We analyze the amount of information needed to carry out model-based recognition tasks, in the context of a probabilistic data collection model, and independently of the recogniti...
The ability to accurately localize objects in an observed scene is regarded as an important precondition for many practical applications including automatic manufacturing, quality ...
We propose a novel probabilistic framework for learning
visual models of 3D object categories by combining appearance
information and geometric constraints. Objects are
represen...
This paper presents a novel method for quickly filtering range data points to make object recognition in large 3D data sets feasible. The general approach, called "3D cueing,...
Abstract. This paper presents a new approach to the problem of simultaneous location and segmentation of object in images. The main emphasis is done on the information provided by ...