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...
There have been important recent advances in object recognition through the matching of invariant local image features. However, the existing approaches are based on matching to i...
The recognition of activities from sensory data is important in advanced surveillance systems to enable prediction of high-level goals and intentions of the target under surveilla...
Nam Thanh Nguyen, Hung Hai Bui, Svetha Venkatesh, ...
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
This paper presents a novel approach to pedestrian classification which involves utilizing the synthesized virtual samples of a learned generative model to enhance the classificat...