Purely bottom-up, unsupervised segmentation of a single
image into two segments remains a challenging task for
computer vision. The co-segmentation problem is the process
of joi...
We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned f...
In this paper we propose an approach capable of simultaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical representation ...
Bag-of-words (BoW) methods are a popular class of object recognition methods that use image features (e.g., SIFT) to form visual dictionaries and subsequent histogram vectors to r...
In this paper, an efficient and computationally fast method for segmenting text and graphics part of document images based on textural cues is presented. We assume that the graphic...