Abstract. In this paper we propose a probabilistic framework that models shape variations and infers dense and detailed 3D shapes from a single silhouette. We model two types of sh...
We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector s...
Assume that some objects are present in an image but can be seen only partially and are overlapping each other. To recognize the objects, we have to rstly separate the objects from...
— This paper describes a method of probabilistic obstacle map building based on Bayesian estimation. Most active or passive obstacle sensors observe only the most frontal objects...
This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. All modeling assumptions are rigorously explained, and...
Tinne De Laet, Joris De Schutter, Herman Bruyninck...