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Visual Search for Objects with Straight Lines

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Visual Search for Objects with Straight Lines VISUAL SEARCH FOR OBJECTS WITH STRAIGHT LINES by SIMON HAIG MELIKIAN Submitted for the degree of Doctor of Philosophy Case School of Engineering Electrical Engineering and Computer Science Thesis Adviser: Prof. Dr. Christos Papachristou January, 2006 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the dissertation of ______________________________________________________ candidate for the Ph.D. degree *. (signed)_______________________________________________ (chair of the committee) ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ (date) _______________________ *We also certify that written approval has been obtained for any proprietary material contained therein. Copyright © 2006 by Simon Haig Melikian ALL RIGHTS RESERVED VISUAL SEARCH FOR OBJECTS WITH STRAIGHT LINES by SIMON HAIG MELIKIAN Abstract I present a new method of visual search for objects that include straight lines. This is usually the case for machine-made objects. I describe existing machine vision search methods and show how my method of visual search gives better performance on objects that have straight lines. Inspired from human vision, a two-step process is used. First straight line segments are detected in an image and characterized by their length, mid- point location, and orientation. Second, hypotheses that a particular straight line segment belongs to a known object are generated and tested. The set of hypotheses is constrained by spatial relationships in the known objects. I discuss implementation of my method and its performance and limitations in real and synthetic images. The speed and robustness of my method make it immediately applicable to many machine vision problems. Table of Contents TABLE OF FIGURES ..................................................................................................................................3 ACKNOWLEDGEMENTS..........................................................................................................................5 ACKNOWLEDGEMENTS..........................................................................................................................5 CHAPTER 1 ..................................................................................................................................................6 1.1 THIS THESIS’S CONTRIBUTIONS.............................................................................................................6 1.2 INTRODUCTION......................................................................................................................................7 1.3 SCOPE OF THIS THESIS .........................................................................................................................10 CHAPTER 2 ................................................................................................................................................13 2.1 A LOOK AT HUMAN VISION.................................................................................................................13 2.1.1 Modular Systems for Features and Hypotheses..........................................................................13 2.1.2 Are Straight Lines Salient Features?..........................................................................................14 2.2 PRIOR MACHINE VISION WORK...........................................................................................................17 2.2.1 Binary Search Methods...............................................................................................................17 2.2.2 Normalized Grayscale Correlation.............................................................................................19 2.2.3 Geometric Based Search and Recognition..................................................................................22 2.2.4 Contour Based Search ................................................................................................................24 2.2.5 Affine Invariant Constellation Based Recognition......................................................................27 2.2.5.1 Corner Based....................................................................................................................................... 28 2.2.5.2 Salient Icons........................................................................................................................................ 30 2.2.5.3 Scale Invariant Feature Transform ...................................................................................................... 34 CHAPTER 3 ................................................................................................................................................38 3.1 VISUAL SEARCH WITH STRAIGHT LINES ..............................................................................................38 3.1.1 Search Constraints for Machine Vision......................................................................................38 3.1.2 Using Straight Lines as Icons .....................................................................................................39 3.1.3 Search with Lines........................................................................................................................41 3.1.4 The Cost of Hypothesis Generation ............................................................................................49 3.1.5 The Cost of Verification..............................................................................................................49 3.1.6 Gradient Angle of a Line.............................................................................................................50 3.1.7 The Number of Reference Lines Needed for Robust Search .......................................................50 3.2 ABSTRACT LOOK AT VISUAL SEARCH WITH STRAIGHT LINES .............................................................50 CHAPTER 4 ................................................................................................................................................52 4.1 CURVATURE BASED STRAIGHT LINE EXTRACTION (CBSLE)..............................................................52 4.1.1 Effect of Span Value....................................................................................................................59 4.2 SPLIT AND MERGE METHOD FOR STRAIGHT LINE EXTRACTION..........................................................60 4.3 LINE EXTRACTION PERFORMANCE ......................................................................................................64 4.3.1 End Points Position Accuracy ....................................................................................................64 4.3.1.1 Effect of Object Size on End Point Accuracy...................................................................................... 64 4.3.1.2 Effect of Noise on End Point Accuracy............................................................................................... 66 CHAPTER 5 ................................................................................................................................................68 5.1 HYPOTHESIS GENERATION ..................................................................................................................68 5.2 VERIFICATION .....................................................................................................................................70 CHAPTER 6 ................................................................................................................................................75 6.1 ABSTRACT TESTING ............................................................................................................................75 6.1.1 Speed vs. Background Straight Lines..........................................................................................76 6.1.2 Speed vs. Target Position............................................................................................................78 6.1.3 Speed vs. Angle ...........................................................................................................................79 1 of 106 6.1.4 Speed vs. Scale............................................................................................................................79 6.1.5 Speed vs. Scale and Angle...........................................................................................................79 6.2 REAL WORLD TESTING.........................................................................................................................80 6.3 FEASIBILITY OF TEACHING WITH SYNTHETIC MODEL .........................................................................90 CONCLUSION............................................................................................................................................96 APPENDIX 1. COMPARISON WITH OTHER METHODS.................................................................97 REFERENCES............................................................................................................................................99 2 of 106 Table of Figures Figure 1. Ice cream package lid on a conveyer belt............................................................ 9 Figure 2. Wheel-rim identification ..................................................................................
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