We propose semantic texton forests, efficient and powerful new low-level features. These are ensembles of decision trees that act directly on image pixels, and therefore do not ne...
We present an efficient method for learning part-based object class models from unsegmented images represented as sets of salient features. A model includes parts' appearance...
Paper forgery is among the leading causes of corruption in many developing regions [2]. In this paper, we introduce PaperSpeckle, a robust system that leverages the natural random...
lass Recognition using Images of Abstract Regions Yi Li, Jeff A. Bilmes, and Linda G. Shapiro Department of Computer Science and Engineering Department of Electrical Engineering Un...
We consider the task of estimating, from observed data, a probabilistic model that is parameterized by a finite number of parameters. In particular, we are considering the situat...