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

Technical field: [0001] The invention relates to the technical field of image edge fitting, and relates to a method for generating B-spline curves based on image edge fitting based on a clustering algorithm. The edge point set generated by using the canny operator not only effectively extracts edge points of useful information, but also Effectively suppress noise. Background technique: [0002] Cluster analysis, also known as group analysis, is a statistical analysis algorithm for studying classification problems. Its main purpose is to discover the structural characteristics of data sets through reasonable planning of data sets. Clustering is the process of grouping or classifying physical or abstract data objects according to the similarity between objects, which is widely used in various research and application fields, such as data mining, image segmentation, pattern recognition and many other aspects. [0003] Image edge detection is one of the important research conte...

AI Technical Summary

Problems solved by technology

The processing results of most of the existing image edge detection algorithms are some discrete edge point sets, which are difficult to directly use in some production practices

[0004] The edge of the image has rich local information and contains many features of the image, but the edge curve is irregular, which is difficult to describe and apply

There are many fitting methods applied to images, such as curve fitting based on gray histogram, curve fitting based on least squares, two-dimensional Gaussian surface fitting algorithm, etc., but in the process of extracting digital images, there are various Affected by various factors, blurring, distortion, noise interference and other phenomena often appear, resulting in image degradation and distortion

The traditional Canny algorithm has defects in the gradient magnitude calculation, and the Canny edge detection algorithm uses the gradient magnitude-based double threshold method to detect and connect edges. Although the noise is suppressed, some low-intensity edges are also damaged.

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