Contrast Driven Elastica for Image Segmentation
| Autor: | Noha Youssry El-Zehiry, Leo Grady |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Segmentation-based object categorization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Initialization 02 engineering and technology Image segmentation Curvature Real image 01 natural sciences Computer Graphics and Computer-Aided Design 010101 applied mathematics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence 0101 mathematics business Gradient descent Software Mathematics |
| Zdroj: | IEEE Transactions on Image Processing. 25:2508-2518 |
| ISSN: | 1941-0042 1057-7149 |
| Popis: | Minimization of boundary curvature is a classic regularization technique for image segmentation in the presence of noisy image data. Techniques for minimizing curvature have historically been derived from gradient descent methods which could be trapped by a local minimum and, therefore, required a good initialization. Recently, combinatorial optimization techniques have overcome this barrier by providing solutions that can achieve a global optimum. However, curvature regularization methods can fail when the true object has high curvature. In these circumstances, existing methods depend on a data term to overcome the high curvature of the object. Unfortunately, the data term may be ambiguous in some images, which causes these methods also to fail. To overcome these problems, we propose a contrast driven elastica model (including curvature), which can accommodate high curvature objects and an ambiguous data model. We demonstrate that we can accurately segment extremely challenging synthetic and real images with ambiguous data discrimination, poor boundary contrast, and sharp corners. We provide a quantitative evaluation of our segmentation approach when applied to a standard image segmentation data set. |
| Databáze: | OpenAIRE |
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