基于自适应模糊阈值分割的韧窝图像分析

Image Processing of Dimples Based on Image Adaptive Thresholding by Index of Fuzziness

为了探明钢材韧窝直径与常规力学性能之间的关系,对其精确测取与统计,并尽量减小在测量分析中人为因素的影响,利用自适应模糊阈值分割法对韧性断裂产生的钢材等轴韧窝图像进行分析及二值分割,实现了韧窝与背景的分离。为了快速准确的测得韧窝直径,对基于Freeman链码的多区域面积的算法进行了改进。改进后的算法可高效、便捷、准确的测取韧窝直径。由此为后继的韧窝定量分类分析工作提供了依据。

In order to minimize the impact of anthropogenic factor and to study the relationships between dimple diameter and common mechanical properties, computer image processing technology is introduced to measure and count the size and number of dimples in metallographic image formed by ductile fracture. The dimples are segmented very well by using an adaptive method for image thresholding based on index of fuzziness. An improved multiregional area algorithm based on Freeman chain is presented. It proves that this algorithm can detect and measure dimple diameter rapidly and accurately. It also provides quantitative results for further classify and analysis of dimples.