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<p>The performance of the classic split-and-merge segmentation algorithm is severely hampered by its rigid split-and-merge processes, which are insensitive to the image semantics. The author proposes efficient algorithms and data structures to optimize the split-and-merge processes by piecewise least-square approximation of image intensity functions. This optimization aims at the unification of segment finding and edge detection. The optimized split-and-merge algorithm is shown to be adaptive to the image semantics and, hence, improves the segmentation validity of the previous algorithms. This algorithm also appears to work well on noisy sources. Since the optimization is done within the split-and-merge framework, the better segmentation performance is achieved at the same order of time complexity as the previous algorithms.</p>
adaptive split and merge segmentation; image segmentation; piecewise least-square approximation; image semantics; data structures; image intensity functions; optimization; edge detection; time complexity; edge detection; image segmentation; least squares approximations; optimisation

X. Wu, "Adaptive Split-and-Merge Segmentation Based on Piecewise Least-Square Approximation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 808-815, 1993.
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