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<p>Due to the potential for essentially unbounded scene complexity, it is often necessary to translate the sensor-derived signals into richer symbolic representations. A key initial stage in this abstraction process is signal-level perceptual organization (SLPO) involving the processes of partitioning and identification. A parallel SLPO algorithm that follows the global hypothesis testing paradigm, but breaks the iterative structure of conventional region growing through the use of alpha -partitioning and region filtering is presented. These two techniques segment an image such that the gray-level variation within each region can be described by a regression model. Experimental results demonstrate the effectiveness of this algorithm.</p>
scene interpretation; parallel algorithm; image segmentation; signal-level perceptual organization; symbolic representations; abstraction process; filtering; gray-level; filtering and prediction theory; knowledge representation; parallel algorithms; pattern recognition; picture processing

A. Chiu, R. Jain and S. Liou, "A Parallel Technique for Signal-Level Perceptual Organization," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 317-325, 1991.
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