We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
We introduce an approach to accurately detect and segment partially occluded objects in various viewpoints and scales. Our main contribution is a novel framework for combining obj...
This paper deals with feature matching and segmentation of common objects in a pair of images, simultaneously. For the feature matching problem, the matching likelihoods of all fea...
Tae Hoon Kim (Seoul National University), Kyoung M...
Most existing object segmentation algorithms suffer from a so-called under-segmentation problem, where parts of the segmented object are missing and holes often occur inside the ob...
In this paper, we propose techniques to make use of two complementary bottom-up features, image edges and texture patches, to guide top-down object segmentation towards higher pre...
Thomas Brox, Lubomir Bourdev, Subhransu Maji, Jite...