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 describe an acoustic chord transcription system that uses symbolic data to train hidden Markov models and gives best-of-class frame-level recognition results. We avoid the extre...
We consider the problem of geometrical surface reconstruction from one or several images using learned shape models. While humans can effortlessly retrieve 3D shape information, t...
Abstract— This paper proposes a novel approach for motion primitive segmentation from continuous full body human motion captured on monocular video. The proposed approach does no...
We present a method to fully automatically fit videos in 16:9 format on 4:3 screens and vice versa. It can be applied to arbitrary aspect ratios and can be used to make videos sui...