Saved in:
| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Heidelberg [u.a.]
Springer
2014
|
| Series: | Studies in computational intelligence
552 |
| Subjects: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=4605418&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027427974&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027427974&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Physical Description: | XVI, 198 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9783642548505 |
Staff View
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Record in the Search Index
| _version_ | 1819331440236560384 |
|---|---|
| adam_text | CONTENTS
1 ACTIVE SHAPE MODEL AND ITS APPLICATION TO FACE ALIGNMENT 1
HUCHUAN LU, FAN YANG
1 INTRODUCTION 1
2 STATISTICAL SHAPE MODELS 3
2.1 POINT DISTRIBUTION MODEL 4
2.2 MODELING LOCAL STRUCTURE 11
2.3 MULTI-RESOLUTION ACTIVE SHAPE MODEL 13
3 IMAGE SEARCH USING ACTIVE SHAPE MODEL 15
3.1 INITIAL ESTIMATE 15
3.2 COMPUTE THE MOVEMENTS OF LANDMARKS 16
3.3 EXAMPLE OF SEARCH 19
3.4 APPLICATION AND PROBLEMS 19
4 IMPROVEMENTS ON CLASSICAL ACTIVE SHAPE MODEL 21
4.1 CONSTRAINT ON B 21
4.2 WIDTH OF SEARCH PROFILE 22
4.3 LANDMARKS GROUPING 22
4.4 DIRECTION OF SEARCH PROFILE 25
4.5 SKIN-COLOR MODEL 25
5 RELATED WORK 27
6 CONCLUSIONS 29
REFERENCES 29
2 CONDITION RELAXATION IN CONDITIONAL STATISTICAL SHAPE MODELS 33
ELCO OOST, SHO TOMOSHIGE, AKINOBU SHIMIZU
1 INTRODUCTION 33
2 CONDITIONAL STATISTICAL SHAPE MODELS 36
3 THE BENEFIT OF CONDITIONAL SSMS 37
4 RELIABILITY OF THE CONDITIONAL TERM 38
5 LEVEL SET BASED CONDITIONAL SSMS 39
6 RELAXATION OF THE CONDITIONAL TERM 39
HTTP://D-NB.INFO/1047985608
XIV CONTENTS
7 EMPLOYING THE SELECTION FORMULA FOR RELAXATION 41
8 AUTOMATIC ESTIMATION OF THE RELIABILITY OF THE CONDITIONAL
FEATURES 44
9 PERFORMANCE COMPARISON OF VARIOUS CONDITIONAL SSMS 47
10 CONCLUSIONS 52
REFERENCES 53
3 INDEPENDENT COMPONENT ANALYSIS AND ITS APPLICATION TO
CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGES 57
XIANG-YAN ZENG, YEN-WEI CHEN
1 INTRODUCTION 57
2 BACKGROUND OF INDEPENDENT COMPONENT ANALYSIS 59
2.1 LINEAR TRANSFORMATION OF MULTIVARIATE DATA 59
2.2 BLIND SOURCE SEPARATION 60
2.3 INDEPENDENT COMPONENTS ANALYSIS 62
2.3.1 DATA MODEL 62
2.3.2 WHY ICA? 63
2.4 ICA ALGORITHMS 63
2.4.1 WHITENING THE DATA 63
2.4.2 ICA BY INFORMATION MAXIMIZATION 65
2.4.3 ICA BY MAXIMIZATION OF NON-GAUSSIANITY .. 67
3 ICA FOR REMOTE SENSING STUDY 70
3.1 ICA FOR HYPERSPECTRAL REMOTE SENSING 70
3.2 ICA FOR HIGH-RESOLUTION REMOTE SENSING 71
3.2.1 INDEPENDENT COMPONENTS OF RGB REMOTE
SENSING IMAGES 71
3.3 CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING
IMAGES 75
3.3.1 PIXEL CLASSIFICATION BY SPECTRAL
INFORMATION 75
3.3.2 CLASSIFICATION BY SPECTRAL INFORMATION AND
SPATIAL CONSISTENCY 76
4 CONCLUSIONS 79
REFERENCES 79
4 SUBSPACE CONSTRUCTION FROM ARTIFICIALLY GENERATED IMAGES FOR
TRAFFIC SIGN RECOGNITION 83
HIROYUKI ISHIDA, ICHIRO IDE, HIROSHI MURASE
1 INTRODUCTION TO THE GENERATIVE LEARNING 83
1.1 MODELING OF DEGRADATION CHARACTERISTICS 84
1.2 ESTIMATION OF DEGRADATION CHARACTERISTICS 84
2 GENERATIVE LEARNING FOR TRAFFIC SIGN RECOGNITION 86
2.1 GENERATION MODELS OF TRAFFIC SIGNS 86
2.2 TRAINING BY GENERATIVE LEARNING 88
2.2.1 PARAMETER ESTIMATION STEP 89
2.2.2 GENERATION OF TRAINING IMAGES 92
CONTENTS XV
3 RECOGNITION BY THE SUBSPACE METHOD 94
3.1 CONSTRUCTION OF A SUBSPACE 94
3.2 MULTIPLE FRAME INTEGRATION 95
3.3 CIRCULAR SIGN DETECTION 95
4 EXPERIMENT 96
4.1 RESULTS 99
4.2 DISCUSSION 100
5 SUMMARY 102
REFERENCES 102
5 LOCAL STRUCTURE PRESERVING BASED SUBSPACE ANALYSIS METHODS AND
APPLICATIONS 105
JIAN CHENG, HANQING LU
1 INTRODUCTION 105
2 LOCAL STRUCTURE PRESERVING 107
3 LOCAL STRUCTURE PRESERVING FOR FACE RECOGNITION 107
3.1 SUPERVISED KERNEL LOCALITY PRESERVING PROJECTIONS 108
3.2 EXPERIMENTAL RESULTS ON FACE RECOGNITION 109
4 LOCAL STRUCTURE PRESERVING FOR IMAGE CLUSTERING ILL
4.1 PLSA WITH LOCAL STRUCTURE PRESERVING ILL
4.1.1 SPARSE NEIGHBORHOOD CONSISTENCY 112
4.1.2 LOCAL WORD CONSISTENCY 113
4.1.3 THE REGULARIZED MODEL 114
4.2 MODEL FITTING 114
4.3 EXPERIMENTAL RESULTS ON IMAGE CLUSTERING 116
5 CONCLUSIONS 119
REFERENCES 119
6 SPARSE REPRESENTATION FOR IMAGE SUPER-RESOLUTION 123
XIAN-HUA HAN, YEN-WEI CHEN
1 INTRODUCTION 123
2 SPARSE CODING 126
2.1 ORTHOGONAL MATCHING PURSUIT 127
2.2 K-SVD ALGORITHM 128
3 SPARSE CODING BASED SUPER-RESOLUTION 132
4 ANALYSIS OF THE REPRESENTED FEATURES FOR LOCAL IMAGE PATCHES... 136
5 HR2LR DICTIONARY PROPAGATION OF SC 140
6 EXPERIMENTS 144
7 CONCLUSIONS 146
REFERENCES 147
7 SAMPLING AND RECOVERY OF CONTINUOUSLY-DEFINED SPARSE SIGNALS
AND ITS APPLICATIONS 151
AKIRA HIRABAYASHI
1 INTRODUCTION 151
2 SIGNALS WITH FINITE RATE OF INNOVATION AS AN EXTENSION OF
BAND-LIMITED SIGNALS 153
XVI
CONTENTS
3 SAMPLING AND RECOVERY OF THE SEQUENCE OF DIRACS 155
3.1 NOISELESS CASE 155
3.2 CADZOW DENOISING 158
3.3 MAXIMUM LIKELIHOOD ESTIMATION 159
4 SAMPLING AND RECOVERY OF SIGNALS OF PIECEWISE POLYNOMIALS.... 161
5 APPLICATION TO IMAGE FEATURE EXTRACTION 164
6 CONCLUSION 169
REFERENCES 169
8 TENSOR-BASED SUBSPACE LEARNING FOR MULTI-POSE FACE SYNTHESIS 171
XU QIAO, TAKANORI IGARASHI, YEN-WEI CHEN
1 INTRODUCTION 171
2 TENSOR AND MULTILINEAR ALGEBRA FOUNDATIONS 173
2.1 DEFINITIONS AND PRELIMINARIES 173
2.1.1 TENSOR DEFINITIONS 173
2.1.2 TENSOR NORM AND RANK 174
2.1.3 SYMMETRY AND DIAGONAL TENSORS 175
2.1.4 MATRICIZATION OF TENSORS 176
2.1.5 TENSOR MULTIPLICATION: THE N-MODE
PRODUCT 176
2.1.6 MATRIX PRODUCT 177
2.2 TENSOR DECOMPOSITION 178
2.2.1 TUCKER DECOMPOSITION 178
2.2.2 CANDECOMP/PARAFAC
DECOMPOSITION 180
2.2.3 OTHER DECOMPOSITIONS 180
3 TENSOR-BASED SUBSPACE LEARNING ALGORITHM 181
3.1 IMAGE REPRESENTATION 181
3.2 TENSOR SUBSPACE BUILDING 181
3.3 SYNTHESIS PROCEDURE 183
4 EXPERIMENTS AND RESULTS 185
4.1 DATA 185
4.2 IMAGE DEFORMATION 185
4.3 DATA COMPRESSION 185
4.4 SYNTHESIS RESULT AND EVALUATION 187
5 CONCLUSION 192
REFERENCES 192
9 EDITORS 197
10 AUTHOR INDEX 199
Subspace Methods for Pattern Recognition in Intelligent
Environment
Ulis
research book provides a comprehensive overview of the state-of-the-art subspace
learning methods for pattern recognition in intelligent environment. With the fast
development of internet and computer technologies, the amount of available data is
rapidi}
increasing in our daily life. How to extract core information or useful features
is an important issue. Subspace methods are widely used tor dimension reduction and
feature extraction in pattern recognition. They transform high dimensional data to a
lower dimensional space (subspace), that focuses on the relevant information only. The
book covers a broad spectrum of subspace methods including linear, nonlinear and
multilinear subspace learning methods and applications. The applications include
tace
alignment, face recognition, medical image analysis, remote sensing image classification,
traffic sign recognition, image clustering, super resolution, edge detection, multi-view
facial image svnlhesis.
|
| any_adam_object | 1 |
| author2 | Chen, Yen-Wei |
| author2_role | edt |
| author2_variant | y w c ywc |
| author_facet | Chen, Yen-Wei |
| building | Verbundindex |
| bvnumber | BV041985621 |
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| dewey-full | 006.42 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.42 |
| dewey-search | 006.42 |
| dewey-sort | 16.42 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| genre_facet | Aufsatzsammlung |
| id | DE-604.BV041985621 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:59:28Z |
| institution | BVB |
| isbn | 9783642548505 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027427974 |
| oclc_num | 871590065 |
| open_access_boolean | |
| owner | DE-739 DE-473 DE-BY-UBG |
| owner_facet | DE-739 DE-473 DE-BY-UBG |
| physical | XVI, 198 S. Ill., graph. Darst. 24 cm |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Subspace methods for pattern recognition in intelligent environment Studies in computational intelligence Mustererkennung (DE-588)4040936-3 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Dimensionsreduktion (DE-588)4224279-4 gnd Hochdimensionale Daten (DE-588)7862975-5 gnd |
| subject_GND | (DE-588)4040936-3 (DE-588)4314440-8 (DE-588)4224279-4 (DE-588)7862975-5 (DE-588)4143413-4 |
| title | Subspace methods for pattern recognition in intelligent environment |
| title_auth | Subspace methods for pattern recognition in intelligent environment |
| title_exact_search | Subspace methods for pattern recognition in intelligent environment |
| title_full | Subspace methods for pattern recognition in intelligent environment YenWei Chen ... eds. |
| title_fullStr | Subspace methods for pattern recognition in intelligent environment YenWei Chen ... eds. |
| title_full_unstemmed | Subspace methods for pattern recognition in intelligent environment YenWei Chen ... eds. |
| title_short | Subspace methods for pattern recognition in intelligent environment |
| title_sort | subspace methods for pattern recognition in intelligent environment |
| topic | Mustererkennung (DE-588)4040936-3 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Dimensionsreduktion (DE-588)4224279-4 gnd Hochdimensionale Daten (DE-588)7862975-5 gnd |
| topic_facet | Mustererkennung Merkmalsextraktion Dimensionsreduktion Hochdimensionale Daten Aufsatzsammlung |
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