We show how scanned images can be segmented on the basis of their stochastic nature without a priori knowledge of the number of regions or their model parameters. The region distribution is modelled by a Mesh Markov Random Field while each region is filled with textures modelled by a Binomial Markov Random Field (BMRF). A hierarchical fuzzy clustering method is used to estimate the BMRF paramet...

Magnetic Resonance Imaging (MRI) offers a wealth of information for medical examination. Fast, accurate and reproducible segmentation of MRI is desirable in many applications. We have developed a new unsupervised MRI segmentation method based on k-means and fuzzy c-means (FCM) algorithms, which uses spatial constraints. Spatial constraints are included by the use of a Markov Random Field model....

Fusion of information gathered from multiple sources is essential to build a comprehensive situation picture for autonomous ground vehicles. In this paper, an approach which performs scene parsing and data fusion for a 3DLIDAR scanner (Velodyne HDL-64E) and a video camera is described. First of all, a geometry segmentation algorithm is proposed for detection of obstacles and ground areas from d...

This paper proposes an original method for the classification of seafloors from high resolution sidescan sonar images. We aim at classifying the sonar images into five kinds of regions: sand, pebbles, rocks, ripples, and dunes. The proposed method adopts a pattern recognition approach based on the extraction and the analysis of the cast shadows exhibited by each seabottom type. This method co...

A hierarchical algorithm for face analysis is presented in this paper. A color video sequence of speaker’s face is acquired, under natural lighting conditions and without any particular make-up. The application aims at providing geometrical features of the face for scalable video transmission when no specific model of the speaker face is assumed. First, a logarithmic hue transform is performed ...