This text examines 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. It gives a unified treatment of representation and filtering of higher order data, such as vectors and tensors in multidimensional space. Included is a systematic organization for the implementation of complex models in a hierarchical modular structure and novel material on adaptive filtering using tensor data representation. This text 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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