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» Cokriging for spatial functional data
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KDD
2009
ACM
151views Data Mining» more  KDD 2009»
16 years 7 days ago
A LRT framework for fast spatial anomaly detection
Given a spatial data set placed on an n ? n grid, our goal is to find the rectangular regions within which subsets of the data set exhibit anomalous behavior. We develop algorithm...
Mingxi Wu, Xiuyao Song, Chris Jermaine, Sanjay Ran...
IGARSS
2009
14 years 9 months ago
Remotely Sensed Phenology for Mapping Biomes and Vegetation Functional Types
This study used remotely-sensed phenology data derived from Advanced Very High Resolution Radiometer (AVHRR), in a fully supervised decision-tree classification based on the new b...
Konrad J. Wessels, Karen Steenkamp, Graham Von Mal...
MICCAI
2009
Springer
16 years 8 days ago
Nonparametric Mean Shift Functional Detection in the Functional Space for Task and Resting-state fMRI
In functional Magnetic Resonance Imaging (fMRI) data analysis, normalization of time series is an important and sometimes necessary preprocessing step in many widely used methods. ...
Jian Cheng, Feng Shi, Kun Wang, Ming Song, Jiefeng...
VVS
1998
IEEE
91views Visualization» more  VVS 1998»
15 years 4 months ago
Design of Accurate and Smooth Filters for Function and Derivative Reconstruction
The correct choice of function and derivative reconstruction filters is paramount to obtaining highly accurate renderings. Most filter choices are limited to a set of commonly use...
Torsten Möller, Klaus Mueller, Yair Kurzion, ...
MICCAI
2005
Springer
16 years 17 days ago
Exploiting Temporal Information in Functional Magnetic Resonance Imaging Brain Data
Functional Magnetic Resonance Imaging(fMRI) has enabled scientists to look into the active human brain, leading to a flood of new data, thus encouraging the development of new data...
Lei Zhang 0002, Dimitris Samaras, Dardo Tomasi, Ne...