Face Image Analysis by Unsupervised LearningSpringer Science & Business Media, 2001. gada 30. jūn. - 173 lappuses Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry. |
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1.–5. rezultāts no 93.
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Marian Stewart Bartlett. by Unsupervised Learning ***すます。ま 11000 Marian Stewart Bartlett Foreword by Terrence J. Sejnowski Kluwer Academic Publishers FACE IMAGE ANALYSIS BY UNSUPERVISED LEARNING This One Z5Y7 -. Face Image Analysis ...
Marian Stewart Bartlett. by Unsupervised Learning ***すます。ま 11000 Marian Stewart Bartlett Foreword by Terrence J. Sejnowski Kluwer Academic Publishers FACE IMAGE ANALYSIS BY UNSUPERVISED LEARNING This One Z5Y7 -. Face Image Analysis ...
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Marian Stewart Bartlett. THE KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE FACE IMAGE ANALYSIS BY UNSUPERVISED LEARNING by Marian Stewart Bartlett.
Marian Stewart Bartlett. THE KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE FACE IMAGE ANALYSIS BY UNSUPERVISED LEARNING by Marian Stewart Bartlett.
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Marian Stewart Bartlett. FACE IMAGE ANALYSIS BY UNSUPERVISED LEARNING by Marian Stewart Bartlett Institute for Neural Computation University of California , San Diego , U.S.A. KLUWER ACADEMIC PUBLISHERS Boston / Dordrecht / London ...
Marian Stewart Bartlett. FACE IMAGE ANALYSIS BY UNSUPERVISED LEARNING by Marian Stewart Bartlett Institute for Neural Computation University of California , San Diego , U.S.A. KLUWER ACADEMIC PUBLISHERS Boston / Dordrecht / London ...
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... Bartlett , Marian Stewart . Face image analysis by unsupervised learning / by Marian Stewart Bartlett . p . cm . -- ( The Kluwer international series in engineering and computer science ; SECS 612 ) Includes bibliographical references ...
... Bartlett , Marian Stewart . Face image analysis by unsupervised learning / by Marian Stewart Bartlett . p . cm . -- ( The Kluwer international series in engineering and computer science ; SECS 612 ) Includes bibliographical references ...
. lappuse
Marian Stewart Bartlett. This book is dedicated to Nigel . 2.1.4 2.1.5 Principal component analysis 1. SUMMARY 2. INTRODUCTION 2.1.
Marian Stewart Bartlett. This book is dedicated to Nigel . 2.1.4 2.1.5 Principal component analysis 1. SUMMARY 2. INTRODUCTION 2.1.
Saturs
SUMMARY | |
INTRODUCTION | 3 |
211 Generative models | 4 |
212 Redundancy reduction as an organizational principle | 6 |
213 Information theory | 7 |
214 Redundancy reduction in the visual system | 9 |
215 Principal component analysis | 10 |
216 Hebbian learning | 11 |
45 Overview of approach | 79 |
IMAGE REPRESENTATIONS FOR FACIAL EXPRESSION ANALYSIS COMPARATIVE STUDY I | 81 |
51 Image database | 82 |
52 Image analysis methods | 83 |
522 Feature measurement | 85 |
523 Optic flow | 86 |
524 Human subjects | 88 |
53 Results | 89 |
217 Explicit discovery of statistical dependencies | 13 |
22 Independent component analysis | 15 |
222 Information maximization learning rule | 16 |
223 Relation of sparse coding to independence | 20 |
23 Unsupervised learning in visual development | 22 |
232 Models of receptive field development based on correlation sensitive learning mechanisms | 24 |
24 Learning invariances from temporal dependencies in the input | 27 |
242 Temporal association in psychophysics and biology | 30 |
25 Computational Algorithms for Recognizing Faces in Images | 31 |
INDEPENDENT COMPONENT REPRESENTATIONS FOR FACE RECOGNITION | 37 |
311 Independent component analysis ICA | 40 |
312 Image data | 42 |
32 Statistically independent basis images | 43 |
Architecture 1 | 44 |
Architecture 1 | 46 |
33 A factorial face code | 51 |
Architecture 2 | 52 |
Architecture 2 | 54 |
34 Examination of the ICA Representation | 57 |
342 Sparseness | 58 |
35 Combined ICA recognition system | 60 |
36 Discussion | 61 |
AUTOMATED FACIAL EXPRESSION ANALYSIS | 67 |
412 Featurebased approaches | 69 |
413 Modelbased techniques | 70 |
414 Holistic analysis | 71 |
42 What is needed | 72 |
43 The Facial Action Coding System FACS | 73 |
531 Hybrid system | 91 |
532 Error analysis | 92 |
54 Discussion | 94 |
IMAGE REPRESENTATIONS FOR FACIAL EXPRESSION ANALYSIS COMPARATIVE STUDY II | 99 |
62 Image database | 101 |
63 Optic flow analysis | 103 |
633 Classification procedure | 104 |
64 Holistic analysis | 106 |
642 Local feature analysis LFA | 107 |
643 FisherActions | 110 |
644 Independent component analysis | 112 |
65 Local representations | 115 |
652 Gabor wavelet representation | 117 |
653 PCA jets | 118 |
66 Human subjects | 120 |
67 Discussion | 121 |
68 Conclusion | 125 |
LEARNING VIEWPOINT INVARIANT REPRESENTATIONS OF FACES | 127 |
72 Simulation | 131 |
721 Model architecture | 132 |
723 Temporal association in an attractor network | 135 |
724 Simulation results | 138 |
73 Discussion | 145 |
CONCLUSIONS AND FUTURE DIRECTIONS | 149 |
References | 155 |
Index | 169 |
Citi izdevumi - Skatīt visu
Face Image Analysis by Unsupervised Learning Marian Stewart Bartlett Ierobežota priekšskatīšana - 2012 |
Face Image Analysis by Unsupervised Learning Marian Stewart Bartlett Priekšskatījums nav pieejams - 2012 |
Bieži izmantoti vārdi un frāzes
Action Coding System approach Architecture attractor network automated Barlow Bartlett based on principal basis functions Bell and Sejnowski cells classification performance coefficients correlations covariance dataset eigenfaces eigenvectors Ekman encoding entropy estimate face recognition face representations Facial Action Coding facial expression analysis FACS feature measurements feature-based feedforward Figure flow fields Gabor filters Gabor wavelet Gaussian graylevel images Hebbian learning high-order dependencies human subjects ICA algorithm ICA representations image set independent component analysis individual information maximization input patterns joint entropy kernels layer learning rule matrix motion Movellan mutual information neural neurons object obtained ocular dominance optic flow orientation output unit Padgett and Cottrell parameters Penev and Atick Pentland principal component analysis receptive fields recognizing facial redundancy reduction representations based representations of faces second-order sequence signals sparse codes statistically independent synaptic temporal associations test images Test Set unit activities unsupervised learning viewpoint invariant views weight vectors
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