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Evolution algorithm of parametric active contour model based on Gaussian smoothing filter

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Abstract

At present, the parametric active contour model is one of the most well-known and widely used image segmentation techniques in image processing and computer vision. However, its evolution computation is slow, which is a great obstacle to some applications such as real-time motion tracking. This paper not only reveals its bottleneck including the high computation cost of the inverse operation of matrix and the matrix multiplication in each iteration, but also proposes a novel scheme that transfers these time-consuming matrix operations into vector convolution operations for better performance. As shown by simulation results the proposed algorithm is always much faster than the conventional algorithm, and the velocity gain increases with the snaxels on the curve, from several times to over 2 orders of magnitude.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 52008340) and High-end Talent Fund of School of XiHua University (No. Z201130).

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Correspondence to Kelun Tang.

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Tang, K., Zhou, X. Evolution algorithm of parametric active contour model based on Gaussian smoothing filter. Machine Vision and Applications 33, 83 (2022). https://doi.org/10.1007/s00138-022-01336-4

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