New Method of Image Segmentation for Standard Images

In applications involving visual inspection, it is often required to separate objects from background, in conditions of poor and nonuniform illumination. In such cases one has to rely on adaptive methods that learn the illumination from the given images and base the Object background decision on this information. We here present a new method for image segmentation via based on clustering. One can apply different algorithms to create different clusterings of the data. Some clustering algorithms like Otsu require initialization of parameters. Different initialization can lead to different data clusterings. In this paper, we explore the idea of evidence accumulation for combining the result of multiple clustering. Initially, data is decomposed into a large number of compact clusters. We compare our new method with the Otsu method. The experiment shows that our approach can achieve higher or comparable performance than the old method.