Machine Vision: Theory, Algorithms, Practicalities

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1 Machine Vision: Theory, Algorithms, Practicalities 2nd Edition E.R. DAVIES Department of Physics Royal Holloway University of London Egham, Surrey, UK ACADEMIC PRESS San Diego London Boston New York Sydney Tokyo Toronto

Preface to First Edition xxi Preface to Second Edition xxiv About the Author xxvi Glossary of Acronyms and Abbreviations Acknowledgements xxix xxvii 1 Vision, the Challenge 1 1.

2 Preface to First Edition xxi Preface to Second Edition xxiv About the Author xxvi Glossary of Acronyms and Abbreviations Acknowledgements xxix xxvii 1 Vision, the Challenge Introduction man and his senses The nature of vision The process of recognition Tackling the recognition problem Object location Scene analysis Vision as inverse graphics Automated visual inspection What this book is about The following chapters Bibliographical notes 15 Part 1 Low-Level Processing 2 Images and Imaging Operations Introduction Grey scale versus colour 21 VII

VIM 2.2 Image processing operations 22 2.2.1 Some basic operations on grey-scale images 23 2.2.2 Basic operations on binary images 27 2.2.3 Noise suppression by image accumulation 31 2.

3 VIM 2.2 Image processing operations Some basic operations on grey-scale images Basic operations on binary images Noise suppression by image accumulation Convolutions and point spread functions Sequential versus parallel operations Concluding remarks Bibliographical and historical notes Problems 39 3 Basic Image Filtering Operations Introduction Noise suppression by Gaussian smoothing Median filtering Mode filtering Bias generated by noise suppression filters Theory of edge shifts caused by median filters in binary images Edge shifts caused by median filters in grey-scale images Edge shifts arising with hybrid median filters Problems with statistics Reducing computational load A bit-based method for fast median filtering VLSI implementation of the median filter The role of filters in industrial applications of vision Sharp-unsharp masking Concluding remarks Bibliographical and historical notes Problems 76 4 Thresholding Techniques Introduction Region-growing methods Thresholding Finding a suitable threshold Tackling the problem of bias in threshold selection 83

4.3.2.1 Methods based on finding a valley in the intensity distribution 85 4.3.2.2 Methods which concentrate on the peaked intensity distribution at high gradient 85 4.3.3 A convenient mathematical model 87 4.

4 Methods based on finding a valley in the intensity distribution Methods which concentrate on the peaked intensity distribution at high gradient A convenient mathematical model Summary Adaptive thresholding The Chow and Kaneko approach Local thresholding methods Concluding remarks Bibliographical and historical notes Problems 101 Locating Objects via Their Edges Introduction Basic theory of edge detection The template matching approach Theory of 3 x 3 template operators Summary design constraints and conclusions The design of differential gradient operators The concept of a circular operator Detailed implementation of circular operators Structured bands of pixels in neighbourhoods of various sizes The systematic design of differential edge operators Problems with the above approach some alternative schemes Concluding remarks Bibliographical and historical notes Problems 130 Binary Shape Analysis Introduction Connectedness in binary images Object labelling and counting Metric properties in digital images Size filtering 140

X 6.6 The convex hull and its computation 142 6.7 Distance functions and their uses 146 6.8 Skeletons and thinning 151 6.8.1 Crossing number 153 6.8.2 Parallel and sequential implementations of thinning 156 6.

5 X 6.6 The convex hull and its computation Distance functions and their uses Skeletons and thinning Crossing number Parallel and sequential implementations of thinning Guided thinning A comment on the nature of the skeleton Skeleton node analysis Application of skeletons for shape recognition Some simple measures for shape recognition Shape description by moments Boundary tracking procedures Concluding remarks Bibliographical and historical notes Problems Boundary Pattern Analysis Introduction Boundary tracking procedures Template matching a reminder Centroidal profiles Problems with the centroidal profile approach Some solutions The 0, V) plot Tackling the problems of occlusion Chain code The (r, s) plot Accuracy of boundary length measures Concluding remarks Bibliographical and historical notes Problems 191 Part 2 Intermediate-Level Processing 8 Line Detection Introduction 195

XI 8.2 Application of the Hough transform to line detection 196 8.3 The foot-of-normal method 200 8.3.1 Error analysis 202 8.3.2 Quality of the resulting data 204 8.3.3 Application of the foot-of-normal method 205 8.

6 XI 8.2 Application of the Hough transform to line detection The foot-of-normal method Error analysis Quality of the resulting data Application of the foot-of-normal method Longitudinal line localization Final line fitting Concluding remarks Bibliographical and historical notes Problem Circle Detection Introduction Hough-based schemes for circular object detection The problem of unknown circle radius Experimental results The problem of accurate centre location Obtaining a method for reducing computational load Improvements on the basic scheme Discussion Practical details Overcoming the speed problem More detailed estimates of speed Robustness Experimental results Summary Concluding remarks Bibliographical and historical notes Problem The Hough Transform and Its Nature Introduction The generalized Hough transform Setting up the generalized Hough transform some relevant questions Spatial matched filtering in images 248

XII 10.5 From spatial matched filters to generalized Hough transforms 249 10.6 Gradient weighting versus uniform weighting 251 10.6.1 Calculation of sensitivity and computational load 252 10.

7 XII 10.5 From spatial matched filters to generalized Hough transforms Gradient weighting versus uniform weighting Calculation of sensitivity and computational load Summary Applying the generalized Hough transform to line detection An instructive example Tradeoffs to reduce computational load The effects of occlusions for objects with straight edges Fast implementations of the Hough transform The approach of Gerig and Klein Concluding remarks Bibliographical and historical notes Ellipse Detection Introduction The diameter bisection method The chord-tangent method Finding the remaining ellipse parameters Reducing computational load for the generalized Hough transform method Practical details Comparing the various methods Concluding remarks Bibliographical and historical notes Problems Hole Detection Introduction The template matching approach The lateral histogram technique The removal of ambiguities in the lateral histogram technique Computational implications of the need to check for ambiguities Further detail of the subimage method 296

XIII 12.5 Application of the lateral histogram technique for object location 298 12.5.1 Limitations of the approach 302 12.6 A strategy based on applying the histograms in turn 303 12.

8 XIII 12.5 Application of the lateral histogram technique for object location Limitations of the approach A strategy based on applying the histograms in turn Appraisal of the hole detection problem Concluding remarks Bibliographical and historical notes Problems Polygon and Corner Detection Introduction The generalized Hough transform Straight edge detection Application to the detection of regular polygons The case of an arbitrary triangle The case of an arbitrary rectangle Lower bounds on the numbers of parameter planes An extension of the triangle result Discussion Determining orientation Why corner detection? Template matching Second-order derivative schemes A median-based corner detector Analysing the operation of the median detector Practical results The Hough transform approach to corner detection The lateral histogram approach to corner detection Corner orientation Concluding remarks Bibliographical and historical notes Problems 343 Part 3 Application-Level Processing 14 Abstract Pattern Matching Techniques Introduction 347

xiv 14.2 A graph-theoretic approach to object location 348 14.2.1 A practical example locating cream biscuits 353 14.3 Possibilities for saving computation 356 14.

9 xiv 14.2 A graph-theoretic approach to object location A practical example locating cream biscuits Possibilities for saving computation Using the generalized Hough transform for feature collation Computational load Generalizing the maximal clique and other approaches Relational descriptors Search Concluding remarks Bibliographical and historical notes Problems The Three-Dimensional World Introduction Three-dimensional vision the variety of methods Projection schemes for three-dimensional vision Binocular images The correspondence problem Shape from shading Photometric stereo The assumption of surface smoothness Shape from texture Use of structured lighting Three-dimensional object recognition schemes The method of Ballard and Sabbah The method of Silberberg et al Horaud 's junction orientation technique The 3DPO system of Bolles and Horaud The IVISM system Lowe's approach Concluding remarks Bibliographical and historical notes Problems Tackling the Perspective N-Point Problem Introduction The phenomenon of perspective inversion 417

xv 16.3 Ambiguity of pose under weak perspective projection 419 16.4 Obtaining unique solutions to the pose problem 422 16.4.1 Solution of the three-point problem 426 16.4.2 Using symmetric trapezia for estimating pose 427 16.

10 xv 16.3 Ambiguity of pose under weak perspective projection Obtaining unique solutions to the pose problem Solution of the three-point problem Using symmetric trapezia for estimating pose Concluding remarks Bibliographical and historical notes Motion Introduction Optical flow Interpretation of optical flow fields Using focus of expansion to avoid collision Time-to-adjacency analysis Basic difficulties with the optical flow model Stereo from motion Applications to the monitoring of traffic flow The system of Bascle et al The system of Koller et al. AA Concluding remarks Bibliographical and historical notes Invariants and Their Applications Introduction Cross ratios: the "ratio of ratios" concept Invariants for noncollinear points Invariants for points on conies Concluding remarks Bibliographical and historical notes Automated Visual Inspection Introduction The process of inspection Review of the types of object to be inspected Food products Precision components Differing requirements for size measurement 475

XVI 19.3.4 Three-dimensional objects 475 19.3.5 Other products and materials for inspection 476 19.4 Summary the main categories of inspection 476 19.

11 XVI Three-dimensional objects Other products and materials for inspection Summary the main categories of inspection Shape deviations relative to a standard template Inspection of circular products Computation of the radial histogram: statistical problems Application of radial histograms Inspection of printed circuits Steel strip and wood inspection Inspection of products with high levels of variability X-ray inspection Bringing inspection to the factory Concluding remarks Bibliographical and historical notes Statistical Pattern Recognition Introduction The nearest neighbour algorithm Bayes'decision theory Relation of the nearest neighbour and Bayes' approaches Mathematical statement of the problem The importance of the nearest neighbour classifier The optimum number of features Cost functions and error-reject tradeoff Cluster analysis Supervised and unsupervised learning Clustering procedures Principal components analysis The relevance of probability in image analysis Concluding remarks Bibliographical and historical notes Problems Biologically Inspired Recognition Schemes Introduction 529

XVII 21.2 Artificial neural networks 530 21.3 The back-propagation algorithm 536 21.4 MLP architectures 539 21.5 Overfitting to the training data 540 21.6 Optimizing the network architecture 543 21.

12 XVII 21.2 Artificial neural networks The back-propagation algorithm MLP architectures Overfitting to the training data Optimizing the network architecture Hebbian learning Case-study: noise suppression using ANNs Genetic algorithms Concluding remarks Bibliographical and historical notes Texture Introduction Some basic approaches to texture analysis Grey-level co-occurrence matrices Laws' texture energy approach Ade's eigenfilter approach Appraisal of the Laws and Ade approaches Fractal-based measures of texture Shape from texture Markov random field models of texture Structural approaches to texture analysis Concluding remarks Bibliographical and historical notes Image Acquisition Introduction Illumination schemes Eliminating shadows Arranging a region of uniform illumination Use of linescan cameras Cameras and digitization Digitization The sampling theorem Concluding remarks Bibliographical and historical notes 600

XVIII 24 The Need for Speed: Real-Time Electronic Hardware Systems 603 24.1 Introduction 603 24.2 Parallel processing 604 24.3 SIMD systems 606 24.

13 XVIII 24 The Need for Speed: Real-Time Electronic Hardware Systems Introduction Parallel processing SIMD systems The gain in speed attainable with N processors Flynn's classification Optimal implementation of an image analysis algorithm Hardware specification and design Basic ideas on optimal hardware implementation Board-level processing systems VLSI Concluding remarks Bibliographical and historical notes 618 Part 4 Perspectives on Vision 25 Machine Vision, Art or Science? Introduction Parameters of importance in machine vision Tradeoffs Some important tradeoffs Tradeoffs for two-stage template matching Future directions Hardware, algorithms and processes A retrospective view Just a glimpse of vision? Bibliographical and historical notes 633 Appendices Appendix A: Programming Notation 635 A. 1 Introduction 635 A.2 The Pascal language 636 A.2.1 Control structures 636 A.2.2 Procedures and functions 638

XIX A.2.3 Other details of Pascal syntax 639 A.2.4 The need for special syntax 642 A.3 Special syntax embedded in Pascal 642 A.3.1 Image handling notation 642 A.3.2 Other succinct notation 643 A.

14 XIX A.2.3 Other details of Pascal syntax 639 A.2.4 The need for special syntax 642 A.3 Special syntax embedded in Pascal 642 A.3.1 Image handling notation 642 A.3.2 Other succinct notation 643 A.4 On the validity of the "repeat until finished" construct 646 Appendix B: Mathematical Morphology 647 B.l Introduction 647 B.2 Dilation and erosion in binary images 648 B.2.1 Dilation and erosion 648 B.2.2 Cancellation effects 648 B.2.3 Modified dilation and erosion operators 649 B.3 Mathematical morphology 649 B.3.1 Generalized morphological dilation 649 B.3.2 Generalized morphological erosion 651 B.3.3 Duality between dilation and erosion 652 B.3.4 Closing and opening 655 B.3.5 Hit-and-miss transform 656 B.3.6 Template matching 657 B.4 Connectedness-based analysis of images 658 B.4.1 Skeletons and thinning 658 B.5 Concluding remarks 660 B.6 Bibliographical and historical notes 660 Appendix C: Image Transformations and Camera Calibration 663 C. 1 Introduction 663 C.2 Image transformations 663 C.3 Camera calibration 668 C.4 Intrinsic and extrinsic parameters 671 C.5 Concluding remarks 675 C.6 Bibliographical and historical notes 675 Appendix D: Robust statistics 677 D. 1 Introduction 677 D.2 Preliminary definitions and analysis 680

15 XX D.3 The M-estimator (influence function) approach 683 D.4 The least median of squares approach to regression 688 D.5 Overview of the robustness problem 691 D.6 Concluding remarks 693 D.7 Bibliographical and historical notes 694 References 695 Subject Index 725 Author Index 741

COMPUTER AND ROBOT VISION

VOLUME COMPUTER AND ROBOT VISION Robert M. Haralick University of Washington Linda G. Shapiro University of Washington A^ ADDISON-WESLEY PUBLISHING COMPANY Reading, Massachusetts Menlo Park, California