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Minimum spanning tree-based segmentation : ウィキペディア英語版 | Minimum spanning tree-based segmentation Image segmentation strives to partition a digital image into regions of pixels with similar properties, e.g. homogeneity.〔R. Haralick, L. Shapiro: Image Segmentation Techniques. CVGIP 29 (January 1985)〕 The higher-level region representation simplifies image analysis tasks such as counting objects or detecting changes, because region attributes (e.g. average intensity or shape〔J. Iivarinen, M. Peura, J. Sarela, A. Visa: Comparison of combined shape descriptors for irregular objects. In: BMVC (1997) pp. 430–439〕) can be compared more readily than raw pixels. ==Motivation for graph-based methods== To speed up segmentation of large images, the work could be divided among several CPUs. One means of accomplishing this involves splitting images into tiles that are processed independently. However, regions that straddle a tile border might be split up or lost if the fragments do not meet the segmentation algorithm's minimum size requirements. A trivial workaround involves overlapping tiles, i.e. allowing each processor to consider additional pixels around its tile's border. Unfortunately this increases the computational load, since the processors on both sides of a tile boundary are performing redundant work. Also, only objects smaller than the tile overlap are guaranteed to be preserved, which means that long objects such as rivers in aerial imagery are still likely to be split. In some cases, the independent tiles' results can be fused to approximate the true results.〔M.-H. Chen, T. Pavlidis: Image seaming for segmentation on parallel architecture. PAMI Vol. 12(6), June 1990, pp. 588–594〕 An alternative exists in the form of graph-based segmentation methods. The connectivity information inherent to graphs allows performing independent work on parts of the original image, and reconnecting them to yield an exact result as if processing had occurred globally.
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