Co-occurrence Filter

2017-03-12 · CVPR 2017 7 · Roy J Jevnisek, Shai Avidan

Co-occurrence Filter (CoF) is a boundary preserving filter. It is based on the Bilateral Filter (BF) but instead of using a Gaussian on the range values to preserve edges it relies on a co-occurrence matrix. Pixel values that co-occur frequently in the image (i.e., inside textured regions) will have a high weight in the co-occurrence matrix. This, in turn, means that such pixel pairs will be averaged and hence smoothed, regardless of their intensity differences. On the other hand, pixel values that rarely co-occur (i.e., across texture boundaries) will have a low weight in the co-occurrence matrix. As a result, they will not be averaged and the boundary between them will be preserved. The CoF therefore extends the BF to deal with boundaries, not just edges. It learns co-occurrences directly from the image. We can achieve various filtering results by directing it to learn the co-occurrence matrix from a part of the image, or a different image. We give the definition of the filter, discuss how to use it with color images and show several use cases.

📄 PDF Abstract BibTeX arXiv:1703.04111

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Co-Occurrence Neural Network

2019-06-01 · CVPR 2019 6 · Irina Shevlev, Shai Avidan

Convolutional Neural Networks (CNNs) became a very popular tool for image analysis. Convolutions are fast to compute and easy to store, but they also have some limitations. First, they are shift-invariant and, as a resul…

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Deep Co-Occurrence Feature Learning for Visual Object Recognition

2017-07-01 · CVPR 2017 7 · Ya-Fang Shih, Yang-Ming Yeh, Yen-Yu Lin, Ming-Fang Weng 외

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Texture segmentation is the process of partitioning an image into regions with different textures containing a similar group of pixels. Detecting the discontinuity of the filter's output and their statistical properties …

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