This paper presents a probabilistic grammar approach to the recognition of complex events in videos. Firstly, based on the original motion features, a rule induction algorithm is a...
—This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing...
The segmentation method proposed in this paper is based on the observation that a single physical reflectance can have many different image values. We call the set of all these val...
Eduard Vazquez, Joost van de Weijer, Ramon Baldric...
Pedestrian detection in still image should handle the large appearance and stance variations arising from the articulated structure, various clothing of human as well as viewpoints...
We propose a method for estimating radiometric response functions from observation of image noise variance, not profile of its distribution. The relationship between radiance inten...
Jun Takamatsu, Yasuyuki Matsushita, Katsushi Ikeuc...
Confidence measures are crucial to the interpretation of any optical flow measurement. Even though numerous methods for estimating optical flow have been proposed over the last thr...
Claudia Kondermann, Rudolf Mester, Christoph S. Ga...
3D surface matching is fundamental for shape registration, deformable 3D non-rigid tracking, recognition and classification. In this paper we describe a novel approach for generati...
Wei Zeng, Yun Zeng, Yang Wang, Xiaotian Yin, Xianf...
Abstract. Successful multi-target tracking requires locating the targets and labeling their identities. This mission becomes significantly more challenging when many targets freque...
Given a collection of images of a static scene taken by many different people, we identify and segment interesting objects. To solve this problem, we use the distribution of images...
We present a novel method for unsupervised classification, including the discovery of a new category and precise object and part localization. Given a set of unlabelled images, som...
Leonid Karlinsky, Michael Dinerstein, Dan Levi, Sh...