We propose a template matching method using a small number of distinctive pixels selected on the basis of hue values as color information. By analyzing the template image, a distribution of co-occurrence probabilities regarding the hue values of two-pixel pairs is generated. A small number of pixels that have low probability are selected and used for matching. Since such pixels have high distinctiveness, reliable matching can be achieved. The recognition success rate of our method is 97% when 571 pixels are selected, and the processing time is 219 msec.
In this paper, we propose a template matching method that enables multi-class classification by scanning an input image only once, focusing on the classification ability of each pixel in the template image. This ability of each pixel in the template image is represented by using the occurrence frequency of the pixel value. On the basis of this ability, a small number of pixels that are effective for class identification are selected. Experimental results in the task of detecting five kinds of objects from 100 real images showed that the recognition rate of the proposed method was 95.4% and the processing time was 1.9 seconds when 0.6% of all template pixels were used.
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