基于改进自适应GACV的水下图像分割算法研究
Research of Underwater Image Segmentation Based on Improved Adaptive GACV Algorithm
DOI: 10.12677/AIRR.2018.71003, PDF,  被引量    国家科技经费支持
作者: 李社蕾*, 辛光红：三亚学院，信息与智能工程学院，海南 三亚
关键词: 活动轮廓模型；水平集；水下图像；图像分割 ；Active Contour Models； Level Set； Underwater Image； Image Segmentation
摘要: 论文针对水下彩色图像对比度低、模糊、偏色等退化问题，研究了几何活动轮廓模型(GACM)的基本理论，结合水下图像的特点，对自适应的GACV图像分割算法进行了改进，尝试设计新的权值函数，并对模型的数值实现方法进行了改进，建立了基于改进的自适应GACV图像分割算法的水下图像分割的数学模型，并对水下图像进行仿真。仿真结果表明，该算法对具有对比度低、图像模糊特点的水下图像实现了完全分割，尤其是水下模糊图像分割效果较好，为水下图像分割研究提供了参考。
Abstract: In this paper, the basic theory of geometric active contour model was studied, for the degradation problems of low contrast, fuzzy and color distortion of underwater image data. Combined with the character of underwater image the adaptive GACV image segmentation algorithm was improved, new weighting function was designed, the numerical realization of the model has improved, and a mathematical model of underwater image segmentation based on the improved adaptive GACV image segmentation algorithm was established, and the underwater images were emulated. The simulation results showed that the algorithm fully segmented underwater images with low contrast and fuzzy features. Especially the underwater fuzzy image segmentation was better, which provides a reference for underwater image segmentation research.
文章引用：李社蕾, 辛光红. 基于改进自适应GACV的水下图像分割算法研究[J]. 人工智能与机器人研究, 2018, 7(1): 25-33. https://doi.org/10.12677/AIRR.2018.71003

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