A STATISTICAL APPROACH TO IDENTIFYING CLOSED OBJECT BOUNDARIES IN IMAGES

In this research, we present a statistical theory, and an algorithm; t o identify one-pixel-wide closed object boundaries in gray-scale image s. Closed-boundary identification is an important problem because boun daries of objects are major features in images. In spite of this, most statistical approaches to image restoration and texture identificatio n place inappropriate stationary model assumptions on the image domain . One way to characterize the structural components present in images is to identify one-pixel-wide closed boundaries that delineate objects . By defining a prior probability model on the space of one-pixel-wide closed boundary configurations and appropriately specifying transitio n probability functions on this space, a Markov chain Monte Carlo algo rithm is constructed that theoretically converges to a statistically o ptimal closed boundary estimate. Moreover, this approach ensures that any approximation to the statistically optimal boundary estimate will have the necessary property of closure.