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Object Recognition using Multiple Thresholding and Local Binary Shape Features

Traditionally, image thresholding is applied to segmentation allowing foreground objects to be segemented. However, selection of thresholds in such schemes can prove difficult. We propose a solution by applying multiple thresholds. The task of object recognition then becomes that of matching binary objects, for which we present a new method based on local shape features. We embed our recognition method in a system which reduces the computational increase caused by using multiple thresholding. Experimental results show our method and system work well despite only using a single example of each object class for matching.