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2009 IEEE Conference on Computer Vision and Pattern Recognition
Several recently-proposed architectures for highperformance object recognition are composed of two main stages: a feature extraction stage that extracts locallyinvariant feature vectors from regularly spaced image patches, and a somewhat generic supervised classifier. The first stage is often composed of three main modules: (1) a bank of filters (often oriented edge detectors); (2) a non-linear transform, such as a point-wise squashing functions, quantization, or normalization; (3) a spatialdoi:10.1109/cvprw.2009.5206545 fatcat:zxun6vqtezb4pjudj6jgnyweby