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IDA
1998
Springer
15 years 6 months ago
Fast Dimensionality Reduction and Simple PCA
A fast and simple algorithm for approximately calculating the principal components (PCs) of a data set and so reducing its dimensionality is described. This Simple Principal Compo...
Matthew Partridge, Rafael A. Calvo
ICDAR
2009
IEEE
16 years 1 months ago
Recognition of Degraded Handwritten Characters Using Local Features
The main problems of Optical Character Recognition (OCR) systems are solved if printed latin text is considered. Since OCR systems are based upon binary images, their results are ...
Markus Diem, Robert Sablatnig
CIVR
2006
Springer
129views Image Analysis» more  CIVR 2006»
15 years 10 months ago
Retrieving Objects Using Local Integral Invariants
The use of local features in computer vision has shown to be promising. Local features have several advantages including invariance to image transformations, independence of the ba...
Alaa Halawani, Hashem Tamimi
CORR
2010
Springer
126views Education» more  CORR 2010»
15 years 6 months ago
Fundamental Limits of Wideband Localization - Part I: A General Framework
The availability of positional information is of great importance in many commercial, public safety, and military applications. The coming years will see the emergence of locationa...
Yuan Shen, Moe Z. Win
CVPR
2007
IEEE
16 years 8 months ago
Integrating Global and Local Structures: A Least Squares Framework for Dimensionality Reduction
Linear Discriminant Analysis (LDA) is a popular statistical approach for dimensionality reduction. LDA captures the global geometric structure of the data by simultaneously maximi...
Jianhui Chen, Jieping Ye, Qi Li