Random spatial sampling and majority voting based image thresholding
نویسنده
چکیده
This paper presents a novel image thresholding algorithm, termed as random spatial sampling and majority voting based image thresholding algorithm (RMIT). RMIT firstly obtains a population of binary subimages by using random spatial sampling and Otsu’s thresholding algorithm [1]. Then RMIT aggregates these binary subimages into a consensus binary image by majority voting technique. Since the subimages are randomly selected with different sizes ranging from one pixel to the whole image, RMIT can make use of both global and local information for thresholding an image. Experimental results on several real images confirm the effectiveness of RMIT.
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