A good FR methodology should consider representation as well as classification issues, and a good representation method should require minimum manual annotaions.
March 10, 2009
March 4, 2009
Invariance Properties of Gabor Filter-based Features –Overview and Applications
The most important properties of Gabor filtering are related to invariance to illumination, rotation, scale, and translation.
Introduction
By invariance, not only are features meant which are invariant to a set of geomeric transforms, but also methods to perform object detection regardless of pose and imaging conditions using features which are not invariant.
Confusing terminologies: Gabor filter, Gabor expansion, Gabor transform, Gabor jet, Gabor frame, or Gabor wavelet?
As Daugman pointed out, the 2-D Gabor filters are good models of the simple cells in the mammalian visual cortex system.
Survey and overview
The uncertainty can be measured by root mean square (rms) bandwidth: dt, df
The signal which occupies the minimum area dt*df=1/4π is the modulation product of a harmonic oscillation of any frequency with pulse of the form of a probability function.
Gabor expansion
It should be noted that the expansion functions do not have to constitute an orthogonal basis as typically assumed in wavelet or FT, but an unconditional basis, a frame, may succeed as well.
Invariant recognition
The Gabor filters are optimally joint localized in time and frequency, and thus, distortions and noise present in distinct locations, time or frequency, do not significantly interfere with the filter responses.
Examples
Symbol recognition
This first example utilizes a globally computed sum of Gabor responses, corresponding to edge histograms over different orientation, and thus, provides no elegance or novelty as a feature extraction method.
Simplified Gabor wavelets for human face recognition
Date:2008
Introduction
The Gabor wavelet (GW) is similar to the response of the 2-D receptive field of the mammalian simple cortical cell.
As the dimension of the feature vectors using GWs is very large, PCA/FLD are used to reduce the dimension. To further improve the performance, kernel methods are also used with the Gabor features. The improvement of both the linear and the kernel methods is due to the fact that GW features are robust to illumination, rotation, and scale.
Simplified GW (SGW)
March 3, 2009
Protected: Optimal sampling of Gabor features for face recognition
March 1, 2009
EEG pattern discrimination betwwen salty and sweet taste using adaptive Gabor transform
Date:2005
Materials and methods
The problem consists of finding the Gabor function hp(t) among the set of normalized Gabor functions that are most similar to sp(t). In each step p, one Gabor function hp(t) is found and the next residual sp+1(t) is computed.
For each EEG raw data, 10 (p=10) AGR (adpative Gabor transform) coefficients are obtained.
Results and discussion
The advantage of this method is the increased spectral and temporal resolution of the signal. Several experiments have shown that the time-frequency analysis method provides narrow frequency peaks, permitting more precise frequency identification and enhanced ability in the determination of frequency changes at any EEG signal point.