A parallel technique for signal-level perceptual organization

Abstract

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 α-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