2 resultados para LIKELIHOOD PRINCIPLE

em Boston University Digital Common


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This position paper outlines a new network architecture, i.e., a style of construction that identifies the objects and how they relate. We do not specify particular protocol implementations or specific interfaces and policies. After all, it should be possible to change protocols in an architecture without changing the architecture. Rather we outline the repeating patterns and structures, and how the proposed model would cope with the challenges faced by today's Internet (and that of the future). Our new architecture is based on the following principle: Application processes communicate via a distributed inter-process communication (IPC) facility. The application processes that make up this facility provide a protocol that implements an IPC mechanism, and a protocol for managing distributed IPC (routing, security and other management tasks). Existing implementation strategies, algorithms, and protocols can be cast and used within our proposed new structure.

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An improved method for deformable shape-based image segmentation is described. Image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. The quality of a candidate region merging is evaluated by a cost measure that includes: homogeneity of image properties within the combined region, degree of overlap with a deformed shape model, and a deformation likelihood term. Perceptually-motivated criteria are used to determine where/how to split regions, based on the local shape properties of the region group's bounding contour. A globally consistent interpretation is determined in part by the minimum description length principle. Experiments show that the model-based splitting strategy yields a significant improvement in segmention over a method that uses merging alone.