This paper develops a weakly supervised algorithm that learns to segment rigid multi-colored objects from a set of training images and key points. The approach uses congealing to ...
Douglas Moore, John Stevens, Scott Lundberg, Bruce...
Abstract In the analysis of any unconstrained document image it is necessary to first decide what the main area of interest is. Previous work has been done on simply removing the ...
John Bunch, Dean Curtis, Christopher Jones, Jia Ts...
We propose a new multiphase level set framework for image segmentation using the Mumford and Shah model, for piecewise constant and piecewise smooth optimal approximations. The pro...
Abstract. We describe a new approach for learning to perform classbased segmentation using only unsegmented training examples. As in previous methods, we first use training images ...
In this paper, we introduce the Skellam distribution as a sensor noise model for CCD or CMOS cameras. This is derived from the Poisson distribution of photons that determine the s...