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Forthcoming from the MIT Press
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: * Evaluation and Generalization in Interpretable Machine Learning * Explanation Methods in Deep Learning * Learning Functional Causal Models with Generative Neural Networks * Learning Interpreatable Rules for Multi-Label Classification * Structuring Neural Networks for More Explainable Predictions * Generating Post Hoc Rationales of Deep Visual Classification Decisions * Ensembling Visual Explanations * Explainable Deep Driving by Visualizing Causal Attention * Interdisciplinary Perspective on Algorithmic Job Candidate Search * Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions * Inherent Explainability Pattern Theory-based Video Event Interpretations
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it's possible to teach a machine to excel at human endeavors-such as drawing, composing music, and completing tasks-by generating an understanding of how its actions affect its environment. With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You'll also learn how to apply the techniques to your own datasets. David Foster, cofounder of Applied Data Science, demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to the most cutting-edge algorithms in the field. Through tips and tricks, you'll learn how to make your models learn more efficiently and become more creative. Get a fundamental overview of deep learning Learn about libraries such as Keras and TensorFlow Discover how variational autoencoders work Get practical examples of generative adversarial networks (GANs) Understand how autoregressive generative models function Apply generative models within a reinforcement learning setting to accomplish tasks
"Machine Vision Algorithms and Applications" is the first
up-to-date textbook for machine vision software provides all the
details on the theory and practical use of the relevant algorithms.
Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques
Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book Supplies supplementary course material for students at the associated website, http: //szeliski.org/Book/
Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision."
For both students and engineers in R&D, this book explains machine vision in a concise, hands-on way, using the Vision Development Module of the LabView software by National Instruments. Following a short introduction to the basics of machine vision and the technical procedures of image acquisition, the book goes on to guide readers in the use of the various software functions of LabView's machine vision module. It covers typical machine vision tasks, including particle analysis, edge detection, pattern and shape matching, dimension measurements as well as optical character recognition, enabling readers to quickly and efficiently use these functions for their own machine vision applications. A discussion of the concepts involved in programming the Vision Development Module rounds off the book, while example problems and exercises are included for training purposes as well as to further explain the concept of machine vision. With its step-by-step guide and clear structure, this is an essential reference for beginners and experienced researchers alike.
This edited volume presents a unique multidisciplinary perspective on the problem of visual object categorization. The result of a series of four highly successful workshops on the topic, the book gathers many of the most distinguished researchers from both computer and human vision to reflect on their experience, identify open problems, and foster a cross-disciplinary discussion with the idea that parallel problems and solutions have arisen in both domains. Twenty-seven of these workshop speakers have contributed chapters, including fourteen from computer vision and thirteen from human vision. Their contributions range from broad perspectives on the problem to more specific approaches, collectively providing important historical context, identifying the major challenges, and presenting recent research results. This multidisciplinary collection is the first of its kind on the topic of object categorization, providing an outstanding context for graduate students and researchers in both computer and human vision.
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks
This book explores recursive architectures in designing progressive hyperspectral imaging algorithms. In particular, it makes progressive imaging algorithms recursive by introducing the concept of Kalman filtering in algorithm design so that hyperspectral imagery can be processed not only progressively sample by sample or band by band but also recursively via recursive equations. This book can be considered a companion book of author's books, Real-Time Progressive Hyperspectral Image Processing, published by Springer in 2016.
"Video Tracking" provides a comprehensive treatment of the fundamental aspects of algorithm and application development for the task of estimating, over time, the position of objects of interest seen through cameras. Starting from the general problem definition and a review of existing and emerging video tracking applications, the book discusses popular methods, such as those based on correlation and gradient-descent. Using practical examples, the reader is introduced to the advantages and limitations of deterministic approaches, and is then guided toward more advanced video tracking solutions, such as those based on the Bayes' recursive framework and on Random Finite Sets.
Key features: Discusses the design choices and implementation issues required to turn the underlying mathematical models into a real-world effective tracking systems. Provides block diagrams and simil-code implementation of the algorithms. Reviews methods to evaluate the performance of video trackers - this is identified as a major problem by end-users.
The book aims to help researchers and practitioners develop techniques and solutions based on the potential of video tracking applications. The design methodologies discussed throughout the book provide guidelines for developers in the industry working on vision-based applications. The book may also serve as a reference for engineering and computer science graduate students involved in vision, robotics, human-computer interaction, smart environments and virtual reality programmes
We live in a society which is increasingly interconnected, in which communication between individuals is mostly mediated via some electronic platform, and transactions are often carried out remotely. In such a world, traditional notions of trust and confidence in the identity of those with whom we are interacting, taken for granted in the past, can be much less reliable. Biometrics - the scientific discipline of identifying individuals by means of the measurement of unique personal attributes - provides a reliable means of establishing or confirming an individual's identity. These attributes include facial appearance, fingerprints, iris patterning, the voice, the way we write, or even the way we walk. The new technologies of biometrics have a wide range of practical applications, from securing mobile phones and laptops to establishing identity in bank transactions, travel documents, and national identity cards. This Very Short Introduction considers the capabilities of biometrics-based identity checking, from first principles to the practicalities of using different types of identification data. Michael Fairhurst looks at the basic techniques in use today, ongoing developments in system design, and emerging technologies, all aimed at improving precision in identification, and providing solutions to an increasingly wide range of practical problems. Considering how they may continue to develop in the future, Fairhurst explores the benefits and limitations of these pervasive and powerful technologies, and how they can effectively support our increasingly interconnected society. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.
Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You'll learn what it takes to build applications that enable computers to "see" and make decisions based on that data. With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you've learned. This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision. Learn OpenCV data types, array types, and array operations Capture and store still and video images with HighGUI Transform images to stretch, shrink, warp, remap, and repair Explore pattern recognition, including face detection Track objects and motion through the visual field Reconstruct 3D images from stereo vision Discover basic and advanced machine learning techniques in OpenCV
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.
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.
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach. Relying on years of industry experience transforming deep-learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, CoreML, and TensorFlow Lite and go from zero to a production-quality system quickly. Develop deep learning applications for the desktop, cloud, smartphones, browser, and Raspberry Pi Learn by building examples such as Silicon Valley's "Not Hotdog," image search engines, and your own mini-autonomous car Use transfer learning to train models in minutes Optimize your apps to run efficiently on different hardware Discover strategies to scale up from a single user to millions Sharpen practical skills for data collection, model interoperability, and model debugging using visualizations Uncover the potential for bias and explore the ethical underpinnings for AI-driven technology
This book is an essential tool for second-year undergraduate students and above, providing clear and concise explanations of the basic concepts of computer graphics, and enabling the reader to immediately implement these concepts in Java 2D and/or 3D with only elementary knowledge of the programming language. Features: provides an ideal, self-contained introduction to computer graphics, with theory and practice presented in integrated combination; presents a practical guide to basic computer graphics programming using Java 2D and 3D; includes new and expanded content on the integration of text in 3D, particle systems, billboard behaviours, dynamic surfaces, the concept of level of detail, and the use of functions of two variables for surface modelling; contains many pedagogical tools, including numerous easy-to-understand example programs and end-of-chapter exercises; supplies useful supplementary material, including additional exercises, solutions, and program examples, at an associated website.
This self-contained, systematic treatment of multivariate approximation begins with classical linear approximation, and moves on to contemporary nonlinear approximation. It covers substantial new developments in the linear approximation theory of classes with mixed smoothness, and shows how it is directly related to deep problems in other areas of mathematics. For example, numerical integration of these classes is closely related to discrepancy theory and to nonlinear approximation with respect to special redundant dictionaries, and estimates of the entropy numbers of classes with mixed smoothness are closely related to (in some cases equivalent to) the Small Ball Problem from probability theory. The useful background material included in the book makes it accessible to graduate students. Researchers will find that the many open problems in the theory outlined in the book provide helpful directions and guidance for their own research in this exciting and active area.
Computer vision has widespread and growing application including robotics, autonomous vehicles, medical imaging and diagnosis, surveillance, video analysis, and even tracking for sports analysis. This book equips the reader with crucial mathematical and algorithmic tools to develop a thorough understanding of the underlying components of any complete computer vision system and to design such systems. These components include identifying local features such as corners or edges in the presence of noise, edge preserving smoothing, connected component labeling, stereopsis, thresholding, clustering, segmentation, and describing and matching both shapes and scenes. The extensive examples include photographs of faces, cartoons, animal footprints, and angiograms, and each chapter concludes with homework exercises and suggested projects. Intended for advanced undergraduate and beginning graduate students, the text will also be of use to practitioners and researchers in a range of applications.
Modern blockbuster movies seamlessly introduce impossible characters and action into real-world settings using digital visual effects. These effects are made possible by research from the field of computer vision, the study of how to automatically understand images. Computer Vision for Visual Effects will educate students, engineers, and researchers about the fundamental computer vision principles and state-of-the-art algorithms used to create cutting-edge visual effects for movies and television. The author describes classical computer vision algorithms used on a regular basis in Hollywood (such as blue screen matting, structure from motion, optical flow, and feature tracking) and exciting recent developments that form the basis for future effects (such as natural image matting, multi-image compositing, image retargeting, and view synthesis). He also discusses the technologies behind motion capture and three-dimensional data acquisition. More than 200 original images demonstrating principles, algorithms, and results, along with in-depth interviews with Hollywood visual effects artists, tie the mathematical concepts to real-world filmmaking.
The definitive work on iris recognition technology, this comprehensive handbook presents a broad overview of the state of the art in this exciting and rapidly evolving field. Revised and updated from the highly-successful original, this second edition has also been considerably expanded in scope and content, featuring four completely new chapters. Features: provides authoritative insights from an international selection of preeminent researchers from government, industry, and academia; reviews issues covering the full spectrum of the iris recognition process, from acquisition to encoding; presents surveys of topical areas, and discusses the frontiers of iris research, including cross-wavelength matching, iris template aging, and anti-spoofing; describes open source software for the iris recognition pipeline and datasets of iris images; includes new content on liveness detection, correcting off-angle iris images, subjects with eye conditions, and implementing software systems for iris recognition.
Although it is now possible to integrate many millions of transistors on a single chip, traditional digital circuit technology is now reaching its limits, facing problems of cost and technical efficiency when scaled down to ever-smaller feature sizes. The analysis of biological neutral systems, especially for visual processing, has allowed angineers to better understand how complex network can effictively process large amounts of information, whilst dealing with difficult computational challenges.
Analog and parallel processing are key characteristics of biological neutral networks. Analog VLSI circuits using the same features can therefore be developed to emulate brain-style processing. Using standard CMOS technology, they can be cheaply manufactured, permitting efficient industrial and consumer applications in robotics and mobile electronics.
This book explores the theory, design and implementation of analog VLSI circuits, inspired by visual motion processing in biological neutral networks. Using a novel approach pioneered by the author himself, Stocker explains in detail the construction of a series of electronic chips, providing the reader with a valuable practical insight into the technology.
Analog VLSI Circuits for the Perception of Visual Motion: analyzes the computational problems in visual motion perception; examines the issue of optimization in analog networks through high level processes such as motion segmentation and selective attention; demonstrates network implementation in anallog VLSI CMOS technology to provide computationally efficient devices; sets out measurements of final hardware implementation; illustrates the similarities of the presented circuitswith the human visual motion perception system; includes an accompanying website with video clips of circuits under real-time visual conditions and additional supplementary material.
With a complete review of all existing neuromorphic analog VLSI systems for visual motion sensing, Analog VLSI Circuits for the Perception of Visual Motion is a unique reference for advanced students in electrical engineering, artificial intelligence, robotics and computational neuroscience. It will also be useful for researcher, professionals, and electronics engineers working in the field.
This book is focused on the recent advances in computer vision methodologies and technical solutions using conventional and intelligent paradigms. The Contributions include: * Morphological Image Analysis for Computer Vision Applications. * Methods for Detecting of Structural Changes in Computer Vision Systems. * Hierarchical Adaptive KL-based Transform: Algorithms and Applications. * Automatic Estimation for Parameters of Image Projective Transforms Based on Object-invariant Cores. * A Way of Energy Analysis for Image and Video Sequence Processing. * Optimal Measurement of Visual Motion Across Spatial and Temporal Scales. * Scene Analysis Using Morphological Mathematics and Fuzzy Logic. * Digital Video Stabilization in Static and Dynamic Scenes. * Implementation of Hadamard Matrices for Image Processing. * A Generalized Criterion of Efficiency for Telecommunication Systems. The book is directed to PhD students, professors, researchers and software developers working in the areas of digital video processing and computer vision technologies.
The research book is focused on the recent advances in computer vision methodologies and innovations in practice. The Contributions include: * Human Action Recognition: Contour-Based and Silhouette-based Approaches. * The Application of Machine Learning Techniques to Real Time Audience Analysis System. * Panorama Construction from Multi-view Cameras in Outdoor Scenes. * A New Real-Time Method of Contextual Image Description and Its Application in Robot Navigation and Intelligent Control. * Perception of Audio Visual Information for Mobile Robot Motion Control Systems. * Adaptive Surveillance Algorithms Based on the Situation Analysis. * Enhanced, Synthetic and Combined Vision Technologies for Civil Aviation. * Navigation of Autonomous Underwater Vehicles Using Acoustic and Visual Data Processing. * Efficient Denoising Algorithms for Intelligent Recognition Systems. * Image Segmentation Based on Two-dimensional Markov Chains. The book is directed to the PhD students, professors, researchers and software developers working in the areas of digital video processing and computer vision technologies.
This book constitutes the thoroughly refereed proceedings of the Third International Multi-topic Conference on Communications, Technologies, Information Security and Sustainable Development, IMTIC 2013, held in Jamshoro, Pakistan, in December 2013. The 27 revised papers presented in this volume were carefully reviewed and selected from 140 submissions. The topics presented had a reasonable balance between theory and practice in multi-disciplined topics including wireless sensor networks, cloud computing, wireless communication, antenna design, signal processing, software engineering, image processing, bioinformatics and telemedicine, neural networks, automation and control, and green renewable energy.
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