Explore the vast range of titles available.

A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers. Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability. There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems. Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book * Offers both foundations and advances on emotion recognition in a single volume * Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains * Inspires young researchers to prepare themselves for their own research * Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints.

The detection and recognition of objects in images is a key research topic in the computer vision community.  Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems.

We propose a method for eliminating the temporal illumination variations in whisk-broom (point-scan) hyperspectral imaging. Whisk-broom scanning is useful for acquiring a spatial measurement using a pixel-based hyperspectral sensor. However, when it is applied to outdoor cultural heritages, temporal illumination variations become an issue due to the lengthy measurement time. As a result, the incoming illumination spectra vary across the measured image locations because different locations are measured at different times. To overcome this problem, in addition to the standard raster scan, we propose an additional perpendicular scan that traverses the raster scan. We show that this additional scan allows us to infer the illumination variations over the raster scan. Furthermore, the sparse structure in the illumination spectrum is exploited to robustly eliminate these variations. We quantitatively show that a hyperspectral image captured under sunlight is indeed affected by temporal illumination variations, that a Naïve mitigation method suffers from severe artifacts, and that the proposed method can robustly eliminate the illumination variations. Finally, we demonstrate the usefulness of the proposed method by capturing historic stained-glass windows of a French cathedral.

Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab.Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated.

While the field of computer vision drives many of today's digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color in computer vision, this book provides a complete set of tools for working with color in the field of image understanding.Based on the authors' intense collaboration for more than a decade and drawing on the latest thinking in the field of computer science, the book integrates topics from color science and computer vision, clearly linking theories, techniques, machine learning, and applications. The fundamental basics, sample applications, and downloadable versions of the software and data sets are also included. Clear, thorough, and practical, Color in Computer Vision explains:Computer vision, including color-driven algorithms and quantitative results of various state-of-the-art methodsColor science topics such as color systems, color reflection mechanisms, color invariance, and color constancyDigital image processing, including edge detection, feature extraction, image segmentation, and image transformationsSignal processing techniques for the development of both image processing and machine learningRobotics and artificial intelligence, including such topics as supervised learning and classifiers for object and scene categorization Researchers and professionals in computer science, computer vision, color science, electrical engineering, and signal processing will learn how to implement color in computer vision applications and gain insight into future developments in this dynamic and expanding field.

Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2).  This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous.  We are now surrounded by cameras, for example cameras on computers & tablets/  cameras built into our mobile phones/  cameras in games consoles; cameras imaging difficult modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This book is concerned with helping the next generation of computer developers to make use of all these images in order to develop systems which are more intuitive and interact with us in more intelligent ways.  * Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries * Offers an introduction to computer vision, with enough theory to make clear how the various algorithms work but with an emphasis on practical programming issues * Provides enough material for a one semester course in computer vision at senior undergraduate and Masters levels  * Includes the basics of cameras and images and image processing to remove noise, before moving on to topics such as image histogramming; binary imaging; video processing to detect and model moving objects; geometric operations & camera models; edge detection; features detection; recognition in images * Contains a large number of vision application problems to provide students with the opportunity to solve real problems. Images or videos for these problems are provided in the resources associated with this book which include an enhanced eBook

Learn the techniques for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications using examples on different functions of OpenCV. Key Features Learn how to apply complex visual effects to images with OpenCV 3.x and Python Extract features from an image and use them to develop advanced applications Build algorithms to help you understand image content and perform visual searches Get to grips with advanced techniques in OpenCV such as machine learning, artificial neural network, 3D reconstruction, and augmented reality Book Description Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications. What you will learn Detect shapes and edges from images and videos How to apply filters on images and videos Use different techniques to manipulate and improve images Extract and manipulate particular parts of images and videos Track objects or colors from videos Recognize specific object or faces from images and videos How to create Augmented Reality applications Apply artificial neural networks and machine learning to improve object recognitionWho this book is for This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV and Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on.

A revealing genealogy of image-recognition techniques and technologies Today's most advanced neural networks and sophisticated image-analysis methods come from 1950s and '60s Cold War culture-and many biases and ways of understanding the world from that era persist along with them. Aerial surveillance and reconnaissance shaped all of the technologies that we now refer to as computer vision, including facial recognition. The Birth of Computer Vision uncovers these histories and finds connections between the algorithms, people, and politics at the core of automating perception today. James E. Dobson reveals how new forms of computerized surveillance systems, high-tech policing, and automated decision-making systems have become entangled, functioning together as a new technological apparatus of social control. Tracing the development of a series of important computer-vision algorithms, he uncovers the ideas, worrisome military origins, and lingering goals reproduced within the code and the products based on it, examining how they became linked to one another and repurposed for domestic and commercial uses. Dobson includes analysis of the Shakey Project, which produced the first semi-autonomous robot, and the impact of student protest in the early 1970s at Stanford University, as well as recovering the computer vision-related aspects of Frank Rosenblatt's Perceptron as the crucial link between machine learning and computer vision. Motivated by the ongoing use of these major algorithms and methods, The Birth of Computer Vision chronicles the foundations of computer vision and artificial intelligence, its major transformations, and the questionable legacy of its origins. Cover alt text: Two overlapping circles in cream and violet, with black background. Top is a printed circuit with camera eye; below a person at a 1977 computer.

A practical guide that will help you build AI and ML solutions faster with fewer efforts and no programming knowledge Key Features ? Start your journey to become an AI expert today. ? Learn how to build AI solutions to solve complex problems in your organization. ? Get familiar with different No-code AI tools and platforms. Description \"No-Code Artificial Intelligence\" is a book that enables you to develop AI applications without any programming knowledge. Authored by the founder of AICromo (https://aicromo.com/), this book takes you through an array of examples that shows how to build AI solutions using No-code AI tools. The book starts by sharing insights on the evolution of No-code AI and the different types of No-code AI tools and platforms available in the market. The book then helps you start building applications of Machine Learning in Finance, Healthcare, Sales, and Cybersecurity. It will also teach you to create AI applications to perform sales forecasting, find fraudulent claims, and detect diseases in plants. Furthermore, the book will show how to build Machine Learning models for a variety of use cases in image recognition, video object recognition, and data prediction. After reading this book, you will be able to build AI applications with ease. What you will learn ? Use different No-code AI tools such as AWS Sagemaker, DataRobot, and Google AutoML. ? Learn how to create a Machine Learning model to predict housing prices. ? Build Natural Language Processing (NLP) models for Healthcare information Identification. ? Learn how to build an AI model to create targeted customer offerings. ? Use traditional ways to perform AI implementation using programming languages and AI libraries. Who this book is for This book is for anyone who wants to build an AI app without writing any code. It is also helpful for current and aspiring AI and Machine Learning professionals who are looking to build automated, intelligent, and smart AI-based solutions. Table of Contents 1. What is AI? 2. Getting Started with No-Code AI 3. Building AI Model to Predict Housing Prices 4. Classifying Different Images 5. Building AI Model to Perform Sales Forecasting 6. Building AI Model to Find Fraudulent Claims 7. Building AI Model to Detect Diseases in Plants 8. Building AI Model to Create Targeted Customer Offerings 9. Building AI Model for Healthcare Information Identification 10. Building AI Model for Video Action Recognition 11. Building AI Applications with Coded AI