The optimal segmentation scheme leads to simple object track ing methods by deriving the segmentation of a frame in a sequence from the final segmentation of the preceding frame. This introduces additional knowledge into the procedure, and only a few iterations are needed to reach final segmentation at each frame. Two tracking methods were tested and are described in this section.In the first tracking scheme, the final segmentation of a frame is used as the initial segmentation for the following frame. This approach is appropriate when the movement of the objects from frame to frame is small relative to their size and is simple to implement.The second tracking method compares the initial segmentation of a new frame to the final segmentation of the preceding frame. After computing the center of gravity for each label, a correspon dence is established between labels in the two frames to minimize size and location differences. After the correspondence has been established, an estimate is available for the displacement of each object. In updating the labels of each frame, a weight is given to the labeling of the preceeding frame, shifted by the computed displacement estimate. Fig. 5 shows a sequence of three frames. The initial and final segmentation of the first frame, after 12 iterations of ordinary optimization, are displayed in Fig. 6. Segmentation of the second and third frames with two iterations only, for different methods, is displayed in Fig. 7. The two tracking algorithms show much improved results compared with ordinary optimization. VI. CONCLUDING REMARKSAn optimization method has been presented that can perform classification based on optimization of a cost function. Experi ments were performed for image segmentation with cost func tions that include roughness, discrepancy from original image, and fitting the gray level gradient. The general method is not limited to this class of functions only; any function appropriate to the problem can be used. This method can also be used for simple tracking algorithms in sequences of frames. It has also been applied to solve substitution ciphers [11], resulting in fast and accurate solutions.Abstract-The information structures associated with deterministic de centralized control are examined. Conditions are derived under which such structures are partially nested. The tuning regulator approach and the sequential optimization approach are used as examples to examine these conditions in the context of concrete decentralized controller design philos ophies.
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