A candidate solutions generator based on mixed strategy for non-rigid object extraction

Abstract

Extracting non-rigid object from images can be used in object recognition, medical image analysis, video monitoring, etc. In order to improve the efficiency and accuracy of visual object extraction, we design a candidate shape generator based on a mixture strategy, called mixture generator, it combines the image data driven method with model parameter driven method, and tends to generate valid shape in area which has a high shape prior density value by exploiting the GPDM model, so the efficiency of search is greatly improved. To prove the accuracy of our mixture generator, we have done experiments under the framework of global optimization algorithm (simulated annealing) on the FGNET face database. Experiments show that, compared with traditional ASM algorithm, our method is not only insensitive to initialization conditions, but also can put up with clutters and realize a more robust object extraction.