In this paper we present a new method for categorizing
video sequences capturing different scene classes. This can
be seen as a generalization of previous work on scene classific...
Paritosh Gupta, Sai Sankalp Arrabolu, Mathew Brown...
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...
This paper extends the traditional binocular stereo problem into the spacetime domain, in which a pair of video streams is matched simultaneously instead of matching pairs of imag...
We present a new approach to appearance-based object recognition, which captures the relationships between multiple model views and exploits them to improve recognition performanc...
Vittorio Ferrari, Tinne Tuytelaars, Luc J. Van Goo...
In this paper we propose an approach capable of simultaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical representation ...