Abstract: There is described a Minimal Iterativity Anisotropic Diffusion (MIAD) approach that estimates the required number of iterations N as a function of the image structure-under-noise ? and the bound of the noise ?. The time step ? is related to the image structure-under-noise ? and to the bound of noise ?. The stopping time is calculated using T=?·N, and the edge strength ? is determined as a function of T, ?, and ?.

Abstract: There is described a Minimal Iterativity Anisotropic Diffusion (MIAD) approach that estimates the required number of iterations N as a function of the image structure-under-noise ? and the bound of the noise ?. The time step ? is related to the image structure-under-noise ? and to the bound of noise ?. The stopping time is calculated using T=?·N, and the edge strength ? is determined as a function of T, ?, and ?.

Abstract: An edge detection method employs binary morphological erosion. A binary image is generated from the gray-scale-value input image. A structure element is guided in a step-by-step manner across the binary image and generates an eroded binary image in accordance with an erosion rule. By forming the difference between the binary image and the eroded binary image, an output image containing the edges is generated. An output image which contains masked edges is generated through the use of a further erosion rule. The further erosion rule is based on a gray-scale value threshold and is applied to the eroded binary image to form a twice-eroded binary image. The difference between the twice-eroded binary image and the binary image forms the image which contains masked edges.