Quantitative Model of Visual Cortex and its Application in Object Detection
Rieseshuber and Poggio from MIT proposed a standard quantitative model of visual cortex(QMVC).This feedforward hierarchical model perfect follows the visual recognition mechanisms of the cortex from simple cells to the complex cells.A set of novel features from QMVC which is invariant of object transform can be used in object recognition systems combined with classifiers.This paper proposed a new recognition system framework based on QMVC′s features.We evaluated the performance of this new system on the single object recognition system and the results show excellent recognition rate and low false alarm rate than other category recognition systems.Together with a scaled window technique,those new features also work well in object detecting and localization in cluster background.