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» Discrete Mixture Models for Unsupervised Image Segmentation
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CVPR
2005
IEEE
13 years 11 months ago
A Two-Stage Level Set Evolution Scheme for Man-Made Objects Detection in Aerial Images
A novel two-stage level set evolution method for detecting man-made objects in aerial images is described. The method is based on a modified Mumford-Shah model and it uses a two-s...
Guo Cao, Xin Yang, Zhihong Mao
CVPR
2000
IEEE
14 years 11 months ago
Towards Automatic Discovery of Object Categories
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
Markus Weber, Max Welling, Pietro Perona
MICCAI
1998
Springer
14 years 1 months ago
Multi-modal Volume Registration Using Joint Intensity Distributions
Abstract. The registration of multimodal medical images is an important tool in surgical applications, since different scan modalities highlight complementary anatomical structures...
Michael E. Leventon, W. Eric L. Grimson
ICCV
2001
IEEE
14 years 11 months ago
Separating Appearance from Deformation
By representing images and image prototypes by linear subspaces spanned by "tangent vectors" (derivatives of an image with respect to translation, rotation, etc.), impre...
Nebojsa Jojic, Patrice Simard, Brendan J. Frey, Da...
CVPR
2009
IEEE
15 years 4 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...