EEG Classification by Learning Vector Quantization - EEG-Klassifikation mit Hilfe eines Learning Vector Quantizers

1992 ◽  
Vol 37 (12) ◽  
pp. 303-309 ◽  
Author(s):  
D. Flotzinger ◽  
J. Kalcher ◽  
G. Pfurtscheller
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Eeg Classification ◽  
Vector Quantizers

An Application of Wavelet-Based Vector Quantization in Target Recognition

1997 ◽  
Vol 06 (02) ◽  
pp. 165-178 ◽  
Author(s):  
Lipchen Alex Chan ◽  
Nasser M. Nasrabadi
Keyword(s):  
Vector Quantization ◽  
Wavelet Decomposition ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Learning Vector Quantization ◽  
Input Image ◽  
Target Area ◽  
Fixed Size ◽  
Target Class ◽  
Vector Quantizers

An automatic target recognition (ATR) classifier is constructed that uses a set of dedicated vector quantizers (VQs). The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition splits the enlarged extraction into several subbands. A dedicated VQ codebook is generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics.

Sign in / Sign up

Export Citation Format

Share Document