Linear feature extraction method for discrete data point set

Abstract

The invention discloses a linear feature extraction method for a discrete data point set. According to obtained mean value, variance and main axis direction, on the basis of clustering analysis, the method uses a coordinate mean value as an initial clustering center and uses the variance and the main axis direction as a constraint value to set an effective area and then calls step (4) and step (5) to further calculate the mean value and variance of data point coordinates in the effective area so as to obtain a new clustering center. Through the iteration, until the distance between a clustering center obtained through current calculation and a clustering center obtained through previous calculation is smaller than a preset threshold, and then using a linear feature, which is determined through using a currently obtained main axis direction as the slope factor to pass the coordinate mean value, as the linear feature. The linear feature extraction method for the discrete data point set is high in speed, high in accuracy and low in false detection rate.

Description

technical field [0001] The invention relates to a line feature extraction method of a discrete data point set. Background technique [0002] Feature detection plays an important role in the field of pattern recognition and digital image processing, and is a key step in the pattern recognition process. Image features are mainly divided into two categories: natural features and artificial features. Artificial features refer to the image features produced by man-made structures, such as histograms, spectrograms, chain codes, etc.; natural features refer to the inherent features of the image itself, which can be directly obtained through the human visual perception system, such as image edges, textures, etc. , shapes, points, lines, etc. Studies have found that the human visual system can quickly and accurately identify target objects, mainly because the human eye can directly extract high-dimensional features such as lines and surfaces of the target object. The extraction of...

AI Technical Summary

Problems solved by technology

However, when the traditional Hough transform is used to detect straight lines, there are problems such as not being able to determine the endpoints of the straight line, detecting false straight lines, and drawing additional straight lines symmetrically.

In addition, the researchers proposed a method to remove false straight lines by combining global threshold and local threshold, and use dynamic grouping principle to determine endpoints, but the threshold determination of this method is difficult and the algorithm is relatively complicated

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