Image Matching Algorithm based on Color Image Segmentation
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摘要: 在零件检测时由于背景复杂、零件表面光照不均匀且存在油污及废屑等问题，导致特征选取与模板制作困难，因此现有图像匹配算法容易出现漏识别与误识别，从而给实际应用带来困难。针对上述问题，提出了一种结合彩色图像分割的图像匹配算法。首先，将HSV颜色空间中各通道的直方图分布曲线方波化；其次，根据方波分布确定阈值，实现自适应阈值分割与目标提取；最后，利用边缘特征进行模板匹配。实验结果表明，该算法能够消除光照和背景噪声的影响，快速准确地分割出目标对象，降低特征选取与模板制作的难度，且算法消除了误识别，耗时短，准确率高，成功应用于工业生产中。Abstract: When the image matching algorithm is applied to inspecting components, there are many factors which can increase the difficulty of extracting features and creating templates, such as complex background, non-uniform illumination, oil and iron filings on the surface of the component. These problems lead to unidentification and misidentification for existing image matching algorithms, which will make image matching algorithms more difficult to be applied in the industries. In order to solve those problems, an image matching algorithm was proposed based on the color image segmentation. Firstly, the different gray curve of each channel in the HSV color space is processed as square wave. Secondly, the threshold used in adaptive image segmentation and extraction is determined with the distribution of square curve. Finally, the edge character information is used for template matching. The experiment results show that the algorithm can eliminate the impact of illumination and background noise, accomplish object segmentation fast and accurate, and reduce the difficulty of feature selection and template creation. The algorithm eliminates the misidentification, and achieves the accuracy with less times consuming. The present algorithm has been applied to industrial production successfully.
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Key words:
- HSV color space /
- color image segmentation /
- edge character /
- image matching
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表 1 分割效果及耗时统计
影响因素 因素水平 固定阈值分割算法 Otsu分割算法 本文算法 耗时/ms 分割效果 耗时/ms 分割效果 耗时/ms 分割效果 环境光 弱 27.5 过分割 51.3 过分割 42.4 保存完整 适中 30.8 保存完整 48.9 保存完整 43.8 保存完整 强 28.3 欠分割 50.5 保存完整 43.1 保存完整 背景噪声 高 28.6 保存完整 49.7 过分割 44.3 保存完整 
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表 2 匹配算法运行结果分析
图像匹配算法 漏识别率/% 误识别率/% 准确率/% 耗时/ms 基于灰度值的NCC模板匹配 1.8 1.2 97 191 基于边缘特征的ESD模板匹配 1.9 3.3 94.8 138 本文算法 0.2 0 99.8 104
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