This paper presents GeoS, a new algorithm for the efficient segmentation of n-dimensional image and video data. The segmentation problem is cast as approximate energy minimization ...
We propose an algorithm to improve the quality of depth-maps used for Multi-View Stereo (MVS). Many existing MVS techniques make use of a two stage approach which estimates depth-m...
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
We observe that everyday images contain dozens of objects, and that humans, in describing these images, give different priority to these objects. We argue that a goal of visual rec...
Abstract. Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the ...
We propose a novel tracking algorithm based on the Wang-Landau Monte Carlo sampling method which efficiently deals with the abrupt motions. Abrupt motions could cause conventional ...
Junseok Kwon (Seoul National University), Kyoung M...
We consider the problem of multi-label, supervised image segmentation when an initial labeling of some pixels is given. In this paper, we propose a new generative image segmentatio...
Tae Hoon Kim (Seoul National University), Kyoung M...
We introduce a new concept of co-recognition for object-level image matching between an arbitrary image pair. Our method augments putative local regionmatches to reliable object-...
Minsu Cho (Seoul National University), Young Min S...
We propose a new method for detecting objects such as bags carried by pedestrians depicted in short video sequences. In common with earlier work on the same problem, the method sta...