This textbook offers a tutorial introduction to robotics and Computer Vision which is light and easy to absorb. The practice of robotic vision involves the application of computational algorithms to data. Over the fairly recent history of the fields of robotics and computer vision a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field - What is the right algorithm for a particular problem?, and importantly: How can I try it out without spending days coding and debugging it from the original research papers? The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used -instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals light and color, camera modelling, image processing, feature extraction and multi-view geometry, and bring it all together in a visual servo system. "An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished Oussama Khatib, Stanford

Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.

All biological systems with vision move about their environments and successfully perform many tasks. The same capabilities are needed in the world of robots. To that end, recent results in empirical fields that study insects and primates, as well as in theoretical and applied disciplines that design robots, have uncovered a number of the principles of navigation. To offer a unifying approach to the situation, this book brings together ideas from zoology, psychology, neurobiology, mathematics, geometry, computer science, and engineering. It contains theoretical developments that will be essential in future research on the topic -- especially new representations of space with less complexity than Euclidean representations possess. These representations allow biological and artificial systems to compute from images in order to successfully deal with their environments.
In this book, the barriers between different disciplines have been smoothed and the workings of vision systems of biological organisms are made clear in computational terms to computer scientists and engineers. At the same time, fundamental principles arising from computational considerations are made clear both to empirical scientists and engineers. Empiricists can generate a number of hypotheses that they could then study through various experiments. Engineers can gain insight for designing robotic systems that perceive aspects of their environment.
For the first time, readers will find:
* the insect vision system presented in a way that can be understood by computational scientists working in computer vision and engineering;
* three complete, working robotic navigation systems presented with all the issues related to their design analyzed in detail;
* the beginning of a computational theory of direct perception, as advocated by Gibson, presented in detail with applications for a variety of problems; and
* the idea that vision systems could compute space representations different from perfect metric descriptions -- and be used in robotic tasks -- advanced for both artificial and biological systems.

Face analysis is essential for a large number of applications such as human-computer interaction or multimedia (e.g. content indexing and retrieval). Although many approaches are under investigation, performance under uncontrolled conditions is still not satisfactory. The variations that impact facial appearance (e.g. pose, expression, illumination, occlusion, motion blur) make it a difficult problem to solve. This book describes the progress towards this goal, from a core building block - landmark detection - to the higher level of micro and macro expression recognition. Specifically, the book addresses the modeling of temporal information to coincide with the dynamic nature of the face. It also includes a benchmark of recent solutions along with details about the acquisition of a dataset for such tasks.

This book defines the emerging field of Active Perception which calls for studying perception coupled with action. It is devoted to technical problems related to the design and analysis of intelligent systems possessing perception such as the existing biological organisms and the "seeing" machines of the future. Since the appearance of the first technical results on active vision, researchers began to realize that perception -- and intelligence in general -- is not transcendental and disembodied. It is becoming clear that in the effort to build intelligent visual systems, consideration must be given to the fact that perception is intimately related to the physiology of the perceiver and the tasks that it performs. This viewpoint -- known as Purposive, Qualitative, or Animate Vision -- is the natural evolution of the principles of Active Vision. The seven chapters in this volume present various aspects of active perception, ranging from general principles and methodological matters to technical issues related to navigation, manipulation, recognition, learning, planning, reasoning, and topics related to the neurophysiology of intelligent systems.

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

Phishing Detection Using Content-Based Image Classification is an invaluable resource for any deep learning and cybersecurity professional and scholar trying to solve various cybersecurity tasks using new age technologies like Deep Learning and Computer Vision. With various rule-based phishing detection techniques at play which can be bypassed by phishers, this book provides a step-by-step approach to solve this problem using Computer Vision and Deep Learning techniques with significant accuracy. The book offers comprehensive coverage of the most essential topics, including: Programmatically reading and manipulating image data Extracting relevant features from images Building statistical models using image features Using state-of-the-art Deep Learning models for feature extraction Build a robust phishing detection tool even with less data Dimensionality reduction techniques Class imbalance treatment Feature Fusion techniques Building performance metrics for multi-class classification task Another unique aspect of this book is it comes with a completely reproducible code base developed by the author and shared via python notebooks for quick launch and running capabilities. They can be leveraged for further enhancing the provided models using new advancement in the field of computer vision and more advanced algorithms.

1. This book focuses on providing a detailed description of the utilization of IoT with computer vision and its underlying technologies in critical application areas, such as smart grids, emergency departments, intelligent traffic cams, insurance, and the automotive industry. It provides a detailed description to the readers with practical ideas of using IoT with computer vision (motion based object data) to deal with human dynamics, challenges involved in surpassing diversified architecture, communications, integrity, and security aspects 2. Over the last few years, Internet of Things (IoT) and computer vision, have become challenging research areas where everyday motion based objects can be equipped with identifying, sensing, networking (wired or wireless), and processing the capabilities that allow communication among themselves and with other devices over Internet Protocol. Hence there will be a demand for the book in the market. 3. This book provides an overview of basic concept from rising of machines and communication to IoT with computer vision, critical applications domains, technologies, medical imaging and solutions to handle relevant challenges. The book will provide a detailed description to the readers with practical ideas of using IoT with computer vision (motion based object data) to deals with human dynamics, and challenges unlike the competition in the market.

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

1) Learn how to develop computer vision application algorithms 2) Learn to use software tools for analysis and development 3) Learn underlying processes need for image analysis 4) Learn concepts so that the reader can develop their own algorithms 5) Software tools provided

This is a study of the use of neural networks for machine vision. It is part of a series which reviews research in natural and synthetic neural networks, as well as in modelling, analysis, design, and development of neural networks in software and hardware areas. Contributions from researchers and practitioners serve as a platform for discussion of topics of interest to the neural network and cognitive information processing communities.

Presents a strategic perspective and design methodology that guide the process of developing digital products and services that provide 'real experience' to users. Only when the material experienced runs its course to fulfilment is it then regarded as 'real experience' that is distinctively senseful, evaluated as valuable, and harmoniously related to others. Based on the theoretical background of human experience, the book focuses on these three questions: How can we understand the current dominant designs of digital products and services? What are the user experience factors that are critical to provide the real experience? What are the important HCI design elements that can effectively support the various UX factors that are critical to real experience? Design for Experience is intended for people who are interested in the experiences behind the way we use our products and services, for example designers and students interested in interaction, visual graphics and information design or practitioners and entrepreneurs in pursuit of new products or service-based start-ups.

As Nixon's unpopularity increased during Watergate, his nose and jowls grew to impossible proportions in published caricatures. Yet the caricatures remained instantly recognizable. Caricatures can even be superportraits, with the paradoxical quality of being more like the face than the face itself. How can we recognize such distorted images? Do caricatures derive their power from some special property of a face recognition system or from some more general property of recognition systems? What kind of mental representations and recognition processes make caricatures so effective? What can the power of caricatures tell us about recognition? In seeking to answer these questions, the author assembles clues from a variety of sources: the invention and development of caricatures by artists, the exploitation of extreme signals in animal communication systems, and studies of how humans, other animals and connectionist recognition systems respond to caricatures. Several conclusions emerge. The power of caricatures is ubiquitous. Caricatures can be superportraits for humans, other animals and computer recognition systems. They are effective for a variety of stimuli, not just faces. They are effective whether objects are mentally represented as deviations from a norm or average member of the class, or as absolute feature values on a set of dimensions. Exaggeration of crucial norm-deviation features, distinctiveness, and resemblance to caricatured memory traces are all potential sources of the power of caricature. Superportraits will be of interest to students of cognitive psychology, perception, the visual arts and animal behavior.

Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.

Understanding Geometric Algebra: Hamilton, Grassmann, and Clifford for Computer Vision and Graphics introduces geometric algebra with an emphasis on the background mathematics of Hamilton, Grassmann, and Clifford. It shows how to describe and compute geometry for 3D modeling applications in computer graphics and computer vision. Unlike similar texts, this book first gives separate descriptions of the various algebras and then explains how they are combined to define the field of geometric algebra. It starts with 3D Euclidean geometry along with discussions as to how the descriptions of geometry could be altered if using a non-orthogonal (oblique) coordinate system. The text focuses on Hamilton's quaternion algebra, Grassmann's outer product algebra, and Clifford algebra that underlies the mathematical structure of geometric algebra. It also presents points and lines in 3D as objects in 4D in the projective geometry framework; explores conformal geometry in 5D, which is the main ingredient of geometric algebra; and delves into the mathematical analysis of camera imaging geometry involving circles and spheres. With useful historical notes and exercises, this book gives readers insight into the mathematical theories behind complicated geometric computations. It helps readers understand the foundation of today's geometric algebra.

In recent years, there has been a growing interest in the fields of pattern recognition and machine vision in academia and industries. New theories have been developed, with new design of technology and systems in both hardware and software. They are widely applied to our daily life to solve real problems in such diverse areas as science, engineering, agriculture, e-commerce, education, robotics, government, medicine, games and animation, medical imaging analysis and diagnosis, military, and national security. The foundation of all this field can be traced back to the late Prof. King-Sun Fu, one of the founding fathers of pattern recognition, who, with visionary insight founded the International Association for Pattern Recognition around 1980. In the almost 30 years since then, the world has witnessed the rapid growth and development of this field. It is probably true to say that most people are affected by, or use applications of pattern recognition in daily life. Today, on the eve of 25th anniversary of the unfortunate and untimely passing of Prof. Fu, we are proud to produce this volume of collected works from world renowned professionals and experts in pattern recognition and machine vision, in honor and memory of the late Prof. King-Sun Fu. We hope this book will help promote further the course, not only of fundamental principles, systems and technologies, but also its vast range of applications to help in solving problems in daily life. Contents Basic Foundations of Pattern Recognition and Artificial Intelligence, Methodologies of Machine Vision and Image Processing, Intelligent Pattern Recognition Systems, 3-D Object Pattern Analysis, Modelling and Simulation, Analysis of DNA Microarray Gene Expression Data based on Pattern Recognition Methods, PRMV Applications.

Image algebra is a comprehensive, unifying theory of image transformations, image analysis, and image understanding. In 1996, the bestselling first edition of the Handbook of Computer Vision Algorithms in Image Algebra introduced engineers, scientists, and students to this powerful tool, its basic concepts, and its use in the concise representation of computer vision algorithms.

Updated to reflect recent developments and advances, the second edition continues to provide an outstanding introduction to image algebra. It describes more than 80 fundamental computer vision techniques and introduces the portable iaC++ library, which supports image algebra programming in the C++ language. Revisions to the first edition include a new chapter on geometric manipulation and spatial transformation, several additional algorithms, and the addition of exercises to each chapter.

The authors-both instrumental in the groundbreaking development of image algebra-introduce each technique with a brief discussion of its purpose and methodology, then provide its precise mathematical formulation. In addition to furnishing the simple yet powerful utility of image algebra, the Handbook of Computer Vision Algorithms in Image Algebra supplies the core of knowledge all computer vision practitioners need. It offers a more practical, less esoteric presentation than those found in research publications that will soon earn it a prime location on your reference shelf.

This eagerly anticipated, revised and updated reference discusses applications of machine vision technology in the semiconductor, electronic, automotive, wood, food, pharmaceutical, printing, and container industries-describing systems that enable projects to move forward swiftly and efficiently. Minimize the risk and avoid the pitfalls associated with working for the first time in an area of new technology Focusing on the nuances of the engineering and system integration of machine vision technology, the Second Edition of Understanding and Applying Machine Vision considers three-dimensional and color machine vision techniques offers the means to perform a back-of-the-envelope estimate to determine the feasibility of a specific application outlines the step-by-step introduction of machine vision into a factory details how to integrate machine vision systems into production processes reviews the algorithms available in commercial machine vision systems provides methodology to evaluate machine vision system vendors explains how to conduct buy-off procedures reviews the underlying principles of image processing and analysis and more Surveying the history of machine vision and including recent developments in the field in the 10 years since publication of the first edition, the Second Edition of Understanding and Applying Machine Vision is an excellent reference for manufacturing, production, quality, industrial, electrical, mechanical, packaging, process, control, automotive, plant, plastics, methods, automation, robotics, and optical engineers and managers; application engineers and trainees at merchant machine vision companies and machine vision system integrators; and upper-level undergraduate and graduate students in these disciplines.

Contents:
Introduction. The Nature of Caricature. Caricatures by Computer. Peacock's Tails and other Natural Caricatures. The Power of Extremes. The Psychology of Caricatures. Caricatures and Face Recognition. The View from Here.

Provides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring This book is the first to fill the gap between scientific research of computer vision and its practical applications for structural health monitoring (SHM). It provides a complete, state-of-the-art review of the collective experience that the SHM community has gained in recent years. It also extensively explores the potentials of the vision sensor as a fast and cost-effective tool for solving SHM problems based on both time and frequency domain analytics, broadening the application of emerging computer vision sensor technology in not only scientific research but also engineering practice. Computer Vision for Structural Dynamics and Health Monitoring presents fundamental knowledge, important issues, and practical techniques critical to successful development of vision-based sensors in detail, including robustness of template matching techniques for tracking targets; coordinate conversion methods for determining calibration factors to convert image pixel displacements to physical displacements; sensing by tracking artificial targets vs. natural targets; measurements in real time vs. by post-processing; and field measurement error sources and mitigation methods. The book also features a wide range of tests conducted in both controlled laboratory and complex field environments in order to evaluate the sensor accuracy and demonstrate the unique features and merits of computer vision-based structural displacement measurement. Offers comprehensive understanding of the principles and applications of computer vision for structural dynamics and health monitoring Helps broaden the application of the emerging computer vision sensor technology from scientific research to engineering practice such as field condition assessment of civil engineering structures and infrastructure systems Includes a wide range of laboratory and field testing examples, as well as practical techniques for field application Provides MATLAB code for most of the issues discussed including that of image processing, structural dynamics, and SHM applications Computer Vision for Structural Dynamics and Health Monitoring is ideal for graduate students, researchers, and practicing engineers who are interested in learning about this emerging sensor technology and advancing their applications in SHM and other engineering problems. It will also benefit those in civil and aerospace engineering, energy, and computer science.

This work examines a broad spectrum of the latest topics in visual science, relating basic studies to applications and delineating points of intersection among the various disciplines that study the mechanisms of vision. It discusses, among other topics: the Purkinje-image eyetracker; the principles of high-definition television; and the role of stabilized-image technology in revealing how eye movements control both luminous and chromatic perceptions.

The concept of visual search embraces a wide range of processing activities, from human cognitive phenomana to applied problems for both human and machine vision in industrial, medical and military environments. This book, the second to be derived from the series of internationl conferences on visual search organized under the auspices of the Applied Vision Association, brings together research from a variety of disciplines, enabling the reader to share experiences at the cutting edge, accessing knowledge which might otherwise be locked away in specialist journals or grey literature.

This is volume 1 of the two-volume set Soft Computing and Its Applications. This volume explains the primary tools of soft computing as well as provides an abundance of working examples and detailed design studies. The book starts with coverage of fuzzy sets and fuzzy logic and their various approaches to fuzzy reasoning. Precisely speaking, this book provides a platform for handling different kinds of uncertainties of real-life problems. It introduces the reader to the topic of rough sets. This book s companion volume, "Volume 2: Fuzzy Reasoning and Fuzzy Control," will move forward from here to discuss several advanced features of soft computing and application methodologies.

This new book:

Discusses the present state of art of soft computing

Includes the existing application areas of soft computing

Presents original research contributions

Discusses the future scope of work in soft computing

The book is unique in that it bridges the gap between theory and practice, and it presents several experimental results on synthetic data and real-life data. The book provides a unified platform for applied scientists and engineers in different fields and industries for the application of soft computing tools in many diverse domains of engineering. "

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28-November 2, 2018.

OBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems. Audience Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.