Transition region based single and multiple object segmentation of gray scale images

Abstract

Transition region based image segmentation has proved to be the simple and effective image segmentation technique. However, the methods have two shortcomings. First, they are applied mostly for image segmentation containing a single object. Second, the methods are effective only when the images contain simple background and foreground. The performance deteriorates when background and foreground are textured or of varying intensities. To overcome this, a novel method has been proposed for multi-object segmentation. In this method, a global threshold and the local variance is computed to achieve the transition regions. The transition regions thus obtained undergo morphological operations to get the object contours. The morphological filling operation is employed on object contours to extract object regions. Finally, the objects are extracted from the image from these object regions. The proposed method is compared with different methods for single-object segmentation, and experimental results show superior performance. The method also works efficiently for multiple object segmentation.