Image segmentation is an important pre-processing step before object recognition. Here, we assume that an image consists of three main primitives, namely the noise, the object and the varying background. First we shall show a mean to characterize the sizes of these primitives based on the morphological opening. Second, we investigate how an image can be effectively enhanced by looking for blocks inscribed under the image surface and then removing the top of the noisy background and the bottom of the foreground, which is small speck noise, constructed from the surfaces of the inscribed blocks. With these findings, a morphological segmentation algorithm is thus formulated. Experimental results are included to illustrate its superiority over other two segmentation algorithms.
In this paper, we shall investigate thinning algorithms based on morphological hit/miss transform. Our target is to extract skeletons of closed-loop objects in noisy images. After the thinning process, many skeletal legs are generated and some of them may be very long, which require much time to be shortened. First, we propose three algorithms which can speed up the skeletal leg shortening process in different situations and investigate their performance to a single skeletal leg. Secondly we suggest a method of classifying an image using the skeletal leg patterns. Finally we investigate the efficiencies of the three proposed algorithms to different kinds of skeletal leg patterns and propose a decision method to select the most efficient algorithm for a particular image.
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