Image edge fitting B spline generating method based on clustering algorithm

Abstract

The invention provides an image edge fitting B spline generating method based on a clustering algorithm. By using the clustering algorithm, the gradient difference of edge discrete points generated by a canny algorithm is used as a clustering judging formula of the clustering algorithm, equidistant points are selected as initial cluster centers of the clustering algorithm, each kind of core is generated by the clustering algorithm in an iterative way, the core is used as a control point of the B spline, and the control point is fitted to generate a B spline curve; and the implementation steps are as follows: smoothly denoising an original image by using a first-order derivative of a two-dimensional Gaussian function so as to obtain a smooth image; by using 3x3 field, calculating an image gradient magnitude and a direction through calculating the differences of first-order partial derivatives in the x direction, the y direction, 45 degrees direction and 135 degrees direction within a pixel 8 field; selecting high and low thresholds and further filtering the high and low thresholds so as to obtain an edge point set; and establishing an edge point structural body array by a discrete edge point set. According to the invention, by using the clustering method as the control point of generating the B spline, the noise can be effectively inhibited and the edge detection fitting effect is enhanced.

Description

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AI Technical Summary

Benefits of technology

Inventions described in this patents involve an improved way that generates images with edges similar to those found at their original boundaries without adding any unnecessary detail or errors during processing. These techniques include algorithms like cynimic operators, splines, and iterative methods called cloning. By combining these feature functions into one framework, they allow users to create customized shapes quickly while maintaining accuracy over large areas.

Problems solved by technology

This patents describes how cluster analyzers work well with different types of problem addressed during their development. It explains that they can be useful tools for finding patterns within large datasets without being limited only at specific points where there may have been significant differences from another dataset. However, these techniques still require manual inputting of certain details about each object's structure beforehand.

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