Abstract
We introduce a new segmentation method based on second-order energies. Compared to the related works it has the significantly lower computational complexity O(NlogN). The increased efficiency is achieved by integrating curvature approximation into a new bidirectional search scheme. Some heuristics are applied in the algorithm at the cost of exact energy minimisation. Our novel pseudo-elastica core algorithm is then incorporated into a user-guided segmentation scheme which represents a generalisation of classic first-order path-based schemes to second-order energies while maintaining the same low complexity. Our results suggest that, compared to first-order approaches, it scores similar or better results and usually requires considerably less user-input. As opposed to a recently introduced efficient second-order scheme, both closed contours and open contours with fixed endpoints can be computed with our technique.
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Krueger, M., Delmas, P., Gimel’farb, G. (2011). Efficient Image Segmentation Using Weighted Pseudo-Elastica. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_8
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DOI: https://doi.org/10.1007/978-3-642-23672-3_8
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