A Data Parallel Algorithm for Solving the Region Growing Problem on the Connection Machine

Copty, Nawal; Ranka, Sanjay; Fox, Geoffrey; Shankar, Ravi

doi:10.1006/jpdc.1994.1049

Cited by 22 publications

(8 citation statements)

References 9 publications

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“…According to the above rule, pixel (7,4,9) and (7, 5, 11) in block (1, 0) have as predecessor pixel (6, 5, 7) in block (0, 0). Therefore, [ (7,4); (6,5)] and [ (7,5); (6,5)] are stored and (7, 4) and (7,5) get unique labels in block (1, 0).…”

Section: The Parallel Watershed Algorithmmentioning

confidence: 99%

“…Therefore, [ (7,4); (6,5)] and [ (7,5); (6,5)] are stored and (7, 4) and (7,5) get unique labels in block (1, 0). Similarly, in block (1, 1), pixel (7,6,9) is preceded by (6,5,7) in block (0, 0) and (7,7,10) by (6,6,7) in block (0, 1). Consequently, [ (7,6); (6,5)] and [ (7,7); (6,6)] are stored and (7, 6) and (7,7) are individually labeled.…”

Section: The Parallel Watershed Algorithmmentioning

confidence: 99%

“…This is the case of pixels (1, 6) in block (0, 1), (7,9) in block (1, 1), and (8, 5) in block (1, 0) (see Figs. 2, 3, and 6a).…”

Section: The Parallel Watershed Algorithmmentioning

confidence: 99%

“…Although several software and hardware algorithms, experimental studies, and surveys on parallelization of various techniques for region growing and labeling of connected components in both binary and gray scale images can be found in the literature [1], [2], [3], [4], [8], [9], [11], [12], [14], [18], [19], [32], [35], the proposed new parallel watershed algorithm is developed by regarding the watershed transformation as both a labeling of connected components-catchment basins-method and a region growing technique.…”

Section: Introductionmentioning

confidence: 99%

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Parallel image component labelling with watershed transformation

Moga

Gabbouj

1997

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-The parallel watershed transformation used in gray scale image segmentation is reconsidered in this paper on the basis of the component labeling problem. The main idea is to break the sequentiality of the watershed transformation and to correctly delimit the extent of all connected components locally, on each processor, simultaneously. The internal fragmentation of the catchment basins, due to domain decomposition, into smaller subcomponents is ulteriorly solved by employing a global connected components operator. Therefore, in a pyramidal structure of master-slave processors, internal contours of adjacent subcomponents within the same component are hierarchically removed. Global final connected areas are efficiently obtained in log 2 N steps on a logical grid of N processors. Timings and segmentation results of the algorithm built on top of the Message Passing Interface (MPI) and tested on the Cray T3D are brought forward to justify the superiority of the novel design solution compared against previous implementations.

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Section: The Parallel Watershed Algorithmmentioning

confidence: 99%

Section: The Parallel Watershed Algorithmmentioning

confidence: 99%

“…This is the case of pixels (1, 6) in block (0, 1), (7,9) in block (1, 1), and (8, 5) in block (1, 0) (see Figs. 2, 3, and 6a).…”

Section: The Parallel Watershed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parallel image component labelling with watershed transformation

Moga

Gabbouj

1997

IEEE Trans. Pattern Anal. Machine Intell.

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“…Segmentation of images can be broadly divided into region-based techniques [3], where the continuity of image elements with uniform properties is extracted, and the edge-based techniques [4], where the contour of a region is extracted from the discontinuity of image properties. Hybrid techniques [5] in which both of the above techniques are combined are also known.…”

Section: Introductionmentioning

confidence: 99%

Parallel image segmentation with adaptive mesh

Lee

Tanaka

2002

Systems & Computers in Japan

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We propose a parallel segmentation algorithm for general images which does not require advance knowledge of the image and where no initial assumptions about the number and position of regions are needed. This algorithm splits the image into a set of small triangular regions with uniform image properties, and the hierarchical construction is based on a parallel adaptive mesh algorithm. Based on a recursive binary traversal of discontinuities of image properties, hierarchical binary split and merge processing is performed. In this paper, a region is defined as space of the image where the features have smooth variation. By evaluating the homogeneity of the region using local differential properties representing the smoothness of the image, the problems of the starting point of processing and of order dependence are solved. Matching between blocks is ensured by sharing the small overlapping space that is needed to preserve the continuity of local properties. Further, it is possible to extract a region boundary line that matches the discontinuities of the local properties. Experiments performed on artificial intensity images, color real images, and range real images confirmed the universality and effectiveness of the proposed algorithm.

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