Abstract: The present disclosure provides a data clustering method based on K-nearest neighbor, which sorts data points to be clustered in ascending order according to the maximum radiuses of K-nearest neighbors of the data points, that is, according to the density, and perform the first pass across the data points after sorting the data points in ascending order to incorporate the data points that conform to the statistical similarity into the same cluster; then perform the second pass across the data points with smaller cluster density according to the scale required during the clustering to find out all noise points and incorporate non-noise points into the nearest large-density cluster, so as to realize data clustering, which has the benefits of no need to preset the number of clusters and know the probability distribution of the data and convenience to set parameters.

Abstract: The present disclosure provides a data clustering method based on K-nearest neighbor, which sorts data points to be clustered in ascending order according to the maximum radiuses of K-nearest neighbors of the data points, that is, according to the density, and perform the first pass across the data points after sorting the data points in ascending order to incorporate the data points that conform to the statistical similarity into the same cluster; then perform the second pass across the data points with smaller cluster density according to the scale required during the clustering to find out all noise points and incorporate non-noise points into the nearest large-density cluster, so as to realize data clustering, which has the benefits of no need to preset the number of clusters and know the probability distribution of the data and convenience to set parameters.

Abstract: The invention relates to a turning radius-based corner detection algorithm, comprising: S1: removing noise by Gaussian filtering and computing a gradient value of each pixel of an original image; S2: locating a neighboring pixel with closest grayscale to the pixel within given neighborhoods therearound; S3: computing a turning radius between the pixel and the closest neighboring pixel thereof; S4: computing a turning radius threshold; S5: marking a pixel with the turning radius which is greater than the threshold and maximum in the given neighborhoods as a corner. By the solution, the present invention can locate corners in images accurately and restrain fake corners resulting from noises and textures, and also can simplify the computation of the threshold and raise the computation efficiency, whereby automatic detection is realized and effect of corner detection is improved. The invention is applicable to 3D reproduction, visual locating, measurement, etc.