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» A probabilistic framework for image segmentation
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CVPR
2009
IEEE
15 years 3 months ago
What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category ...
Sudheendra Vijayanarasimhan (University of Texas a...
MICCAI
2010
Springer
13 years 6 months ago
Manifold Learning for Biomarker Discovery in MR Imaging
We propose a framework for the extraction of biomarkers from low-dimensional manifolds representing inter- and intra-subject brain variation in MR image data. The coordinates of ea...
Robin Wolz, Paul Aljabar, Joseph V. Hajnal, Daniel...
ECCV
1994
Springer
14 years 9 months ago
Recursive Affine Structure and Motion from Image Sequences
Abstract. This paper presents a new algorithm for structure from motion from an arbitrary number of tracked features over an arbitrary number of images, which possesses several adv...
Philip F. McLauchlan, Ian D. Reid, David W. Murray
SIBGRAPI
2006
IEEE
14 years 1 months ago
Extracting Discriminative Information from Medical Images: A Multivariate Linear Approach
Statistical discrimination methods are suitable not only for classification but also for characterisation of differences between a reference group of patterns and the population u...
Carlos E. Thomaz, Nelson A. O. Aguiar, Sergio H. A...
ICCV
2009
IEEE
15 years 27 days ago
Wide-Baseline Image Matching Using Line Signatures
We present a wide-baseline image matching approach based on line segments. Line segments are clustered into local groups according to spatial proximity. Each group is treated as...
Lu Wang, Ulrich Neumann and Suya You