Computer vision /:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hauppauge, N.Y. :
Nova Science Publishers, Inc.,
[2011]
|
| Schriftenreihe: | Computer science, technology and applications.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 1 online resource : illustrations (some color). |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781620817018 1620817012 |
Internformat
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| 300 | |a 1 online resource : |b illustrations (some color). | ||
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| 490 | 1 | |a Computer science, technology and applications | |
| 504 | |a Includes bibliographical references and index. | ||
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| 546 | |a English. | ||
| 505 | 0 | |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. | |
| 505 | 8 | |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. | |
| 505 | 8 | |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. | |
| 505 | 8 | |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. | |
| 505 | 8 | |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. | |
| 505 | 8 | |a 4.6.3. Detection Stage -- Attention System. | |
| 650 | 0 | |a Computer vision. |0 http://id.loc.gov/authorities/subjects/sh85029549 | |
| 650 | 6 | |a Vision par ordinateur. | |
| 650 | 7 | |a COMPUTERS |x Computer Vision & Pattern Recognition. |2 bisacsh | |
| 650 | 7 | |a Computer vision |2 fast | |
| 700 | 1 | |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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| contents | 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. 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. 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. 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. 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. 4.6.3. Detection Stage -- Attention System. |
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| discipline | Informatik |
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| id | ZDB-4-EBA-on1162241136 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:29:57Z |
| institution | BVB |
| isbn | 9781620817018 1620817012 |
| language | English |
| lccn | 2020686885 |
| oclc_num | 1162241136 |
| open_access_boolean | |
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| physical | 1 online resource : illustrations (some color). |
| psigel | ZDB-4-EBA |
| publishDate | 2011 |
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| publisher | Nova Science Publishers, Inc., |
| record_format | marc |
| series | Computer science, technology and applications. |
| series2 | Computer science, technology and applications |
| spelling | Computer vision / Sota R. Yoshida, editor. Hauppauge, N.Y. : Nova Science Publishers, Inc., [2011] 1 online resource : illustrations (some color). text txt rdacontent computer c rdamedia online resource cr rdacarrier Computer science, technology and applications Includes bibliographical references and index. Description based on print version record. English. 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. 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. 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. 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. 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. 4.6.3. Detection Stage -- Attention System. Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Vision par ordinateur. COMPUTERS Computer Vision & Pattern Recognition. bisacsh Computer vision fast Yoshida, Sota R., editor. Print version: Computer vision Hauppauge, N.Y. : Nova Science, c2011. 9781612093994 (hardcover) (DLC) 2010054279 Computer science, technology and applications. http://id.loc.gov/authorities/names/no2010162081 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=541358 Volltext |
| spellingShingle | Computer vision / Computer science, technology and applications. 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. 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. 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. 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. 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. 4.6.3. Detection Stage -- Attention System. Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Vision par ordinateur. COMPUTERS Computer Vision & Pattern Recognition. bisacsh Computer vision fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85029549 |
| title | Computer vision / |
| title_auth | Computer vision / |
| title_exact_search | Computer vision / |
| title_full | Computer vision / Sota R. Yoshida, editor. |
| title_fullStr | Computer vision / Sota R. Yoshida, editor. |
| title_full_unstemmed | Computer vision / Sota R. Yoshida, editor. |
| title_short | Computer vision / |
| title_sort | computer vision |
| topic | Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Vision par ordinateur. COMPUTERS Computer Vision & Pattern Recognition. bisacsh Computer vision fast |
| topic_facet | Computer vision. Vision par ordinateur. COMPUTERS Computer Vision & Pattern Recognition. Computer vision |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=541358 |
| work_keys_str_mv | AT yoshidasotar computervision |