Integrating Visual System Mechanisms Computational Models And Algorithms Technologies

Although there has been much progress in developing theories, models and systems in the areas of Natural Language Processing (NLP) and Vision Processing (VP) there has heretofore been little progress on integrating these subareas of Artificial Intelligence (AI). This book contains a set of edited papers addressing computational models and systems for the integration of NLP and VP. The papers focus on site descriptions such as that of the large Japanese $500 million Real World Computing (RWC) project, on historical philosophical issues, on systems which have been built and which integrate the processing of visual scenes together with language about them, and on spatial relations which appear to be the key to integration. The U.S.A., Japan and the EU are well reflected, showing up the fact that integration is a truly international issue. There is no doubt that all of this will be necessary for the InformationSuperHighways of the future.

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

This is an invaluable five-volume reference on the very broad and highly significant subject of computer aided and integrated manufacturing systems. It is a set of distinctly titled and well-harmonized volumes by leading experts on the international scene.The techniques and technologies used in computer aided and integrated manufacturing systems have produced, and will no doubt continue to produce, major annual improvements in productivity, which is defined as the goods and services produced from each hour of work. This publication deals particularly with more effective utilization of labor and capital, especially information technology systems. Together the five volumes treat comprehensively the major techniques and technologies that are involved.

This book contains a key component of the NII 2000 project of the Computer Science and Telecommunications Board, a set of white papers that contributed to and complements the project's final report, The Unpredictable Certainty: Information Infrastructure Through 2000, which was published in the spring of 1996. That report was disseminated widely and was well received by its sponsors and a variety of audiences in government, industry, and academia. Constraints on staff time and availability delayed the publication of these white papers, which offer details on a number of issues and positions relating to the deployment of information infrastructure.

Digital technologies play a significant role in the popular imagination about the future of education, as they are a prominent aspect of modern education provision and practice across the globe. Due to the increased adoption of digital education materials during the COVID-19 pandemic, the pedagogical significance of digital technology has been amplified. Advancing Equity and Inclusion Through Educational Technology builds upon the available literature in equity and inclusion through educational technology while providing further research opportunities in this dynamic and growing field. It provides the opportunity for reflection on this crucial issue by increasing the understanding of the importance of inclusion and equity in the context of educational improvements and providing relevant academic work, empirical research findings, and an overview of this relevant field of study. Covering topics such as sustainable inclusion learning, virtual school press programs, and generic skills, this major reference work is a comprehensive and timely resource for educators and administrators of both K-12 and higher education, government officials, pre-service teachers, teacher educators, librarians, researchers, and academicians.

This research monograph describes a large programming project in which an underwater organism, capable of perceiving, learning, deciding, and navigating, is computationally simulated. The developed computational model serves as a contemporary theory of perceptual-motor performance, embodying much of what is known about human vision and some of what is known about other cognitive processes. This artificial intelligence project has substantial contributions to make to the development of autonomous underwater vehicles. It also makes a specific theoretical statement about the organization and nature of organic perceptual motor systems that may be useful to psychologists, neuroscientists, and theoreticians in a number of other fields.

Models of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavily pre-processed and regulated information streams to provide learning algorithms with appropriate, well timed, and meaningful data to match the assumptions of learning rules. On the contrary, natural scenarios are often rich of raw, asynchronous, overlapping and uncertain inputs and outputs whose relationships and meaning are progressively acquired, disambiguated, and used for further learning. Therefore, recent research efforts focus on neural embodied systems that rely less on well timed and pre-processed inputs, but rather extract autonomously relationships and features in time and space. In particular, realistic and more complete models of plasticity must account for delayed rewards, noisy and ambiguous data, emerging and novel input features during online learning. Such approaches model the progressive acquisition of knowledge into neural systems through experience in environments that may be affected by ambiguities, uncertain signals, delays, or novel features.