Learn More
A new algorithm is presented for the automatic segmentation and classification of brain tissue from 3D MR scans. It uses discriminative Random Decision Forest classification and takes into account partial volume effects. This is combined with correction of intensities for the MR bias field, in conjunction with a learned model of spatial context, to achieve(More)
In this paper we exploit normalized mutual information for the nonrigid registration of multimodal images. Rather than assuming that image statistics are spatially stationary, as often done in traditional information-theoretic methods, we take into account the spatial variability through a weighted combination of global normalized mutual information and(More)
Gene expression data provide information on the location where certain genes are active; in order for this to be useful, such a location must be registered to an anatomical atlas. Because gene expression maps are considerably different from each other - they display the expression of different genes - and from the anatomical atlas, this problem is currently(More)
Cartographic feature vectorization is one of the most salient problems in the acquisition of geographic information from scanned topographic maps. This paper presents a novel automatic approach to linear feature vectorization based on image domain. By the approach, the adjacent pixels with identical color are segmented self-adaptively into basic units(More)
We describe Information Forests, an approach to classification that generalizes Random Forests by replacing the splitting criterion of non-leaf nodes from a discriminative one - based on the entropy of the label distribution - to a generative one - based on maximizing the information divergence between the class-conditional distributions in the resulting(More)
The main contribution of this paper was to propose a dynamic and objective model of E-business website quality management. The key quality factors, quality characters and sub-characters from the user’s perspective were discussed firstly, they formed the inherent causality relationship. The theoretical background of Bayesian Network was presented(More)
  • 1