We present an efficient multi stage approach to detection of deformable objects in real, cluttered images given a single or few hand drawn examples as models. The method handles de...
Abstract. This paper presents a novel probabilistic approach to integrating multiple cues in visual tracking. We perform tracking in different cues by interacting processes. Each p...
Viewpoint invariant pedestrian recognition is an important yet under-addressed problem in computer vision. This is likely due to the difficulty in matching two objects with unknown...
Abstract. A new approach, called Collective Shape Difference Classifier (CSDC), is proposed to improve the accuracy and computational efficiency of 3D face recognition. The CSDC le...
Yueming Wang, Xiaoou Tang, Jianzhuang Liu, Gang Pa...
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 present in this paper a system which automatically
builds, from real images, a scene model containing both
3D geometric information of the scene structure and its
photometric...