A Color Classification Method of Bamboo Strips Based on KNN Algorithm

Abstract

The invention discloses a method for classifying the color of bamboo strips based on the KNN algorithm, and relates to the technical field of bamboo strip processing methods. The invention proposes a method for classifying the color of bamboo strips based on the KNN algorithm. By extracting the hue and color saturation of the image, the The Otsu segmentation method is based on the color tone feature of the image, and calculates the tone contrast, and then classifies the bamboo strips by analyzing and comparing the characteristic distance between the bamboo strips to be classified and the bamboo strips that have been classified. The method of the present invention is convenient to implement, supports incremental learning, has strong anti-interference ability to the noise on the surface of bamboo strips, and achieves a high classification accuracy rate; can improve production efficiency, reduce labor force, and reduce labor intensity and An important measure to ensure the quality of bamboo classification.

Description

technical field [0001] The invention relates to the technical field of bamboo strip processing methods, in particular to a color classification method. Background technique [0002] In the production of modern bamboo products, sorting bamboo strips of different colors has become a key production procedure. At present, the classification methods of domestic bamboo products enterprises are mostly manual, and the speed of human eye resolution is limited. The classification results are easily affected by subjective factors, and misclassifications and misclassifications are prone to occur. Replacing the artificial color classification of bamboo strips with advanced automatic color classification technology is an important measure to improve production efficiency, reduce labor, reduce labor intensity and ensure the quality of bamboo strip classification. [0003] The color classification of bamboo strips currently uses the following two algorithms the most. The first one is to co...

AI Technical Summary

Problems solved by technology

The selected features cannot be too single. Although the single feature is easy to implement in terms of algorithm and can meet the application requirements in terms of speed, it cannot cope with various types of bamboo products, and the anti-interference performance will be poor; it cannot be too comprehensive, otherwise it will be greatly affected. Increase the complexity of the algorithm in practice

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