⎯This paper presents an improved multi-object segmentation algorithm based on probabilistic labeling. First, a critical look is focused on utilizing vector calculus operator and ...
We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inferen...
Mert R. Sabuncu, B. T. Thomas Yeo, Koenraad Van Le...
Mesh analysis and clustering have became important issues in order to improve the efficiency of common processing operations like compression, watermarking or simplification. In t...
We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a...
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...