The Multiplicative Path Toward Prior-Shape Guided Active Contour for Object Detection

In detecting the boundary of an object in an image, if certain prior shape knowledge of the object is available, an effective approach is to have the intensity gradient information in the image and the prior shape knowledge be combined together to drive an active contour for the purpose. While in the classical methods the two terms are almost always summed with a certain weight between them to form the optimization functional, in the method we propose, they are multiplied together so as to avoid the need and thus design of the weight parameter. We show that the object detection result in the traditional formulation could indeed be very much affected by the weight value, and the proposed method, being without its presence, is therefore free from the influence of the important parameter. Experimental results on cells in real biological images, whose boundaries are blurred to very different degrees across the image by the inevitably uneven illumination, are shown to demonstrate the improvement in performance.