Computer vision /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Yoshida, Sota R. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hauppauge, N.Y. : Nova Science Publishers, Inc., [2011]
Colección:Computer science, technology and applications.
Temas:
Computer vision.
Vision par ordinateur.
COMPUTERS > Computer Vision & Pattern Recognition.
Computer vision
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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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