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Edge feature analysis by a vectorized feature extractor and in multiple edges

机译:通过矢量化特征提取器并在多个边缘中进行边缘特征分析

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In this paper we propose a method to generalize a 1-D edge feature extractor to 2-D and clarifies its properties for the edges located closely each other (i.e., multiple edges). In a previous paper (1995) we stated necessity of a measure which can discriminate a clear edge with small edge height from a noisy edge with large edge height. Then we claimed that the edge features be viewed as a composition of edge height and edge reliability, based on the analysis of variances within a window around the edge. From this analysis, we constructed an edge feature extractor in 1-D. In this paper we generalize our 1-D edge feature extractor to a 2-D vectorized edge extractor and prove it able to calculate an edge orientation and edge height accurately and also to reduce computation time. Experiments show this clearly. The same vectorization technique is also applicable to the Canny's operator (1986). In multiple edges case, we made the conditions clear for calculating accurate edge locations. Since most edge detection methods use local maximums of the edge height function to detect edge points, we found the conditions by differentiating the edge height function.
机译:在本文中,我们提出了一种将一维边缘特征提取器概括为二维的方法,并阐明了彼此相邻的边缘(即多个边缘)的属性。在先前的论文(1995年)中,我们指出了一种措施,可以区分边缘高度较小的净边缘和边缘高度较大的噪声边缘。然后,我们根据对边缘周围窗口内方差的分析,声称边缘特征被视为边缘高度和边缘可靠性的组合。通过此分析,我们构造了一维的边缘特征提取器。在本文中,我们将一维边缘特征提取器推广到了二维矢量化边缘提取器,证明了它能够精确地计算边缘方向和边缘高度,并减少了计算时间。实验清楚地表明了这一点。相同的矢量化技术也适用于Canny's运算符(1986)。在多边缘情况下,我们为计算精确的边缘位置提供了明确的条件。由于大多数边缘检测方法都使用边缘高度函数的局部最大值来检测边缘点,因此我们通过微分边缘高度函数来找到条件。

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