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» Learning from Highly Structured Data by Decomposition
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ICCAD
2006
IEEE
126views Hardware» more  ICCAD 2006»
15 years 6 months ago
Exploring linear structures of critical path delay faults to reduce test efforts
It has been shown that the delay of a target path can be composed linearly of other path delays. If the later paths are robustly testable (with known delay values), the target pat...
Shun-Yen Lu, Pei-Ying Hsieh, Jing-Jia Liou
DASFAA
2005
IEEE
120views Database» more  DASFAA 2005»
15 years 3 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...
EDBT
2000
ACM
15 years 1 months ago
Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
In this paper we present the Slim-tree, a dynamic tree for organizing metric datasets in pages of fixed size. The Slim-tree uses the "fat-factor" which provides a simple ...
Caetano Traina Jr., Agma J. M. Traina, Bernhard Se...
EMMCVPR
2007
Springer
15 years 4 months ago
Compositional Object Recognition, Segmentation, and Tracking in Video
Abstract. The complexity of visual representations is substantially limited by the compositional nature of our visual world which, therefore, renders learning structured object mod...
Björn Ommer, Joachim M. Buhmann
ICCV
2007
IEEE
15 years 12 months ago
3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in...
Albert Murtha, Dana Cobzas, Mark Schmidt, Martin J...