Segmentation-Free Object Recognition Using Full Pixel Matching

We present a novel method for recognizing an object in an image using full pixel matching between a reference image and an input image without advance segmentation of the image. A method called two-dimensional continuous dynamic programming (2DCDP) is adopted to optimally calculate the accumulated local distances of all corresponding pixels in nonlinearly matched areas in an input image and a reference image representing an object. The object is recognized by using two parameters, the matching rate and the standard deviation of the amplitude of a vector of pixel displacement between matched pixels, so that images can be mapped into a two-dimensional space. Finally a general decision space is proposed for nonlinear transformation in object recognition.Experimental results show that the proposed method performs well in recognizing objects.