Abstract. This paper evaluates strategies to combine multiple segmentations of the same image, generated for example by different segmentation methods or by different human experts...
Torsten Rohlfing, Daniel B. Russakoff, Calvin R. M...
We describe how to teach deformable models to maximize image segmentation correctness based on user-specified criteria, and we present a method for evaluating which criteria work ...
We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test im...
Alexander Vezhnevets, Vittorio Ferrari, Joachim M....
We propose a novel unsupervised learning algorithm to extract the layout of an image by learning latent object-related aspects. Unlike traditional image segmentation algorithms th...
This paper considers the problem of tissue classification in 3D MRI. More specifically, a new set of texture features, based on phase information, is used to perform the segmentati...
Pierrick Bourgeat, Jurgen Fripp, Andrew L. Janke, ...