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» Support Vector Machines for 3D Shape Processing
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107
Voted
DAGM
2010
Springer
15 years 3 months ago
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma
Abstract. We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped ...
Peter J. Schüffler, Thomas J. Fuchs, Cheng So...
120
Voted
ICPR
2006
IEEE
16 years 3 months ago
A fast binary-image comparison method with local-dissimilarity quantification
Image similarity measure is widely used in image processing. For binary images that are not composed of a single shape, a local comparison is interesting but the features are usel...
Etienne Baudrier, Frédéric Nicolier,...
109
Voted
CIBCB
2005
IEEE
15 years 8 months ago
A Neural Network for Predicting Protein Disorder using Amino Acid Hydropathy Values
— Proteins have been discovered to contain ordered regions and disordered regions, where ordered regions have a defined three-dimensional (3D) structure and disordered regions d...
Deborah Stoffer, L. Gwenn Volkert
148
Voted
ICCV
2009
IEEE
15 years 11 days ago
Structural SVM for visual localization and continuous state estimation
We present an integrated model for visual object localization and continuous state estimation in a discriminative structured prediction framework. While existing discriminative `p...
Catalin Ionescu, Liefeng Bo, Cristian Sminchisescu
ICCV
2003
IEEE
16 years 4 months ago
Landmark-based Shape Deformation with Topology-Preserving Constraints
This paper presents a novel approach for landmarkbased shape deformation, in which fitting error and shape difference are formulated into a support vector machine (SVM) regression...
Song Wang, Jim Xiuquan Ji, Zhi-Pei Liang