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This book develops the theoretical perspective on visuospatial reasoning in ecocultural contexts, granting insights on how the language, gestures, and representations of different cultures reflect visuospatial reasoning in context.   For a number of years, two themes in the field of mathematics education have run parallel with each other with only a passing acquaintance.  These two areas are the psychological perspective on visuospatial reasoning and ecocultural perspectives on mathematics education.  This volume examines both areas of research and explores the intersection of these powerful ideas.   In addition, there has been a growing interest in sociocultural aspects of education and in particular that of Indigenous education in the field of mathematics education.  There has not, however, been a sound analysis of how environmental and cultural contexts impact visuospatial reasoning, although it was noted as far back as the 1980s when Alan Bishop developed his duality of visual processing and interpreting visual information.  This book provides this analysis and in so doing not only articulates new and worthwhile lines of research, but also uncovers and makes real a variety of useful professional approaches in teaching school mathematics.  With a renewed interest in visuospatial reasoning in the mathematics education community, this volume is extremely timely and adds significantly to current literature on the topic.

Didactics of mathematics --- didactiek --- studeren --- wiskunde --- account management --- lesgeven --- Space perception. --- Mental representation. --- Visualization. --- Cognition and culture. --- Mathematics --- Study and teaching --- Psychological aspects. --- Social aspects. --- Math --- Science --- Culture and cognition --- Cognition --- Culture --- Ethnophilosophy --- Ethnopsychology --- Socialization --- Visualisation --- Imagination --- Visual perception --- Imagery (Psychology) --- Representation, Mental --- Abstraction --- Perception --- Spatial perception --- Spatial behavior --- Figure-ground perception --- Geographical perception

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This pioneering text/reference presents a detailed focus on the use of machine vision techniques in industrial inspection applications. An internationally renowned selection of experts provide insights on a range of inspection tasks, drawn from their cutting-edge work in academia and industry, covering practical issues of vision system integration for real-world applications.  Topics and features:  Presents a comprehensive review of state-of-the-art hardware and software tools for machine vision, and the evolution of algorithms for industrial inspection Includes in-depth descriptions of advanced inspection methodologies and machine vision technologies for specific needs Discusses the latest developments and future trends in imaging and vision techniques for industrial inspection tasks Provides a focus on imaging and vision system integration, implementation, and optimization Describes the pitfalls and barriers to developing successful inspection systems for smooth and efficient manufacturing process Bridging the gap between theoretical knowledge and engineering practice, this indispensable book will appeal to graduate students interested in imaging, machine vision, and industrial inspection. The work also serves as an excellent reference for researchers seeking to develop innovative solutions to tackle practical challenges, and for professional engineers who will benefit from the coverage of applications at both system and component level.

Applied Physics --- Engineering & Applied Sciences --- Computer vision --- Engineering inspection. --- Industrial applications. --- Machine vision --- Vision, Computer --- Inspection, Engineering --- Computer science. --- Image processing. --- Pattern recognition. --- Computer Science. --- Image Processing and Computer Vision. --- Pattern Recognition. --- Quality control --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Computer vision. --- Optical pattern recognition. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Optical data processing. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.

Computer Science. --- Data Mining and Knowledge Discovery. --- Pattern Recognition. --- Computer science. --- Data mining. --- Optical pattern recognition. --- Informatique --- Exploration de données (Informatique) --- Reconnaissance optique des formes (Informatique) --- Engineering & Applied Sciences --- Computer Science --- Computer algorithms. --- Data structures (Computer science) --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Pattern recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Informatics --- Science --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Algorithms --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination

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The book presents a coherent understanding of computational intelligence from the perspective of what is known as "intelligent computing" with high-dimensional parameters. It critically discusses the central issue of high-dimensional neurocomputing, such as quantitative representation of signals, extending the dimensionality of neuron, supervised and unsupervised learning and design of higher order neurons. The strong point of the book is its clarity and ability of the underlying theory to unify our understanding of high-dimensional computing where conventional methods fail. The plenty of application oriented problems are presented for evaluating, monitoring and maintaining the stability of adaptive learning machine. Author has taken care to cover the breadth and depth of the subject, both in the qualitative as well as quantitative way. The book is intended to enlighten the scientific community, ranging from advanced undergraduates to engineers, scientists and seasoned researchers in computational intelligence.

Engineering. --- Computational Intelligence. --- Pattern Recognition. --- Mathematical Models of Cognitive Processes and Neural Networks. --- Biometrics. --- Optical pattern recognition. --- Ingénierie --- Reconnaissance optique des formes (Informatique) --- Engineering & Applied Sciences --- Computer Science --- Pattern recognition. --- Biometrics (Biology). --- Neural networks (Computer science). --- Computational intelligence. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Natural computation --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Construction --- Industrial arts --- Technology --- Statistical methods --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Neural computers. --- Neural net computers --- Neural network computers --- Neurocomputers --- Electronic digital computers --- Neural networks (Computer science) .

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This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED), one of the most flexible graph distance models available. The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: Formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm Describes a reformulation of GED to a quadratic assignment problem Illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem Reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework Examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time Includes appendices listing the datasets employed for the experimental evaluations discussed in the book Researchers and graduate students interested in the field of structural pattern recognition will find this focused work to be an essential reference on the latest developments in GED. Dr. Kaspar Riesen is a university lecturer of computer science in the Institute for Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland.

Electrical Engineering --- Computer Science --- Electrical & Computer Engineering --- Engineering & Applied Sciences --- Pattern recognition systems. --- Computer vision. --- Machine vision --- Vision, Computer --- Pattern classification systems --- Pattern recognition computers --- Computer science. --- Data structures (Computer science). --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Data Structures. --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Pattern perception --- Computer vision --- Optical pattern recognition. --- Data structures (Computer scienc. --- Optical data processing --- Perceptrons --- Visual discrimination --- Data structures (Computer science) --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception