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EBSCO_on1162241136 |
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20231017213018.0 |
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110127s2011 nyua ob 001 0 eng |
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|a 2020686885
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|a 006.3/7
|2 22
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| 049 |
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|a UAMI
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| 245 |
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|a Computer vision /
|c Sota R. Yoshida, editor.
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| 264 |
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|a Hauppauge, N.Y. :
|b Nova Science Publishers, Inc.,
|c [2011]
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| 300 |
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|a 1 online resource :
|b illustrations (some color).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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| 490 |
1 |
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|a Computer science, technology and applications
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| 504 |
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|a Includes bibliographical references and index.
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| 588 |
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|a Description based on print version record.
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| 546 |
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|a English.
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0 |
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|a PREFACE ; SOME APPLICATIONS OF COMPUTER VISION SYSTEMS IN MICROMECHANICS ; ABSTRACT ; I. TEXTURE RECOGNITION TASK ; 1.1 Introduction ; 1.2 Metal surface texture recognition ; 1.3 The LIRA neural classifier ; 1.3.1. Image coding ; 1.3.2 Training procedure ; 1.3.3 Recognition procedure ; 1.4 Results ; 1.5 Discussion ; 1.6. Conclusion ; II. TASK OF SHAPE RECOGNITION OF SMALL SCREWS ; 2.1 Introduction ; 2.2 Neural Classifier LIRA_grayscale ; 2.2.1 Architecture ; 2.2.2 Layer interconnections and neuron activation ; 2.2.3 Learning method.
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| 505 |
8 |
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|a 2.2.3.1. Initial phase. 2.2.3.2. Winner selection scheme. ; 2.2.3.3. Weights adaptation. ; 2.2.4. Improvements in learning process ; 2.3 Results ; 2.4 Conclusion ; III. TASK OF MICRO WORK PIECE RECOGNITION WITH LIRA NEURAL CLASSIFIER ; 3.1 Introduction ; 3.2 State of the art and related works ; 3.2.1. Pattern matching method ; 3.2.2. Principal components analysis method ; 3.2.3. Graph matching method ; 3.2.4. Generalized Hough transform (GHT) ; 3.3 Technical Vision Subsystem (TVS) ; 3.4. Neural classi er ; 3.4.1. The structure ; 3.4.2. Training process.
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| 505 |
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|a 3.4.3. Distortions 3.5 Software and databases ; 3.5.1. The developed software ; 3.5.2. Databases ; 3.5.3. Chosen work pieces ; 3.5.4. Databases description ; 3. 6. Experiments and results ; 3.6.1. Work piece recognition with grey scale images ; 3.6.2. Experiments with distortions ; 3.6.3. Work piece recognition in contour images ; 3.6.4. Experiments with database II and position recognition ; 3.6.5. Position recognition ; 3.7 Discussion ; 3.8 Conclusion ; ACKNOWLEDGMENT ; REFERENCES ; A SURVEY OF FACE RECOGNITION BY THE GENETIC ALGORITHM ; ABSTRACT.
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| 505 |
8 |
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|a INTRODUCTION THEORY AND EXPERIMENTAL SETTING ; A. Genetic encoding ; B. Genetic algorithm and decoding ; C. Experimental setting ; EXPERIMENT AND COMPARISON ; CONCLUSION ; ACKNOWLEDGMENT ; REFERENCES ; THE ATTENTIVE CO-PILOT: ROBUST DRIVER ASSISTANCE RELYING ON HUMAN-LIKE SIGNAL PROCESSING PRINCIPLES; Abstract; 1. Introduction into Advanced Driver Assistance Systems; 2. RelatedWork Advanced Driver Assistance; 3. The "Attentive Co-Pilot" -- System Description; 4. Visual Attention Sub-System; 4.1. RelatedWork; 4.2. Attention Features; 4.2.1. Intensity Feature; BiologicalMotivation.
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| 505 |
8 |
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|a Parameterization of the DoG KernelDiscussion; 4.2.2. Orientation Feature; BiologicalMotivation; Parameterization of the Gabor Kernel; Conceptional Extensions; Discussion; 4.2.3. RGBY Color Space; BiologicalMotivation; Computation of RGBY Colors; Conceptional Extensions; Discussion; 4.3. Real-World Challenges for Top-Down Attention Systems; 4.4. Attention System Description; 4.5. Functional Comparison to Other Top-Down AttentionModels; 4.6. Application notes: Attention-based Recognition of Traf c Signs; 4.6.1. RelatedWork -- Detection Phase; 4.6.2. RelatedWork -- Classi cation Phase.
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| 505 |
8 |
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|a 4.6.3. Detection Stage -- Attention System.
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| 590 |
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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| 650 |
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0 |
|a Computer vision.
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| 650 |
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6 |
|a Vision par ordinateur.
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| 650 |
|
7 |
|a COMPUTERS
|x Computer Vision & Pattern Recognition.
|2 bisacsh
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| 650 |
|
7 |
|a Computer vision
|2 fast
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| 700 |
1 |
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|a Yoshida, Sota R.,
|e editor.
|
| 776 |
0 |
8 |
|i Print version:
|t Computer vision
|d Hauppauge, N.Y. : Nova Science, c2011.
|z 9781612093994 (hardcover)
|w (DLC) 2010054279
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|a Computer science, technology and applications.
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