We propose machine learning methods for the estimation of deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspo...
We present the results of a three year field study of the software development process choices made by project teams at two leading offshore vendors. In particular, we focus on th...
In this work we present a novel approach for learning nonhomogenous textures without facing the unlearning problem. Our learning method mimics the human behavior of selective lear...
Class membership probability estimates are important for many applications of data mining in which classification outputs are combined with other sources of information for decisi...
The paper describes a reliable and rapid method for detecting and removing a color cast (i.e. a superimposed dominant color) in a digital image without any a priori knowledge of i...