Local and global Gabor features for object recognition

Invariant object recognition is one of the most central problems in computer vision. To be successful when occlusion and distortions are present, object recognition has to be based on local features. The features should express the significant information while being robust in the presence of noise and distortions, and stable in terms of feature parameters. In this study, Gabor filtering based features is analyzed in terms of the above requirements. Two classes of Gabor features are introduced: global Gabor features and fundamental frequency Gabor features. The Gabor filter response and stability issues are analyzed in terms of the filtering parameters. The robustness of the proposed features is examined through experiments. Both analytical and experimental results indicate that when certain conditions on the filter parameters are met, Gabor filtering can be reliably used in low-level feature extraction in image processing, and the filter responses can be used to construct robust invariant recognition systems.

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