Relational MatchingSpringer Science & Business Media, 10 Eyl 1992 - 190 sayfa Relational matching is a method for finding the best correspondences betweenstructural descriptions. It is widely used in computer vision for the recognition and location of objects in digital images. For this purpose, the digital images and the object models are represented by structural descriptions. The matching algorithm then has to determine which image elements and object model parts correspond. This book is the result of abasic study of relational matching. The book focuses particularly on the evaluation of correspondences. In order to find the best match, one needs a measure to evaluate the quality of a match. The author reviews the evaluation measures that have been suggested over the past few decades and presents a new measure based on information theory. The resulting theorycombines matching strategies, information theory, and tree search methods. For the benefit of the reader, comprehensive introductions are given to all these topics. |
İçindekiler
Introduction to relational matching | 1 |
12 Relational matching theory | 2 |
13 Organization of the thesis | 3 |
A classification of matching methods | 5 |
212 Relations | 6 |
213 The image as a function of coordinates | 7 |
22 The match evaluation function | 8 |
222 Constraints | 11 |
642 Advantages of the mutual information | 81 |
643 Mutual information of uncertain attributes | 83 |
644 Mutual information as a compatibility function for relaxation processes | 84 |
Tree search methods and heuristics | 87 |
71 Problem representations in a tree | 88 |
72 Tree search methods | 91 |
722 Informed search methods | 94 |
723 Informed search with a merit function | 99 |
23 Search methods | 12 |
231 Tree search | 13 |
232 Simulated annealing | 15 |
234 Least squares | 17 |
235 Relaxation labeling | 18 |
24 Hierarchy | 20 |
25 Examples | 22 |
252 3Dimensional object recognition with relational matching | 23 |
253 Stereo matching with simulated annealing | 25 |
255 Least squares area based matching | 26 |
256 Least squares feature based matching | 28 |
257 Scene interpretation by relaxation labeling | 30 |
258 Computational models of human stereo vision | 31 |
26 Discussion | 32 |
Formal description of relational matching | 35 |
32 Compositions | 37 |
33 Exact matching | 38 |
34 Inexact matching | 41 |
35 Tree search | 42 |
36 Some problems using relational matching | 43 |
Problem definition and contributions of the thesis | 45 |
42 Tree search methods | 47 |
Theory of relational matching | 49 |
51 Information measures for discrete signals | 51 |
52 Information measures for continuous signals | 54 |
53 The minimum description length principle | 57 |
532 Interpretation of noisy point distributions | 59 |
533 Relation to maximum a posteriori and maximum likelihood estimation | 61 |
54 Discretization of continuous signals | 62 |
Evaluation of mappings between relational descriptions | 67 |
62 Mapping as an information channel | 69 |
622 Modeling the transfer of information over a communication channel | 71 |
63 The conditional information as a distance function after Boyer and Kak | 73 |
633 Analysis of the conditional information as an evaluation function | 74 |
64 The mutual information as a merit function | 79 |
641 A probabilistic view | 80 |
73 Checking consistency of future instantiations | 101 |
731 Forward checking and looking ahead | 102 |
732 Relational matching with forward checking | 104 |
74 Unit ordering | 105 |
75 The necessity of stop criteria for the correspondence problem | 107 |
752 Perceptual grouping of primitives | 109 |
Object location by relational matching | 111 |
Relational image and model description | 112 |
82 Extraction of image features | 115 |
822 Line extraction | 116 |
823 Region extraction | 117 |
824 Symbolic postprocessing | 118 |
83 Used primitives and relations and their attributes | 121 |
Evaluation functions for object location | 123 |
911 Mutual information of line length measurements | 124 |
912 Mutual information of angle measurements | 129 |
913 Mutual information determined from training matches | 131 |
92 The mutual information of the spatial resection | 134 |
93 Construction of the evaluation function for object location | 135 |
941 Reliability of transformation parameters | 136 |
942 A statistical test on the amount of support | 138 |
Strategy and performance of the tree search for object location | 143 |
101 Estimation of the future merit | 144 |
102 Heuristics for object location | 146 |
1022 The usefulness of a known transformation | 148 |
1023 Underestimation of future merit | 150 |
103 Description of the objects and their images | 152 |
104 Performance of the object location | 155 |
Summary and discussion | 163 |
Literature | 169 |
Mutual information between a continuous signal and a discretized noisy observation | 181 |
Distribution of the coordinates of points on a sphere | 185 |
Conditional probability density function of the image line length | 187 |
Diğer baskılar - Tümünü görüntüle
Sık kullanılan terimler ve kelime öbekleri
algorithm Amax Amin angle Artificial Intelligence attribute value best mapping best-first search Boyer breadth-first search calculated Computer Vision conditional information conditional probability constraints contour coordinates correspondence problem corresponding primitives cost function current node defined depth-first depth-first search described discretization distance distribution edges elliptic estimate evaluation function expanded extraction Figure Förstner forward checking future merit future units gradient graph grey value images Haralick heuristic Hough transformation image descriptions image line length instantiation interval leaf node least squares matching problems measure merit function minimum description length model line mutual information N-queens problem node stack object location problem object models object recognition optimal p(lillm path Photogrammetry pixels primitives and relations probability density function Proceedings relation tuples relational descriptions relational matching method relaxation labeling root node search space search tree simulated annealing solution stereo strategy symbol transformation parameters tree search methods unit-label pairs wildcard
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