LED classification method based on manifold learning

Abstract

The invention discloses an LED classification method based on manifold learning, and the method comprises the following steps: S1, obtaining an image comprising an LED, and converting the image into agray image; S2, performing image masking on the area, except for the edge part area of the LED fluorescent glue, of the grayscale image in the step S1 to obtain a mask image; S3, performing dimensionreduction processing on the mask image obtained in the step S2 to obtain dimension reduction data; and S4, transmitting the dimension reduction data obtained in the step S3 to a classifier for classification, and obtaining LED classification of good LEDs, LEDs with large glue amount and LEDs with small glue amount. According to the method, the LED fluorescent glue edge annular image is separated,the nonlinear dimension reduction algorithm is combined to change the distribution of glue amount characteristics, the glue amount characteristics of the LED are extracted, the interference of redundant information on characteristic extraction is reduced, and the classification accuracy of a classifier is improved; and meanwhile, in the dimension reduction algorithm, a conditional probability function used in an iterative process is optimized, so that the overall time consumption of the algorithm can be reduced.

Description

technical field [0001] The present invention relates to the technical field of unsupervised pattern recognition and image processing, and more specifically, relates to an LED classification method based on manifold learning. Background technique [0002] In LED visual inspection, LEDs need to be divided into good products and defective products, and the defective products include two categories: high glue amount and low glue amount. The problem of the amount of glue highlights the difference at the edge of the fluorescent glue. After preliminary image processing, the image of the glue amount can be extracted. The image is actually a matrix, and its element value is the pixel value of the image. In this matrix, it is impossible to directly observe the characteristic distribution of the glue output, and it is even more difficult to use a classifier to directly classify the image. t-SNE is a nonlinear dimensionality reduction algorithm in manifold learning, which converts the ...

AI Technical Summary

Problems solved by technology

[0003] The present invention overcomes the defects of the prior art that the feature distribution of glue output cannot be directly observed, and it is more difficult to use a classifier to directly classify images, and provides a LED classification method based on manifold learning, which reduces the characteristic data of the matrix to a minimum dimension, improve the accuracy of feature analysis, and complete the classification of LED images

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