We propose a probabilistic formulation of 3D segmentation given a series of images from calibrated cameras. Instead of segmenting each image separately in order to build a 3D surfa...
In many machine learning problems, labeled training data is limited but unlabeled data is ample. Some of these problems have instances that can be factored into multiple views, ea...
We introduce a new method for data clustering based on a particular Gaussian mixture model (GMM). Each cluster of data, modeled as a GMM into an input space, is interpreted as a hy...
Abstract. We introduce a generative probabilistic model for segmentation of tumors in multi-dimensional images. The model allows for different tumor boundaries in each channel, ref...
Bjoern H. Menze, Koenraad Van Leemput, Danial Lash...
Bayesian principal component analysis (BPCA), a probabilistic reformulation of PCA with Bayesian model selection, is a systematic approach to determining the number of essential p...