Purple soil image segmentation and extraction method based on adaptive density peak clustering

Abstract

The invention provides a purple soil image segmentation and extraction method based on adaptive density peak clustering, and the method comprises the following steps: S1, carrying out the separable gray scale transformation of a purple soil color image containing a purple soil region, and obtaining a gray scale image I; S2, performing preliminary segmentation on the grayscale image I by using an adaptive density peak clustering algorithm to obtain a binary image II after preliminary segmentation; S3, performing boundary extraction processing on the binary image II to obtain a boundary matrix of the purple soil area; S4, filling the extracted boundary matrix to obtain a binary image III; and S5, solving the Hadamard product of the binary image III and the color image containing the purple soil area to obtain a segmented image only containing the purple soil image. According to the method, the purple soil region image can be accurately and completely segmented from the background, the self-adaptive segmentation of the purple soil is realized in the segmentation process, and the method has the beneficial technical effects of high segmentation speed, accuracy and completeness.

Description

technical field [0001] The invention relates to an image segmentation and extraction method, in particular to a purple soil image segmentation and extraction method based on adaptive density peak clustering. Background technique [0002] With the continuous development of artificial intelligence technology, it has been widely used in many fields of production and life; among the many branches of artificial intelligence, machine vision has developed rapidly. Simply put, machine vision is to replace human eyes with machines. Measure and judge. In agricultural production, the identification of soil types and their genus depends entirely on agricultural experts. As we all know, the identification of soil is very important in agricultural production, because soil identification requires high professional skills of the identification person, such as the existing The identification of soil types and their soil genus is completely dependent on agricultural experts, hindering the po...

AI Technical Summary

Problems solved by technology

Machine vision soil recognition is to identify soil images with complex backgrounds taken under natural conditions in the field and accurately determine the soil type of the soil. However, the color images of purple soil taken in natural environments generally contain crops, lichens, mosses, Complex backgrounds such as grass will interfere with machine vision recognition

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