Overview

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 could play a pivotal role, has been relatively slow. This volume 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, the text 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.

Full Product Details

Author:   V.F. Leavers
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Edition. ed.
Dimensions:   Width: 17.00cm , Height: 1.20cm , Length: 24.20cm
Weight:   0.387kg
ISBN:  

9783540197232

ISBN 10:   3540197230
Pages:   201
Publication Date:   10 September 1992
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

1 Computer Vision: Shape Detection.- 1.1 Why Computer Vision?.- 1.1.1 Industrial Applications.- 1.1.2 Medical Applications.- 1.1.3 Social Applications.- 1.1.4 Military Applications.- 1.2 Why This Book?.- 1.3 Why the Hough Transform?.- 1.4 Representing Shape Symbolically.- 2 Transforms Without Tears.- 2.1 Beginning to See.- 2.2 What about Shape?.- 2.3 Tackling the Maths.- 2.4 Beginning to Compute.- 3 Preprocessing.- 3.1 The Real World.- 3.2 Spot the Difference.- 3.3 Convolution, a Necessary Tool.- 3.4 Edge Detection.- 3.5 Which Parametrisation?.- 3.6 Getting Started.- 3.7 Quantization.- 3.8 Test Images.- 4 Postprocessing.- 4.1 Results of the Transformation.- 4.2 The Butterfly Filter.- 4.3 Designer Butterflies.- 4.4 Putting Things to Work.- 4.5 Reconstruction.- 4.6 Summary.- 5 Representing Shape.- 5.1 From Lines to Circles.- 5.2 Double Houghing.- 5.3 Towards a Representation of Shape.- 5.3.1 Decomposition.- 5.3.2 Geometric and Spatial Relations.- 5.3.3 Saliency.- 5.3.4 Invariance.- 5.3.5 Stability.- 5.3.6 Accessibility.- 5.4 Summary.- 6 Which Hough?.- 6.1 Background.- 6.1.1 Historical.- 6.1.2 Whole Shape Detection.- 6.2 Refinements.- 6.2.1 Preprocessing Considerations.- 6.2.2 Postprocessing Considerations.- 6.3 Software Solutions.- 6.3.1 Computer Friendly Algorithms.- 6.3.2 Dynamically Quantised Accumulators.- 6.3.3 Constraints on Parameter Calculation.- 6.3.4 Parameter Space Decomposition.- 6.3.5 Segmenting the Image.- 6.4 Parallel Processing.- 6.4.1 SIMD Implementations.- 6.4.2 MIMD Implementations.- 6.5 Dedicated Hardware.- 6.6 The Probabilistic Houghs: A Review.- 7 A Case Study: Circles and Ellipses.- 7.1 Preprocessing the Image Data.- 7.2 The Dynamic Generalized Hough Transform.- 7.2.1 Connectivity Detection.- 7.2.2 Segmentation.- 7.2.3 Sampling of Data Points.- 7.2.4 Calculating the Parameters.- 7.2.5 Accumulation of the Parameters.- 7.2.6 Statistical Information and a Robust Stopping Criterion.- 7.2.7 Removal of Features from the Image.- 7.3 A Case Study.- 7.3.1 Edge Intensity Threshold and Connectivity Detection.- 7.3.2 Segmentation.- 7.3.3 Automatic Stopping Criterion.- 7.3.4 Results.- 7.3.5 Coping with the Unexpected.- 7.4 Discussion.- 7.5 Conclusions.- Appendix 1.- 1.1 The Radon Transform.- 1.2 Generalized Function Concentrated on a Line.- 1.3 The General Case.- 1.4 Application to an Ellipse.- Appendix 2.- Appendix 3.- Appendix 4.- References.

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