to the fact that attention is the beginning of intelligence in humans, computational attention may be the starting point of artificial intelligence in engineering applications. Several databases containing different kinds of signals were used to test the model and its applications: audio signals of natural complex ambiences and events, real-life video sequences as well as simulated sequences and finally natural scenes, textured or synthetic images. Results are presented in a clear and comprehensive way within each application providing the relevance of the use of the computational attention model. Finally, a large discussion is opened based on the theoretical and practical achievements and future extensions are proposed.

Attention has represented a core scienti?c topic in the design of AI-enabled systems in the last few decades. Today, in the ongoing debate, design, and c- putationalmodelingofarti?cialcognitivesystems,attentionhasgainedacentral position as a focus of research. For instance, attentional methods are considered in investigating the interfacing of sensory and cognitive information processing, for the organization of behaviors, and for the understanding of individual and social cognition in infant development. Whilevisualcognitionplaysacentralroleinhumanperception,?ndingsfrom neuroscience and experimental psychology have provided strong evidence about the perception–action nature of cognition. The embodied nature of senso- motor intelligence requires a continuous and focused interplay between the c- trolofmotoractivitiesandtheinterpretationoffeedbackfromperceptualmod- ities. Decision making about the selection of information from the incoming sensory stream – in tune with contextual processing on a current task and an agent’s global objectives – becomes a further challenging issue in attentional control. Attention must operate at interfaces between a bottom-up-driven world interpretationandtop-down-driveninformationselection,thusactingatthecore of arti?cial cognitive systems. These insights have already induced changes in AI-related disciplines, such as the design of behavior-based robot control and the computational modeling of animats. Today, the development of enabling technologiessuch as autonomous robotic systems,miniaturizedmobile–evenwearable–sensors,andambientintelligence systems involves the real-time analysis of enormous quantities of data. These data have to be processed in an intelligent way to provide “on time delivery” of the required relevant information. Knowledge has to be applied about what needs to be attended to, and when, and what to do in a meaningful sequence, in correspondence with visual feedback.