A novel feature descriptor based on microscopy image statistics
The International Conference on Image Processing (ICIP) 2015.
Abstract:
We propose a novel feature description algorithm based on image statistics. The pipeline first performs independent component analysis on training image patches to obtain basis vectors (filters) for a lower dimensional representation. Then for a given image, a set of filter responses at each pixel is computed. Finally, a histogram representation, which considers the signs and magnitudes of the responses as well as the number of filters, is applied on local image patches. We propose to apply proposed natural image statistics based (NISF) descriptor to a microscopy image pixel identification system based on a learning framework.
