Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
The steerable pyramid: a flexible architecture for multi-scale derivative computation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
The Amsterdam Library of Object Images
International Journal of Computer Vision
Statistical modeling and conceptualization of visual patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
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The scale invariant property of an ensemble of natural images is examined which motivates a new early visual representation termed the higher order pyramid. The representation is a non-linear generalization of the Laplacian pyramid and is tuned to the type of scale invariance exhibited by natural imagery as opposed to other scale invariant images such as 1/f correlated noise and the step edge. The transformation of an image to a higher order pyramid is simple to compute and straightforward to invert. Because the representation is invertible it is shown that the higher order pyramid can be truncated and quantized with little loss of visual quality. Images coded in this representation have much less redundancy than the raw image pixels and decorrelating transformations such as the Laplacian pyramid. This is demonstrated by showing statistical independence between pairs of coefficients. Because the representation is tuned to the ensemble redundancies the coefficients of the higher order pyramid are more efficient at capturing the variation within the ensemble which leads too improved matching results. This is demonstrated on two recognition tasks, face recognition with illumination changes and object recognition which viewpoint changes.