Corner detection using iterative Gaussian smoothing with constant window size
Corner detection using iterative Gaussian smoothing with constant window size
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DOI：
10.1016/0031-3203(95)00046-3
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发表时间：
1995-11
期刊：
影响因子：
--
通讯作者：
B. Ray;K. Ray
中科院分区：
文献类型：
--
作者：
B. Ray;K. Ray
针对现有的变窗口高斯平滑方法，提出了一种常窗口迭代高斯卷积方法。迭代过程中收敛的卷积矩阵的范数小于单位。对于一个封闭的数字曲线的迭代次数被证明是相关的曲线上的点的数量。用于平滑曲线的高斯滤波器系数被示出享受尺度空间属性。提出了一个尺度空间映射，它显示了迭代过程中绝对曲率最大值的位置。根据对不同角点模型（如Γ模型、END模型和STAIR模型）的尺度空间行为的分析，将地图转换为树组织。在解释树的过程中检测角。角点检测器已成功地应用于不同的数字曲线，即使在存在加性白色高斯噪声和在不同的方向。
In contrast to the existing Gaussian smoothing process with varying window size, an iterative Gaussian convolution with constant window size is proposed. The iterative process is shown to converge as the norm of the convolution matrix is less than unity. For a closed digital curve the number of iterations is shown to be related to the number of points on the curve. The Gaussian filter coefficients that are used to smooth the curve are shown to enjoy the scale-space property. A scale-space map showing the location of the maxima of absolute curvature over iterations is proposed. The map is converted into a tree organization on the basis of an analysis of the scale-space behavior of different corner models such as Γ models, END models and STAIR models. Corners are detected in a process of interpreting the tree. The corner detector has been applied successfully on different digital curves even in presence of additive white Gaussian noise and at varying orientations.