Applied Pattern Recognition

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Horst Bunke, Abraham Kandel, Mark Last
Springer Science & Business Media, Apr 11, 2008 - Mathematics - 246 pages
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A sharp increase in the computing power of modern computers, accompanied by a decrease in the data storage costs, has triggered the development of extremely powerful algorithms that can analyze complex patterns in large amounts of data within a very short period of time. Consequently, it has become possible to apply pattern recognition techniques to new tasks characterized by tight real-time requirements (e.g., person identification) and/or high complexity of raw data (e.g., clustering trajectories of mobile objects). The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains.

 

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Contents

Skinbased Face DetectionExtraction and Recognition of Facial Expressions
3
2 Neural color constancy based skin detection
4
3 Image segmentation
5
4 Local region graph Facial feature region graph
6
41 Facial feature matching using local graph
7
5 Skin region synthesis
9
6 Matching multiple regions with Local Global LG Graph method
11
61 Image Representation with Local Global LG Graph
12
212 Summary of related work
116
32 Defining a new similarity measure
119
33 Clustering trajectories with the KMeans Algorithm
120
34 Using incremental approach for clustering
121
5 Evaluation Experiments
122
52 Detailed Results
123
6 Conclusions
125
References
126

62 LG Graph matching
13
7 Experimental results
18
72 Effect of the missing facial features nodes and partial LG graph matching
19
8 Extending LG Graph method for recognizing facial expressions
20
9 Conclusions
22
References
23
Facial Image Processing
29
2 Removal of glasses
31
3 Facial expression synthesis
34
4 Eye synthesis
36
5 Redeye removal
40
6 Restoration of facial images
42
8 Facial weight change
43
References
45
Face Recognition and Pose Estimation with Parametric Linear Subspaces
49
2 Problem Overview and Definitions
52
22 PoseInsensitive Face Recognition
53
3 Parametric Linear Subspace Model
56
32 Parametric Piecewise Linear Subspace Model
61
4 Interpersonalized Pose Estimation
64
5 Experiments
65
52 Personalized Pose Estimation and View Synthesis
66
53 PoseInsensitive Face Recognition
69
54 Interpersonalized Pose Estimation
70
6 Conclusion
71
References
72
SpatioTemporal Patterns
75
4D Segmentation of Cardiac Data Using Active Surfaces with Spatiotemporal Shape Priors
76
1 Introduction
78
2 Method
81
22 Adding time to shapebased segmentation methods
82
23 Generating Training Data
84
24 Segmentation
88
3 Periodic data
90
41 Consistency Tests
91
42 Accuracy Tests
96
5 Conclusion
99
Measuring Similarity Between Trajectories of Mobile Objects
101
2 Related Work
103
22 Representing spatiotemporal data
104
23 Spatiotemporal data summarization
106
24 Querying spatiotemporal data
107
25 Indexing trajectories of moving objects
108
26 Clustering moving objects and trajectories
110
27 Spatiotemporal group patterns mining
111
28 Incremental maintenance of mobile patterns
113
210 Spatiotemporal similarity measures
114
211 Spatiotemporal data generation
115
GraphBased Methods
129
Matching of Hypergraphs Algorithms Applications and Experiments
131
2 Preliminaries
133
3 Hypergraph Matching
137
4 Algorithms for Hypergraph Matching
140
5 Experimental Results
144
52 Experiments on Pseudoreal and Real Data
147
6 Conclusions
152
References
153
FeatureDriven Emergence of Model Graphs for Object Recognition and Categorization
155
1 Introduction
156
2 Learning Set Partitionings and Categories
158
3 Parquet Graphs
162
31 Similarity Function
164
4 Learning a Visual Dictionary
165
42 Feature Vectors
167
5 Preselection Network
168
51 Neural Model
170
53 Weighting of Feature Detectors
171
54 Neurons Connectivity and Synaptic Weights
172
55 Saliencies
175
6 Verification of Model Candidates
177
62 Matching
181
63 Model Selection
182
71 Object Recognition
183
72 Object Categorization
191
8 Summary and Future Work
196
References
197
Special Applications
200
A Waveletbased Statistical Method for Chinese Writer Identification
203
TwoDimensional Gabor Model
206
3 Our Algorithm for Writer Identification
207
31 Preprocessing
208
32 Feature Extraction Based on Wavelet
209
33 Similarity Measurement
214
4 Experiments
215
42 Identification performance evaluation 2
217
5 Conclusions
219
Texture Analysis by Accurate Identification of a Generic MarkovGibbs Model
221
2 Identification of a generic MarkovGibbs model
222
22 Generic MGRF with pairwise interaction
223
23 Accurate first approximation of potentials
224
24 Modelbased interaction maps MBIM
228
3 Characteristic Structure and Texels
232
4 Texture Synthesis by Bunch Sampling
236
5 Comparisons and conclusions
237
References
244
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