IAPR Workshop on Machine Vision and Applications, pp. 455-458, 2000, Tokyo, Japan In this paper, we integrate the model-based tracking and local contexture (temporal and spatial) ...
We propose a novel probabilistic framework for learning
visual models of 3D object categories by combining appearance
information and geometric constraints. Objects are
represen...
We present a system for recognising human behaviour given a symbolic representation of surveillance videos. The input of our system is a set of timestamped short-term behaviours, t...
Tracking moving obstacles from a moving platform is a useful skill for the coming generation of mobile robot. The methods used in existing moving objects tracking that operated fr...
We develop an integrated, probabilistic model for the appearance and three-dimensional geometry of cluttered scenes. Object categories are modeled via distributions over the 3D lo...
Erik B. Sudderth, Antonio B. Torralba, William T. ...