A PARALLEL TECHNIQUE FOR SIGNAL-LEVEL PERCEPTUAL ORGANIZATION

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. We present the 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. 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.