Abstract
When using polar dynamic programming (PDP) for image segmentation, the object size is one of the main features used. This is because if size is left unconstrained the final segmentation may include high-gradient regions that are not associated with the object. In this paper, we propose a new feature, polar variance, which allows the algorithm to segment the objects of different sizes without the need for training data. The polar variance is the variance in a polar region between a user-selected origin and a pixel we want to analyze. We also incorporate a new technique that allows PDP to segment complex shapes by finding low-gradient regions and growing them. The experimental analysis consisted on comparing our technique with different active contour segmentation techniques on a series of tests. The tests consisted on robustness to additive Gaussian noise, segmentation accuracy with different grayscale images and finally robustness to algorithm-specific parameters. Experimental results show that our technique performs favorably when compared with other segmentation techniques.
Original language | English (US) |
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Article number | 7585054 |
Pages (from-to) | 5857-5866 |
Number of pages | 10 |
Journal | IEEE Transactions on Image Processing |
Volume | 25 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2016 |
Keywords
- Dynamic programming
- image segmentation
- polar variance
- region growing
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design