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IPMI
2009
Springer
14 years 8 months ago
Multivariate High-Dimensional Cortical Folding Analysis, Combining Complexity and Shape, in Neonates with Congenital Heart Disea
The paper presents a novel statistical framework for cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (RO...
Suyash P. Awate, Paul A. Yushkevich, Zhuang Song, ...
ICCV
2007
IEEE
14 years 9 months ago
High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors
Matching of high-dimensional features using nearest neighbors search is an important part of image matching methods which are based on local invariant features. In this work we hi...
Dusan Omercevic, Ondrej Drbohlav, Ales Leonardis
ICMLA
2009
13 years 5 months ago
Learning Deep Neural Networks for High Dimensional Output Problems
State-of-the-art pattern recognition methods have difficulty dealing with problems where the dimension of the output space is large. In this article, we propose a new framework ba...
Benjamin Labbé, Romain Hérault, Cl&e...
TIP
2008
177views more  TIP 2008»
13 years 7 months ago
Visual Tracking in High-Dimensional State Space by Appearance-Guided Particle Filtering
Abstract--In this paper, we propose a new approach, appearance-guided particle filtering (AGPF), for high degree-of-freedom visual tracking from an image sequence. This method adop...
Wen-Yan Chang, Chu-Song Chen, Yong-Dian Jian
BMCBI
2007
123views more  BMCBI 2007»
13 years 7 months ago
Robust clustering in high dimensional data using statistical depths
Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor cent...
Yuanyuan Ding, Xin Dang, Hanxiang Peng, Dawn Wilki...