Introduction - If you have any usage issues, please Google them yourself
a new mathematical and algorithmic
framework for unsupervised image segmentation, which is a
critical step in a wide variety of image processing applications.
We have found that most existing segmentation methods are
not successful on histopathology images, which prompted us to
investigate segmentation of a broader class of images, namely
those without clear edges between the regions to be segmented.
We model these images as occlusions of random images, which we
call textures, and show that local histograms are a useful tool for
segmenting them. Based on our theoretical results, we describe
a flexible segmentation framework that draws on existing work
on nonnegative matrix factorization and image deconvolution.
Results on synthetic texture mosaics and real histology images
show the promise of the method.