The papers in this volume deal specifically with the problem of handwriting recognition and the new frontiers of research in the scientific community - including the significance of linguistic and contextual information in handwriting recognition and for adaptive preprocessing and postprocessing. Also addressed are problems related to human and computer behaviour in recognition. The aim of this book is not only to point out the state-of-the-art and the frontiers in the field of handwriting recognition, but also to stimulate research and ideas in the fields of design and implementation of on-line and off-line systems that recognize isolated and connected handwritten characters, words and signatures."

Parallelism in problems of low- and medium-level image processing and pattern recognition is the subject of this book. It covers the investigation of parallelism in algorithms and in fundamental methods of image processing and pattern recognition. Based on this, new concepts for parallel architectures are derived and their performance is evaluated. Different hardware structures such as SIMD, MIMD, data flow machines, transputer systems, neural networks and interconnection networks are described, including high-speed VLSI-implementations. Additional topics covered include software aspects and image processing systems.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Learn how to build your own computer vision (CV) applications quickly and easily with SimpleCV, an open source framework written in Python. Through examples of real-world applications, this hands-on guide introduces you to basic CV techniques for collecting, processing, and analyzing streaming digital images. You'll then learn how to apply these methods with SimpleCV, using sample Python code. All you need to get started is a Windows, Mac, or Linux system, and a willingness to put CV to work in a variety of ways. Programming experience is optional. Capture images from several sources, including webcams, smartphones, and Kinect Filter image input so your application processes only necessary information Manipulate images by performing basic arithmetic on pixel values Use feature detection techniques to focus on interesting parts of an image Work with several features in a single image, using the NumPy and SciPy Python libraries Learn about optical flow to identify objects that change between two image frames Use SimpleCV's command line and code editor to run examples and test techniques

This book constitutes the refereed proceedings of the First Brazilian Symposium on Document Image Analysis, BSDIA'97, held in Curitiba in November 1997. The volume presents 19 revised full papers selected from 30 submissions as well as eight full-paper invited contributions by internationally leading authorities. The invited papers give a unique survey of the state of the art in the area. The selected papers are organized in sections on low level processing, document processing and retrieval, handwriting recognition, signature verification, and application systems.

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. As one reviewer noted, "The main strength of the proposed book is the exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. - Named a 2012 Notable Computer Book for Computing Methodologies by Computing Reviews - Essential reading for engineers and students working in this cutting-edge field - Ideal module text and background reference for courses in image processing and computer vision - The only currently available text to concentrate on feature extraction with working implementation and worked through derivation

The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based. It also covers applications related to texture features, such as facial imaging. It is a valuable resource for machine vision researchers and practitioners in different application areas.