首页> 外国专利> KERNAL APPROXIMATION ON FRACTIONAL DIFFERENTIAL OPERATOR FOR EDGE DETECTION

KERNAL APPROXIMATION ON FRACTIONAL DIFFERENTIAL OPERATOR FOR EDGE DETECTION

机译:分数阶微分算子的边缘检测核逼近

摘要

Methods are described for detecting an edge of an object within an image with a fractional differential operator. Methods are also described for calculating 3D information using a strip pattern and the disclosed edge detection method. The fractional differential operator may be a modified Riesz space fractional differential operator. When calculating the fractional derivative of the image, a kernel approximation, in which a scaled Gaussian kernel function or a simplified scaled Gaussian kernel function, is applied to discretize the modified Riesz space fractional differential operator locally. The disclosed method improves the accuracy of edge detection, eliminates the need of applying additional fractional integration for noise suppression, and requires a smaller mask length to achieve desired accuracy.
机译:描述了用于使用分数微分算子检测图像内的对象的边缘的方法。还描述了用于使用带状图案和所公开的边缘检测方法来计算3D信息的方法。分数微分算子可以是修改的Riesz空间分数微分算子。在计算图像的分数导数时,将应用近似的核逼近,其中使用缩放的高斯核函数或简化的缩放的高斯核函数,以局部离散化修改后的Riesz空间分数微分算子。所公开的方法提高了边缘检测的精度,消除了应用额外的分数积分来抑制噪声的需要,并且需要更小的掩模长度以实现期望的精度。

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