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Learning Sparse High Dimensional Filters: Image Filtering, Dense Crfs And Bilateral Neural Networks

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), no. 1 (2016): 4452-4461

Cited: 163|Views50
EI

Abstract

Bilateral filters have wide spread use due to their edge-preserving properties. The common use case is to manually choose a parametric filter type, usually a Gaussian filter. In this paper, we will generalize the parametrization and in particular derive a gradient descent algorithm so the filter parameters can be learned from data. This d...More

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