The goal of this work is to find all people in archive films. Challenges include low image quality, motion blur, partial occlusion, non-standard poses and crowded scenes. We base ...
Because of the large variation across different environments, a generic classifier trained on extensive data-sets may perform sub-optimally in a particular test environment. In th...
In this paper, we address the problems of deformable object matching (alignment) and segmentation with cluttered background. We propose a novel hierarchical log-linear model (HLLM...
Long Zhu, Yuanhao Chen, Xingyao Ye, Alan L. Yuille
In this paper, we present an interactive offline tracking system for generic color objects. The system achieves 60100 fps on a 320 ? 240 video. The user can therefore easily refin...
In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...