The Community for Technology Leaders
Green Image
<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.H. Chiu, R.C. Jain, S.P. Liou, "A Parallel Technique for Signal-Level Perceptual Organization", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 317-325, April 1991, doi:10.1109/34.88567
91 ms
(Ver 3.3 (11022016))