In this paper we present a probabilistic framework for tracking objects based on local dynamic segmentation. We view the segn to be a Markov labeling process and abstract it as a ...
Abstract This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that r...
Roman Katz, Juan Nieto, Eduardo Mario Nebot, Bertr...
This paper presents two techniques to boost the quantization performance of trelli-based beamforming vector quantization schemes [?], [?]. It is well known that tail-biting trelli...
Despite the fact that it provides a potentially useful analytical tool, allowing for the joint modeling of dynamic interdependencies within a group of connected areas, until latel...
We present a novel ``dynamic learning'' approach for an intelligent image database system to automatically improve object segmentation and labeling without user interven...