An Integrated Method for Simultaneous Recognition and Segmentation of Deformable Objects

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This research is primarily aimed at robustly locating deformable structures/objects of approximately known shape from natural images. The problem of locating and recognizing underlying object structures in an image is of importance in many image analysis and computer vision applications including robot vision, pattern recognition and biomedical image processing. Algorithms that can reliably perform these basis tasks are at the core of a variety of systems that will permit humans to more effectively interact with pictorial data for the purposes of visualization, analysis, control or, as in the case of image database systems, retrieval of task-specific information within these applications areas. The robust identification and measurement of such structure is not always achievable using a single analysis technique that depends on a single image derived source of information. This is especially true when one is dealing with a wide range of images obtained under different conditions having different image content. The approach in this research project utilizes two different sources of image-derived information: 1.) gray-level gradients and ii.) homogeneity of intensity or texture elements, etc., as well as model-based information (i.e approximate object shape). Furthermore, two different processing methods are combined in this approach, in order to integrate the above-mentioned information sources: a) regionbased methods, which are primarily based on homogeneity properties and b.) boundary methods which capitalize on both gradient (imagederived) and shape (model based) properties. A game theoretic framework will be used, where, unlike the global objective approach which is widely used as an integration method in computer vision, the modularity of the underlying objectives are retained. The integration problem is then framed as a family of coupled and coex isting objectives whereby the output of one module depends upon the previous outputs of the other modules. This effort will build on initial work which has been able to locate structure in two dimensional images. It will also extend the concept to analyzing three dimensional image data. Both theoretical and experimental investigations will be carried out regarding the applicability and the limits of the approach.