Shape Priors for Image Segmentation

Cremers, Daniel

doi:10.1007/978-1-4471-5195-1_7

Cited by 1 publication

(2 citation statements)

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“…The method of image segmentation by active contours can be divided into three approaches: edge based, region based, and hybrid. Edge based active contours represented by [4]and the references therein use an energy driven by attraction to the edges of regions of interest in the image. For accuracy, the object of interest should have obvious boundaries usually represented by a dramatic change in the gradient values of the image.…”

Section: Active Contour Modelsmentioning

confidence: 99%

“…The difficulty in computational processing of natural images can be attributed to inherent statistical complexities of the image and a lack of homogeneity or saliency of local features at the same spatial or quantization scale [2]. As a result, segmentation of natural images remains a challenging task in image processing and computer vision.In recent literature, methods for segmentation of natural images include active contours [3][4][5], clustering methods [6][7][8][9],lossy data compression [10,11] and graph cuts [12].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Morphological Active Contour for Natural Images

Fox¹,

Milanova²,

Al-Ali³

2013

IJCSEA

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Morphological active contours for image segmentation have become popular due to their low computational complexity coupled with their accurate approximation of the partial differential equations involved in the energy minimization of the segmentation process. In this paper, a morphological active contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is coupled with a morphological edge-driven segmentation term to accurately segment natural images. By using morphological approximations of the energy minimization steps, the algorithm has a low computational complexity. Additionally, the coupling of the edge-based and region-based segmentation techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and report on the segmentation results using the Sorensen-Dice similarity coefficient.

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Section: Active Contour Modelsmentioning

confidence: 99%