Recognizing Partially Occluded Objects Using Information Extracted From Polygonal Approximation.

This thesis addresses the problem of recognizing partially occluded two dimensional objects. The goal is to develop a system which is able to identify and locate several overlapping objects in the scene. To achieve this goal, the system must perform the following specific tasks: (1) storing useful information about objects in some format, which is often referred to as the process of object representation or model formation (2) matching procedure based on the object representation, and (3) efficient search of the best matching. This thesis presents a new approach to accomplish these tasks. Polygonal approximation is used to represent an object in this research. The accumulated lengths of line segments, s, and the accumulated sizes of turning angles, $\theta$, along the boundary from some starting point are extracted. The boundary of an object is then described as an equation $\theta$ = f(s). As algorithm shows, matching objects under s-$\theta$ space will be simple and effective. To avoid exhaustive matching in the recognition process, index diagrams of the features characterizing the boundary are established. Once the features of some unknown object are detected, the possible objects which might produce the best matching can be efficiently retrieved from this scheme

Rule-Based Approach to Binocular Stereopsis

This research is motivated by a desire to integrate some of the diverse, yet complimentary, developments that have taken place during the past few years in the area of passive stereo vision. On the one hand, we have approaches based on matching zero-crossings along epipolar lines, and, on the other, people have proposed techniques that match directly higher level percepts, such as line elements and other geometrical forms. Our rule-based program is a modest attempt at integrating these different approaches into a single program. Such integration was made necessary by the fact that no single method by itself appears capable of generating usable range maps of a scene

A two-stage framework for polygon retrieval.

by Tung Lun Hsing.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 80-84).Abstract --- p.iAcknowledgement --- p.iiChapter 1 --- Introduction --- p.1Chapter 2 --- Literature Survey --- p.8Chapter 2.1 --- The Freeman Chain Code Approach --- p.8Chapter 2.2 --- The Moment Approach --- p.10Chapter 2.3 --- The Rectangular Cover Approach --- p.12Chapter 2.4 --- The Potential-Based Approach --- p.15Chapter 2.5 --- The Normalized Coordinate System Approach --- p.17Chapter 2.6 --- The Hausdorff Distance Method --- p.20Chapter 2.7 --- The PCA Approach --- p.22Chapter 3 --- Binary Shape Descriptor --- p.26Chapter 3.1 --- Basic idea --- p.26Chapter 3.2 --- Standardized Binary String Descriptor --- p.27Chapter 3.3 --- Number of equivalent classes for n-gons --- p.28Chapter 4 --- The Two-Stage Framework --- p.30Chapter 5 --- Multi-Resolution Area Matching --- p.33Chapter 5.1 --- The idea --- p.33Chapter 5.2 --- Computing MRAI --- p.34Chapter 5.3 --- Measuring similarity using MRAI --- p.36Chapter 5.4 --- Query processing using MRAM --- p.38Chapter 5.5 --- Characteristics and Discussion --- p.40Chapter 6 --- Circular Error Bound and Minimum Circular Error Bound --- p.41Chapter 6.1 --- Polygon Matching using Circular Error Bound --- p.41Chapter 6.1.1 --- Translation --- p.43Chapter 6.1.2 --- Translation and uniform scaling in x-axis and y-axis directions --- p.45Chapter 6.1.3 --- Translation and independent scaling in x-axis and y-axis directions --- p.47Chapter 6.2 --- Minimum Circular Error Bound --- p.48Chapter 6.3 --- Characteristics --- p.49Chapter 7 --- Experimental Results --- p.50Chapter 7.1 --- Setup --- p.50Chapter 7.1.1 --- Polygon generation --- p.51Chapter 7.1.2 --- Database construction --- p.52Chapter 7.1.3 --- Query processing --- p.54Chapter 7.2 --- Running time comparison --- p.55Chapter 7.2.1 --- Experiment I --- p.55Chapter 7.2.2 --- Experiment II --- p.58Chapter 7.2.3 --- Experiment III --- p.60Chapter 7.3 --- Visual ranking comparison --- p.61Chapter 7.3.1 --- Experiment I --- p.61Chapter 7.3.2 --- Experiment II --- p.62Chapter 7.3.3 --- Experiment III --- p.63Chapter 7.3.4 --- Conclusion on visual ranking experiments --- p.66Chapter 8 --- Discussion --- p.68Chapter 8.1 --- N-ary Shape Descriptor --- p.68Chapter 8.2 --- Distribution of polygon equivalent classes --- p.69Chapter 8.3 --- Comparing polygons with different number of vertices --- p.72Chapter 8.4 --- Relaxation of assumptions --- p.73Chapter 8.4.1 --- Non-degenerate --- p.74Chapter 8.4.2 --- Simple --- p.74Chapter 8.4.3 --- Closed --- p.76Chapter 9 --- Conclusion --- p.78Bibliography --- p.8

Shape-based image retrieval in iconic image databases.

by Chan Yuk Ming.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 117-124).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Content-based Image Retrieval --- p.3Chapter 1.2 --- Designing a Shape-based Image Retrieval System --- p.4Chapter 1.3 --- Information on Trademark --- p.6Chapter 1.3.1 --- What is a Trademark? --- p.6Chapter 1.3.2 --- Search for Conflicting Trademarks --- p.7Chapter 1.3.3 --- Research Scope --- p.8Chapter 1.4 --- Information on Chinese Cursive Script Character --- p.9Chapter 1.5 --- Problem Definition --- p.9Chapter 1.6 --- Contributions --- p.11Chapter 1.7 --- Thesis Organization --- p.13Chapter 2 --- Literature Review --- p.14Chapter 2.1 --- Trademark Retrieval using QBIC Technology --- p.14Chapter 2.2 --- STAR --- p.16Chapter 2.3 --- ARTISAN --- p.17Chapter 2.4 --- Trademark Retrieval using a Visually Salient Feature --- p.18Chapter 2.5 --- Trademark Recognition using Closed Contours --- p.19Chapter 2.6 --- Trademark Retrieval using a Two Stage Hierarchy --- p.19Chapter 2.7 --- Logo Matching using Negative Shape Features --- p.21Chapter 2.8 --- Chapter Summary --- p.22Chapter 3 --- Background on Shape Representation and Matching --- p.24Chapter 3.1 --- Simple Geometric Features --- p.25Chapter 3.1.1 --- Circularity --- p.25Chapter 3.1.2 --- Rectangularity --- p.26Chapter 3.1.3 --- Hole Area Ratio --- p.27Chapter 3.1.4 --- Horizontal Gap Ratio --- p.27Chapter 3.1.5 --- Vertical Gap Ratio --- p.28Chapter 3.1.6 --- Central Moments --- p.28Chapter 3.1.7 --- Major Axis Orientation --- p.29Chapter 3.1.8 --- Eccentricity --- p.30Chapter 3.2 --- Fourier Descriptors --- p.30Chapter 3.3 --- Chain Codes --- p.31Chapter 3.4 --- Seven Invariant Moments --- p.33Chapter 3.5 --- Zernike Moments --- p.35Chapter 3.6 --- Edge Direction Histogram --- p.36Chapter 3.7 --- Curvature Scale Space Representation --- p.37Chapter 3.8 --- Chapter Summary --- p.39Chapter 4 --- Genetic Algorithm for Weight Assignment --- p.42Chapter 4.1 --- Genetic Algorithm (GA) --- p.42Chapter 4.1.1 --- Basic Idea --- p.43Chapter 4.1.2 --- Genetic Operators --- p.44Chapter 4.2 --- Why GA? --- p.45Chapter 4.3 --- Weight Assignment Problem --- p.46Chapter 4.3.1 --- Integration of Image Attributes --- p.46Chapter 4.4 --- Proposed Solution --- p.47Chapter 4.4.1 --- Formalization --- p.47Chapter 4.4.2 --- Proposed Genetic Algorithm --- p.43Chapter 4.5 --- Chapter Summary --- p.49Chapter 5 --- Shape-based Trademark Image Retrieval System --- p.50Chapter 5.1 --- Problems on Existing Methods --- p.50Chapter 5.1.1 --- Edge Direction Histogram --- p.51Chapter 5.1.2 --- Boundary Based Techniques --- p.52Chapter 5.2 --- Proposed Solution --- p.53Chapter 5.2.1 --- Image Preprocessing --- p.53Chapter 5.2.2 --- Automatic Feature Extraction --- p.54Chapter 5.2.3 --- Approximated Boundary --- p.55Chapter 5.2.4 --- Integration of Shape Features and Query Processing --- p.58Chapter 5.3 --- Experimental Results --- p.58Chapter 5.3.1 --- Experiment 1: Weight Assignment using Genetic Algorithm --- p.59Chapter 5.3.2 --- Experiment 2: Speed on Feature Extraction and Retrieval --- p.62Chapter 5.3.3 --- Experiment 3: Evaluation by Precision --- p.63Chapter 5.3.4 --- Experiment 4: Evaluation by Recall for Deformed Images --- p.64Chapter 5.3.5 --- Experiment 5: Evaluation by Recall for Hand Drawn Query Trademarks --- p.66Chapter 5.3.6 --- "Experiment 6: Evaluation by Recall for Rotated, Scaled and Mirrored Images" --- p.66Chapter 5.3.7 --- Experiment 7: Comparison of Different Integration Methods --- p.68Chapter 5.4 --- Chapter Summary --- p.71Chapter 6 --- Shape-based Chinese Cursive Script Character Image Retrieval System --- p.72Chapter 6.1 --- Comparison to Trademark Retrieval Problem --- p.79Chapter 6.1.1 --- Feature Selection --- p.73Chapter 6.1.2 --- Speed of System --- p.73Chapter 6.1.3 --- Variation of Style --- p.73Chapter 6.2 --- Target of the Research --- p.74Chapter 6.3 --- Proposed Solution --- p.75Chapter 6.3.1 --- Image Preprocessing --- p.75Chapter 6.3.2 --- Automatic Feature Extraction --- p.76Chapter 6.3.3 --- Thinned Image and Linearly Normalized Image --- p.76Chapter 6.3.4 --- Edge Directions --- p.77Chapter 6.3.5 --- Integration of Shape Features --- p.78Chapter 6.4 --- Experimental Results --- p.79Chapter 6.4.1 --- Experiment 8: Weight Assignment using Genetic Algorithm --- p.79Chapter 6.4.2 --- Experiment 9: Speed on Feature Extraction and Retrieval --- p.81Chapter 6.4.3 --- Experiment 10: Evaluation by Recall for Deformed Images --- p.82Chapter 6.4.4 --- Experiment 11: Evaluation by Recall for Rotated and Scaled Images --- p.83Chapter 6.4.5 --- Experiment 12: Comparison of Different Integration Methods --- p.85Chapter 6.5 --- Chapter Summary --- p.87Chapter 7 --- Conclusion --- p.88Chapter 7.1 --- Summary --- p.88Chapter 7.2 --- Future Research --- p.89Chapter 7.2.1 --- Limitations --- p.89Chapter 7.2.2 --- Future Directions --- p.90Chapter A --- A Representative Subset of Trademark Images --- p.91Chapter B --- A Representative Subset of Cursive Script Character Images --- p.93Chapter C --- Shape Feature Extraction Toolbox for Matlab V53 --- p.95Chapter C.l --- central .moment --- p.95Chapter C.2 --- centroid --- p.96Chapter C.3 --- cir --- p.96Chapter C.4 --- ess --- p.97Chapter C.5 --- css_match --- p.100Chapter C.6 --- ecc --- p.102Chapter C.7 --- edge一directions --- p.102Chapter C.8 --- fourier-d --- p.105Chapter C.9 --- gen_shape --- p.106Chapter C.10 --- hu7 --- p.108Chapter C.11 --- isclockwise --- p.109Chapter C.12 --- moment --- p.110Chapter C.13 --- normalized-moment --- p.111Chapter C.14 --- orientation --- p.111Chapter C.15 --- resample-pts --- p.112Chapter C.16 --- rectangularity --- p.113Chapter C.17 --- trace-points --- p.114Chapter C.18 --- warp-conv --- p.115Bibliography --- p.11