一种曲面拟合图像边缘特征提取算法

doi:10.3969/j.issn.1001-2400.2011.03.027

Abstract

Abstract:

The edge contains much visual information of the image, so the image feature extraction is important in image processing. In this paper, the former least squares support vector machines(LS-SVM) edge feature extraction algorithm is analysed, and it is found that its universality is weaken. So this paper proposes a novel method for edge extraction, in which firstly the digital image is transfered to the fuzzy characteristic plane, where the image edge part is extruded, and the other part is weakened. The the image intensity surface is well fitted by the LS-SVM function, in which the first and second derivatives are calculated. Finally, the rather fine image edge feature can be gained. Experiments show that this algorithm can lead to a higher segmentation quality and that the parameters can be fixed, which is very useful in image processing.

Key words: fuzzy sets, curve fitting, edge detection, least squares support vector machines (LS-SVM), image processing

CLC Number:

TP391.4

DU Yaqin；HONG Bo；GUO Lei；YANG Ning. Novel curve fitting edge feature extraction algorithm[J].J4, 2011, 38(3): 164-168+188.

References

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Novel curve fitting edge feature extraction algorithm

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