Artificial Neural Networks for Computer Vision

Zhou, Yi-Tong; Chellappa, Rama

ISBN 10: 0387976833 / ISBN 13: 9780387976839

Editore: Springer, 1991

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Dati bibliografici

• Titolo: Artificial Neural Networks for Computer Vision
• Editore: Springer
• Data di pubblicazione: 1991
• Lingua: Inglese
• ISBN 10: 0387976833
• ISBN 13: 9780387976839
• Rilegatura: Brossura
• Condizione: As New

Descrizione articolo

Riassunto

This book describes artificial neural network (ANN) based algorithms for early vision. The book focuses on several important early vision problems such as static and motion stereo, motion estimation and restoration, and emphasizes finding effective solutions to these problems, using the ANN. Many practical real time image data are provided. Although the book is written for researchers and engineers, it provides a fairly complete and readable introduction to both neural networks and computer vision. Readers can also expect to derive a better understanding of the interaction between these two fields.

Contenuti

1 Introduction.- 1.1 Neural Methods.- 1.2 Plan of the Book.- 2 Computational Neural Networks.- 2.1 Introduction.- 2.2 Amari and Hopfield Networks.- 2.3 A Discrete Neural Network for Vision.- 2.3.1 A Discrete Network.- 2.3.2 Decision Rules.- 2.4 Discussion.- 3 Static Stereo.- 3.1 Introduction.- 3.2 Depth from Two Views.- 3.3 Estimation of Intensity Derivatives.- 3.3.1 Fitting Data Using Chebyshev Polynomials.- 3.3.2 Analysis of Filter M(y).- 3.3.3 Computational Consideration for the Natural Images.- 3.4 Matching Using a Network.- 3.5 Experimental Results.- 3.5.1 Random Dot Stereograms.- 3.5.2 Natural Stereo Images.- 3.6 Discussion.- 4 Motion Stereo—Lateral Motion.- 4.1 Introduction.- 4.2 Depth from Lateral Motion.- 4.3 Estimation of Measurement Primitives.- 4.3.1 Estimation of Derivatives.- 4.3.2 Estimation of Chamfer Distance Values.- 4.4 Batch Approach.- 4.4.1 Estimation of Pixel Positions.- 4.4.2 Batch Formulation.- 4.5 Recursive Approach.- 4.6 Matching Error.- 4.7 Detection of Occluding Pixels.- 4.8 Experimental Results.- 4.9 Discussion.- 5 Motion Stereo—Longitudinal Motion.- 5.1 Introduction.- 5.2 Depth from Forward Motion.- 5.2.1 General Case: Images Are Nonequally Spaced.- 5.2.2 Special Case: Images Are Equally Spaced.- 5.3 Estimation of the Gabor Features.- 5.3.1 Gabor Correlation Operator.- 5.3.2 Computational Considerations.- 5.4 Neural Network Formulation.- 5.5 Experimental Results.- 5.6 Discussion.- 6 Computation of Optical Flow.- 6.1 Introduction.- 6.2 Estimation of Intensity Values and Principal Curvatures.- 6.2.1 Estimation of Polynomial Coefficients.- 6.2.2 Computing Principal Curvatures.- 6.2.3 Analysis of Filters.- 6.3 Neural Network Formulation.- 6.3.1 Physiological Considerations.- 6.3.2 Computational Considerations.- 6.3.3 Computing Flow Field.- 6.4 Detection of Motion Discontinuities.- 6.5 Multiple Frame Approaches.- 6.5.1 Batch Approach.- 6.5.2 Recursive Algorithm.- 6.5.3 Detection Rules.- 6.6 Experimental Results.- 6.6.1 Synthetic Image Sequence.- 6.6.2 Natural Image Sequence.- 6.7 Discussion.- 7 Image Restoration.- 7.1 Introduction.- 7.2 An Image Degradation Model.- 7.3 Image Representation.- 7.4 Estimation of Model Parameters.- 7.5 Restoration.- 7.6 A Practical Algorithm.- 7.7 Computer Simulations.- 7.8 Choosing Boundary Values.- 7.9 Comparisons to Other Restoration Methods.- 7.9.1 Inverse Filter and SVD Pseudoinverse Filter.- 7.9.2 MMSE and Modified MMSE Filters.- 7.10 Optical Implementation.- 7.11 Discussion.- 8 Conclusions and Future Research.- 8.1 Conclusions.- 8.2 Future Research.