Relational Matching Book

I	Introduction to relational matching
1	Computer Vision and Matching
1.1	Correspondence problems	1
1.2	Relational matching theory	2
1.3	Organization of the thesis	3
2	A classification of matching methods
2.1	Data description	5
2.2	The match evaluation function	8
2.3	Search methods	12
2.4	Hierarchy	20
2.5	Examples	22
2.6	Discussion	32
3	Formal description of relational matching
3.1	Definition of relational description	35
3.2	Compositions	37
3.3	Exact matching	38
3.4	Inexact matching	41
3.5	Tree search	42
3.8	Some problems using relational matching	43
4	Problem definition and contributions of the thesis
4.1	Evaluation of mappings	45
4.2	Tree search methods	47
II	Theory of relational matching
5	Information theory: selected topics
5.1	Information measures for discrete signals	51
5.2	Information measures for continuous signals	54
5.3	The minimum description length principle	57
5.4	Discretization of continuous signals	62
6	Evaluation of mappings between relational descriptions
6.1	Two traditional distance measures on graphs and relations	67
6.2	Mapping as an information channel	69
6.3	The conditional information as a distance function (after Boyer and Kak)	73
6.4	The mutual information as a merit function	79
7	Tree search methods and heuristics
7.1	Problem representations in a tree	88
7.2	Tree search methods	91
7.3	Checking consistency of future instantiations	101
7.4	Unit ordering	105
7.5	The necessity of stop criteria for the correspondence problem	107
III	Object location by relational matching
8	Relational image and model description
8.1	Image segmentation techniques	112
8.2	Extraction of image features	115
8.3	Used primitives and relations and their attributes	121
9	Evaluation functions for object location
9.1	Composing the mutual information tables	123
9.2	The mutual information of the spatial resection	134
9.3	Construction of the evaluation function for object location	135
9.4	Functions for the self-diagnosis of object location	135
10	Strategy and performance of the tree search for object location
10.1	Estimation of the future merit	144
10.2	Heuristics for object location	146
10.3	Description of the objects and their images	152
10.4	Performance of the object location	155
11	Summary and discussion	163
	Literature	169
	A: Mutual information between a continuous signal and a discretized noisy observation	181
	B: Distribution of the coordinates of points on a sphere	185
	C: Conditional probability density function of the image line length	187
	D: Tables with search results	189