Hypercube and shuffle-exchange algorithms for image component labeling
Journal of Algorithms
Hypercube algorithms: with applications to image processing and pattern recognition
Hypercube algorithms: with applications to image processing and pattern recognition
Orthogonal multiprocessor sharing memory with an enhanced mesh for integrated image understanding
CVGIP: Image Understanding
Parallel algorithms for regular architectures: meshes and pyramids
Parallel algorithms for regular architectures: meshes and pyramids
Computing connected components on parallel computers
Communications of the ACM
Computational Geometry A Survey
IEEE Transactions on Computers
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The paper proposes a new architecture, which has the potential to support low-level image processing as well as intermediate and high-level vision analysis efficiently. The integrated architecture consists of a mesh of processors enhanced with an efficient recursive network. Low-level image processing is performed on the mesh processor, while intermediate and high-level vision analysis is performed on the recursive network. The interaction between the two levels is supported by parallel data and control paths. To illustrate the power of such a two-level system, we present efficient parallel algorithms for a variety of problems from low-level image processing to high-level vision. Representative applications include image component labeling convexity problems, and histogram-type operations. Through time complexity analysis, we show that the integrated architecture meets the requirements of many image understanding tasks.