This book presents the results of research into one of the most complex and difficult areas - research into thinking and understanding. The research was carried out at the newly founded Queen Jadwiga Research Institute of Understanding and is focused on the problem of visual understanding and visual thinking. The authors believe this is the first book to attempt to investigate the complexity of visual thinking problems in the context of building the thinking machine.

This book presents the selected results of research on the further development of the shape understanding system (SUS) described in our previous book titled “Shape Understanding System: the First Steps Toward the Visual Thinking Machines”. This is the second book that presents the results of research in the area of thinking and understanding carried out by authors in the newly founded the Queen Jadwiga Research Institute of Understanding. In this book, the new term knowledge implementation is introduced to denote the new method of the meaningful learning in the context of machine understanding. SUS ability to understand is related to the different categories of objects such as the category of visual objects, the category of sensory objects and the category of text objects. In this book, new terms and concepts are introduced in order to describe and explain some issues connected with SUS development. These terms are explained by referring to the content of our books and other our works rather than to existing literature in related areas of research. This book raises many questions that are discussed in the area of cognitive science or philosophy of mind.

Our book presents a unique and original viewpoint on natural and engineered systems. The authors' goal is to propose and explain core principles that govern the formation and function of simple and complex systems. Examples are drawn from a broad range of topics from common materials and manufactured structures to the behavior of cells, organisms and socio-economic organizations. We provide a technical discussion of key engineering principles without the use of mathematics so that we may describe for a general audience how the systems of daily life form, operate, and evolve. We use analogy and illustrations to show how the components self-organize and scale to form complex adaptive systems. In this way we hope to understand how those systems come to be, achieve stability, and suddenly transition to new equilibrium states, including the sudden onset of economic recessions, ecosystem collapse, the evolution of species, development of cancer, and other wide-ranging topics. The existential role of component variability in these processes is emphasized.This book targets engineering instructors and undergraduate students curious to explore the grand challenges facing society today so they might build productive and long-lasting careers in science and technology. The six essays can be used to frame classroom discussions on systems from a broad range of disciplines. The essays are designed to appeal to those with a basic science and engineering background as we illustrate many fundamental engineering concepts in our descriptions of system behavior. We also hope our book appeals to curious members of the general public who are interested in understanding foundational ideas.

This unique book discusses machine understanding (MU). This new branch of classic machine perception research focuses on perception that leads to understanding and is based on the categories of sensory objects. In this approach the visual and non-visual knowledge, in the form of visual and non-visual concepts, is used in the complex reasoning process that leads to understanding. The book presents selected new concepts, such as perceptual transformations, within the machine understanding framework, and uses perceptual transformations to solve perceptual problems (visual intelligence tests) during understanding, where understanding is regarded as an ability to solve complex visual problems described in the authors’ previous books. Thanks to the uniqueness of the research topics covered, the book appeals to researchers from a wide range of disciplines, especially computer science, cognitive science and philosophy.

This book is the outcome of a NATO Advanced Study Institute on Pattern Recog nition Theory and Applications held in Spa-Balmoral, Belgium, in June 1986. This Institute was the third of a series which started in 1975 in Bandol, France, at the initia tive of Professors K. S. Fu and A. Whinston, and continued in 1981 in Oxford, UK, with Professors K. S. Fu, J. Kittler and L. -F. Pau as directors. As early as in 1981, plans were made to pursue the series in about 1986 and possibly in Belgium, with Professor K. S. Fu and the present editors as directors. Unfortunately, Ie sort en decida autrement: Professor Fu passed away in the spring of 1985. His sudden death was an irreparable loss to the scientific community and to all those who knew him as an inspiring colleague, a teacher or a dear friend. Soon after, Josef Kittler and I decided to pay a small tribute to his memory by helping some of his plans to materialize. With the support of the NATO Scientific Affairs Division, the Institute became a reality. It was therefore but natural that the proceedings of the Institute be dedicated to him. The book contains most of the papers that were presented at the Institute. Papers are grouped along major themes which hopefully represent the major areas of contem porary research. These are: 1. Statistical methods and clustering techniques 2. Probabilistic relaxation techniques 3. From Markovian to connectionist models 4.

This book constitutes the thoroughly refereed proceedings of the 17th International Conference on Discrete Geometry for Computer Imagery, DGCI 2013, held in Seville, Spain, in March 2013. The 34 revised full papers presented were carefully selected from 56 submissions and focus on geometric transforms, discrete and combinatorial tools for image segmentation and analysis, discrete and combinatorial topology, discrete shape representation, recognition and analysis, models for discrete geometry, morphological analysis and discrete tomography.

In a highly authoritative and systematic manner, this book offers an in-depth treatment of the essence of the case–based reasoning strategy and case-based design dwelling upon the algorithmic facet of the paradigm. It provides an excellent applied research framework by showing how this development can be effectively utilized in the real word complicated environment of process engineering, a pursuit that is rarely reported in the literature in such a comprehensive manner.

Written by world-renowned authors, this unique compendium presents the most updated progress in pattern recognition and computer vision (PRCV), fully reflecting the strong international research interests in the artificial intelligence arena.Machine learning has been the key to current developments in PRCV. This useful comprehensive volume complements the previous five editions of the book. It places great emphasis on the use of deep learning in many aspects of PRCV applications, not readily available in other reference text.