A Parallel Framework for Image Segmentation Using Region Based Techniques
| Autor: | Juan C. Pichel, Francisco F. Rivera, David E. Singh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Pixel
business.industry Computer science Segmentation-based object categorization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Image segmentation Image texture Minimum spanning tree-based segmentation Region growing Segmentation Computer vision Artificial intelligence business |
| Zdroj: | Vision Systems: Segmentation and Pattern Recognition |
| Popis: | Segmentation is the partitioning of an image into multiple regions (sets of pixels) according to a given criterion. The goal of segmentation is typically to locate objects of interest within the image. A wide variety of methods and algorithms are available to deal with the problem of the segmentation of images (Fu and Mui, 1981; Haralick and Shapiro, 1985; Pal and Pal, 1993). These methods can be broadly classified into four categories (Zhu and Yuille, 1996): • Edge-based techniques. • Region-based techniques. • Deformable models. • Global optimization approaches. The edge-based techniques are based on information about the boundaries of the image. Therefore, they try to locate the points in which abrupt changes occur in the levels of some property of the image, typically brightness (Canny, 1986; Rosenfeld and Kak, 1982). On the other hand, those methods that use spatial information of the image (e.g. color or texture) to produce the segmented image fit into the region-based techniques (Chen et al., 1992; Sahoo et al., 1988). These methods depend on the consistency of some relevant property in the different regions of the image. The deformable models are based on curves or surfaces defined within an image that moves due to the influence of certain forces. They can be classified into various groups, principally snakes, deformable templates and active contours (Blake and Isard, 1998; Kass et al., 1988). All of these techniques avoid the use of a global criterion when segmenting the image, which is contrary to the global optimization approaches (Geman and Geman, 1984; Kanungo et al., 1994). In this work a unified framework for image segmentation is proposed. The technique consists of two stages: a parallel seeded region growing algorithm (PSRG) and a region merging heuristic (RM). In Figure 1 the functional scheme of the proposed algorithm is shown. In the first step, different segmentations, performed in parallel, of the same input image are obtained. Each of these segmentations, which from now on will be called partial segmentations, are also generated in parallel using different number of processors. This way, the region growing algorithm uses a two level parallelism. Next, a region merging heuristic is applied to the oversegmented image created as result of combining the different initial |
| Databáze: | OpenAIRE |
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