Shape Detection In Computer Vision Using The Hough Transform
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Shape Detection in Computer Vision Using the Hough Transform
Author: V.F. Leavers
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Shape detection techniques are an important aspect of computer vision and are used to transform raw image data into the symbolic representations needed for object recognition and location. However, the availability and application of research data relating to shape detection has traditionally been limited by a lack of computational and mathematical skill on the part of the intended end-user. As a result progress in areas such as the automation of visual inspection techniques, where shape detection couls play a pivotal role, has been relatively slow. In this volume, Violet Leavers, an established author and researcher in the field, examines the Hough Transform, a technique which is particularly relevant to industrial applications. By making computational recipes and advice available to the non-specialist, the book aims to popularize the technique, and to provide a bridge between low level computer vision tasks and specialist applications. In addition, Shape Detection in Computer Vision Using the Hough Transform assesses practical and theoretical issues which were previously only available in scientific literature in a way which is easily accessible to the non-specialist user. Shape Detection in Computer Vision Using the Hough Transform fills an obvious gap in the existing market. It is an important textbook which will provide postgraduate students with a thorough grounding in the field, and will also be of interest to junior research staff and program designers.
Advanced Concepts for Intelligent Vision Systems
Author: Jacques Blanc-Talon
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-09-15
This book constitutes the refereed proceedings of the 8th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2006, held in Antwerp, Belgium in September 2006. The 45 revised full papers and 65 revised poster papers presented were carefully reviewed and selected from around 242 submissions. The papers are organized in topical sections on noise reduction and restoration, segmentation, motion estimation and tracking, video processing and coding, camera calibration, image registration and stereo matching, biometrics and security, medical imaging, image retrieval and image understanding, as well as classification and recognition.
Feature Extraction and Image Processing for Computer Vision
Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. 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 link between theory and exemplar code of the algorithms." Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation. - The only text to concentrate on feature extraction with working implementation and worked through mathematical derivations and algorithmic methods - A thorough overview of available feature extraction methods including essential background theory, shape methods, texture and deep learning - Up to date coverage of interest point detection, feature extraction and description and image representation (including frequency domain and colour) - Good balance between providing a mathematical background and practical implementation - Detailed and explanatory of algorithms in MATLAB and Python