基于改进的K-means聚类算法水下图像边缘检测

Underwater image edge detection based on improved K-means clustering algorithm

边缘检测被广泛用于图像分析与处理中,由于水的吸收和散射效应,传统的边缘检测算法对于水下图像得不到较好的效果。在此应用一种新的方法来得到较准确的水下图像边缘,首先,将原始图像使用暗原色先验算法进行处理得到较清晰的水下图像;然后,使用梯度幅值边缘检测算法检测出初始边缘,在初始边缘上检测出端点,使用改进的K-means聚类算法对端点进行分类,从而确定背景和目标灰度值接近的区域作为窗口;最后,在窗口内使用梯度幅值检测边缘,通过多个窗口的并集得到最终边缘。实验结果表明,边缘检测效果得到明显的改善。

Edge detection is widely used in image analysis and processing. The traditional edge-detection algorithms are al-ways ineffective to underwater images due to the absorption and scattering nature of seawater. A new approach is used in this pa-per to obtain the accurate edges of underwater images. Firstly,the original image is processed with the dark primary colour prior algorithm to get the clearer image. Then,the initial edge is calculated by the gradient magnitude edge detecting algorithm,the endpoints in the initial edge of the image are detected,and the modified K-means clustering algorithm is used to classify the endpoints to determine the region where the gray-scale of background and target is approximate as the window. Finally,the edge is detected in the window by the adaptive gradient magnitude,and then the final edge is got by union set of multiple windows. The edge detection result is significantly improved.