标题: 应用广义加权平均集成运算于影像边缘侦测
Applying Weighted Generalized Mean Aggregation to Edge Detection of Images
作者: 张一新
Chang, Yih-Sin
张志永
Chang, Jyh-Yeong
电控工程研究所
关键字: 边缘侦测;模糊关系;W-模糊关系;加权平均集成运算;参数学习;edge detection;fuzzy relation;W-fuzzy relation;weighted mean aggregation calculation;parameters learning
公开日期: 2012
摘要: 本论文主要包含两大部分。第一部分运用区间值模糊关系的概念,在任意一张灰阶影像中建构出足以显现3×3视窗内中心像素和其8邻域像素强度值变化之模糊边缘影像;第二部分,我们利用一组广义加权平均运算元,针对中心像素值进行加权平均差值集成运算,此运算可实现第一部分之模糊边缘影像,然后经由门槛值作用以获得边缘侦测结果。另外,我们也经由最速梯度演算法的概念进行平均集成参数值的学习,并提升边缘侦测正确率,意即我们已成功开发出一套能自动学习参数之影像边缘侦测方法。而从八张添加随机杂讯的灰阶合成影像测试结果显示,整合区间值模糊关系的技术与像素值加权平均集成演算法,将产生更为强健的边缘侦测响应。最后,藉由以最佳边缘侦测运算参数于自然影像边缘侦测的应用,我们发现其效果将更胜于着名的Canny边缘侦测器。
This thesis consists of two parts. The first part utilizes the concept of interval-valued fuzzy relations in any grayscale image to construct a fuzzy edge image. This fuzzy image shows the changes in intensity values between a 3×3 window central pixel and its eight neighbor pixels. In the second part, we employ a set of weighted generalized mean operands, and perform the weighted mean aggregation calculation for the central pixels. The calculation realizes the fuzzy edge images of the first part. Then, we obtain the image edge maps through a thresholding operation. Moreover, we make use of the steepest gradient method to learn the mean aggregation parameters, which in terms increase the edge detection accuracy. Namely, we have developed an automatic parameter learning mechanism for edge detection. By the testing results of eight grayscale synthetic images mixed with random noises, we have shown that the integration of interval-valued fuzzy relation technique with the weighted mean aggregation algorithm will lead to a more robust response for image edge detection. Finally, by applying the best parameters of edge detection to the edge detection of natural images, we have found that the effect is better compared to the popular Canny edge detector.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079912586
http://hdl.handle.net/11536/49278
显示于类别:Thesis


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