Design and Simulation of Product Shape Feature Extraction Algorithm under Eye Movement Tracking
The appearance of different products is very complex and diverse.In the process of extracting product appearance features,the accuracy of feature extraction is reduced due to the influence of noise,cluttered background,or other interference elements.In order to accurately extract these product appearance features,an algorithm under eye movement tracking was proposed.At first,the product appearance image with noise was decomposed by a pyramidal directional filter.Then,the multi-scale threshold was used to denoise the high-frequency sub-band,thus achieving the purpose of suppressing noise coefficients while effectively retaining signal coefficients.Based on the Contourlet in-verse transformation,the denoised image was obtained.The area of interest in the product appearance image was de-termined by eye movement tracking technology.Meanwhile,multiple low-level features were extracted in the area of interest.Finally,the improved quantum genetic algorithm was adopted to select the low-level features.Thus,the prod-uct appearance feature extraction was achieved.Experimental results show that the proposed algorithm effectively im-proves the accuracy and efficiency of feature extraction.
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