Accurate segmentation using multiple sources and probability networks

Data from more than one source can be useful in that we can use information from one source to overcome a deficiency in another source. This can be especially beneficial when segmenting an image. We present a method for fusing data from more than one source of information or more than one segmentation method to achieve an accurate segmentation of an object. By this we mean a surface estimation consisting of surface and boundary properties (e.g., orientation, curvature, perimeter, etc.). This paper builds significantly on previous results where we outlined some simple, preliminary concepts used to obtain accurate estimation of the object's surface properties. Objects to be segmented may now consist of curved surfaces and curved boundaries. Probabilistic networks are used to process the variety of data that is available in order to provide the best segmentation results.