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Underwater image edge detection based on K-means algorithm

机译:基于K-means算法的水下图像边缘检测

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Edge detection is widely used in image analysis and processing. The traditional edge-detection algorithms are always ineffective to underwater images due to the absorption and scattering nature of seawater. In this paper a new approach is used to obtain the accurate edges of underwater pipeline. Firstly, we use the dark channel prior method to get the clear original image. Then, we calculate the processed image's gradient, and then the endpoints in the original edge image are detected. Next, the modification K-means clustering algorithm is used to classify the endpoints. Finally, the multiple windows using adaptive gradient magnitude are merged to get the final edge map. The edge detection result is significantly improved.
机译:边缘检测广泛用于图像分析和处理。由于海水的吸收和散射特性,传统的边缘检测算法始终对水下图像无效。本文采用一种新方法来获得水下管线的精确边缘。首先,我们使用暗通道先验方法获得清晰的原始图像。然后,我们计算处理后的图像的梯度,然后检测原始边缘图像中的端点。接下来,使用修正的K均值聚类算法对端点进行分类。最后,将使用自适应梯度幅度的多个窗口合并以获得最终的边缘图。边缘检测结果显着改善。

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