The second highlight of this paper is developed a wavelet edge detect and shadow removal algorithm based on illuminance information for foreground objects segmentation. Shadow removal is a well known difficult problem in computer vision.
本文的另一亮点是提出了基于感兴趣区的小波边缘检测算法和基于亮度信息的前景目标分割中的阴影消除算法。
参考来源 - 复杂天气条件下交通监控系统目标检测与跟踪技术研究·2,447,543篇论文数据,部分数据来源于NoteExpress
The second step is to detect the edge point by microcosmic operator.
然后利用微观检测算子检测出微小型零件的边缘点;
The third part USES wavelet analysis and Mathematical Morphology to detect the image edge, and contrasts the two results, we have gotten respective apply condition and good or evil.
第三部分分别使用小波分析和数学形态学进行了数字图象边缘检测,并将两者的检测结果进行了对比,得出了各自的适应条件和优劣之处。
Experimental results shows that obtaining threshold by image partition and restricting the range of the modulus can accomplish better edge detect results.
实验结果表明,采用图像分块方法确定阈值,并用该阈值来限定模值,可以得到更好的边缘检测效果。
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