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ICIP
2008
IEEE
16 years 8 months ago
Conditional iterative decoding of Two Dimensional Hidden Markov Models
Two Dimensional Hidden Markov Models (2D-HMMs) provide substantial benefits for many computer vision and image analysis applications. Many fundamental image analysis problems, inc...
Mehmet Emre Sargin, Alphan Altinok, Kenneth Rose, ...
IBPRIA
2007
Springer
16 years 13 days ago
False Positive Reduction in Breast Mass Detection Using Two-Dimensional PCA
In this paper we present a novel method for reducing false positives in breast mass detection. Our approach is based on using the Two-Dimensional Principal Component Analysis (2DPC...
Arnau Oliver, Xavier Lladó, Joan Mart&iacut...
DASFAA
2005
IEEE
120views Database» more  DASFAA 2005»
15 years 12 months ago
A New Indexing Method for High Dimensional Dataset
Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering “curse...
Jiyuan An, Yi-Ping Phoebe Chen, Qinying Xu, Xiaofa...
ICPR
2006
IEEE
16 years 7 months ago
Dimensionality Reduction with Adaptive Kernels
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propo...
Shuicheng Yan, Xiaoou Tang
DAGM
2005
Springer
15 years 11 months ago
Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Estimation
Abstract. This paper presents the integration of 3D shape knowledge into a variational model for level set based image segmentation and tracking. Having a 3D surface model of an ob...
Thomas Brox, Bodo Rosenhahn, Joachim Weickert