Gösta H. Granlund, Hans Knutsson

Signal Processing for Computer Vision

• Gebundenes Buch

Produktbeschreibung

Signal Processing for Computer Vision is a unique and thorough treatment of the signal processing aspects of filters and operators for low-level computer vision.
Computer vision has progressed considerably over recent years. From methods only applicable to simple images, it has developed to deal with increasingly complex scenes, volumes and time sequences. A substantial part of this book deals with the problem of designing models that can be used for several purposes within computer vision. These partial models have some general properties of invariance generation and generality in model generation.
Signal Processing for Computer Vision is the first book to give a unified treatment of representation and filtering of higher order data, such as vectors and tensors in multidimensional space. Included is a systematic organisation for the implementation of complex models in a hierarchical modular structure and novel material on adaptive filtering using tensor data representation.
Signal Processing for Computer Vision is intended for final year undergraduate and graduate students as well as engineers and researchers in the field of computer vision and image processing.

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Produktdetails

• Verlag: Springer, Berlin
• 1995.
• Seitenzahl: 460
• Erscheinungstermin: 31. Dezember 1994
• Englisch
• Abmessung: 241mm x 160mm x 34mm
• Gewicht: 931g
• ISBN-13: 9780792395300
• ISBN-10: 0792395301
• Artikelnr.: 20999178

Inhaltsangabe

Preface. 1. Introduction and Overview. 2. Biological Vision. 3. Low Level Operations. 4. Fourier Transforms. 5. Kernel Optimization. 6. Orientation and Velocity. 7. Local Phase Estimation. 8. Local Frequency. 9. Representation and Averaging. 10. Adaptive Filtering. 11. Vector and Tensor Field Filtering. 12. Classification and Response Generation. 13. Texture Analysis. References. Index.

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1 Introduction and Overview.- 2 Biological Vision.- 3 Low Level Operations.- 4 Fourier Transforms.- 5 Kernel Optimization.- 6 Orientation and Velocity.- 7 Local Phase Estimation.- 8 Local Frequency.- 9 Representation and Averaging.- 10 Adaptive Filtering.- 11 Vector and Tensor Field Filtering.- 12 Classification and Response Generation.- 13 Texture Analysis.- References.