A Time-Optimal Multiple Search Algorithm on Enhanced Meshes, with Applications

Bhagavathi, D.; Olariu, Stephan; Shen, W.; Wilson, Larry

doi:10.1006/jpdc.1994.1074

Cited by 27 publications

(10 citation statements)

References 13 publications

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“…Applications ranging from image processing [19], [27], to computer graphics and robotics [26], digital geometry and pattern recognition [5], [8], [9], [10], [18], [21], [25], to optimization [13], to other fundamental problems [2], [6], [7], [12] have found efficient solutions on this architecture and some of its variants [21].…”

Section: Introductionmentioning

confidence: 98%

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Podality-based time-optimal computations on enhanced meshes

Bokka

Gurla²,

Olariu

et al. 1997

IEEE Trans. Parallel Distrib. Syst.

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The main contribution of this paper is to present simple and elegant podality-based algorithms for a variety of computational tasks motivated by, and finding applications to, pattern recognition, computer graphics, computational morphology, image processing, robotics, computer vision, and VLSI design. The problems that we address involve computing the convex hull, the diameter, the width, and the smallest area enclosing rectangle of a set of points in the plane, as well as the problems of finding the maximum Euclidian distance between two planar sets of points, and of constructing the Minkowski sum of two convex polygons. Specifically, we show that once we fix a positive constant e, all instances of size m, n m n 1 2 + £ £ e e j of the problems above, stored in the first m n columns of a mesh with multiple broadcasting of size n n ¥ can be solved time-optimally in Q m n e j time.

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Section: Introductionmentioning

confidence: 98%

“…Refer toFig. 9for an illustration: Here, k = 3, M 1 contains the submeshes R 1,1 , R 2,2 , R 3,3 , R 4,4 , and M 2 contains the submeshes R 5,5 , R 6,6 , R 7,7 , R8,8 .…”

mentioning

confidence: 99%

Podality-based time-optimal computations on enhanced meshes

Bokka

Gurla²,

Olariu

et al. 1997

IEEE Trans. Parallel Distrib. Syst.

Self Cite

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show abstract

“…In turn, due to its commercial availability, the mesh with multiple broadcasting has attracted a great deal of attention. Applications ranging from image processing [21,31,34], to visibility and robotics [9,30], to digital geometry and pattern recognition [7,9,20,23,30], to optimization [14], to other basic problems [3,6,8,13] have been reported on this platform and some of its variants [23].…”

Section: Introductionmentioning

confidence: 99%

Time-Optimal Proximity Graph Computations on Enhanced Meshes

Olariu

Zomaya

1998

Journal of Parallel and Distributed Computing

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“…Being of theoretical interest, as well as commercially available, the MMB has attracted a great deal of attention. Applications ranging from image processing [33], [44], [48], to computer graphics and robotics [43], to computational geometry and pattern recognition [6], [7], [9], [10], [11], [13], [32], [42], to optimization [20], to query processing and mobile computing [15], and to other fundamental problems [4], [8], [19] have found efficient solutions on this architecture.…”

Section: Introductionmentioning

confidence: 99%

The mesh with hybrid buses: an efficient parallel architecture for digital geometry

Lin

Olariu

Schwing

et al. 1999

IEEE Trans. Parallel Distrib. Syst.

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AbstractÐThe first main contribution of this work is to propose an efficient VLSI architecture obtained by augmenting the Mesh with Multiple Broadcasting (MMB) with precharged 1-bit row and column buses. The new architecture, which we call Mesh with Hybrid Buses (MHB for short), is realizable in VLSI with no increase in the area or the wiring complexity of the MMB chip. Our second main contribution is to show that the MHB is extremely well-suited for solving an entire slew of digital geometry tasks. The MHB is not a reconfigurable architecture. Yet, quite remarkably, for a large number of fundamental digital geometry tasks, the MHB offers a level of performance previously attained only by reconfigurable architectures. Specifically, with a digital image pretiled onto a MHB of size n p Â n p one pixel per processor, we show that the problems of computing the convex hull of the image, computing the diameter and the width of the image, deciding whether a set of digital points is a digital line, computing the maximum distance between two images, deciding whether two images are linearly separable, computing several moments and low-level descriptors of the image, including the perimeter, area, center, and median row of its convex hull, can be solved in ylog n time. By contrast, the fastest possible algorithms for the problems above on the MMB run in Ân IaT time. Finally, we go on to show that, with minor changes, our algorithms can be implemented to run within cost-optimality on a MHB of size n p log n Â n p log n .

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A Time-Optimal Multiple Search Algorithm on Enhanced Meshes, with Applications

Cited by 27 publications

References 13 publications

Podality-based time-optimal computations on enhanced meshes

Podality-based time-optimal computations on enhanced meshes

Time-Optimal Proximity Graph Computations on Enhanced Meshes

The mesh with hybrid buses: an efficient parallel architecture for digital geometry

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