Grading method of white gray cloth flatness based on low-dimensional image coding and integrated learning
A technology of image coding and integrated learning, which is applied in the field of flatness grading of white gray cloth, and can solve problems such as large amount of calculation
Active Publication Date: 2022-02-01
SUN YAT SEN UNIV
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However, this method has a large amount of calculation, and it needs to perform a filter bank convolution operation on each pixel of all the folded areas around the crease, which is almost close to pixel-by-pixel processing; secondly, the final image features have more than 5000 dimensions; the final part of this method The verification results also need to be further improved
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[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
[0025] figure 1 It is the overall flowchart of the embodiment of the present invention, such as figure 1 As shown, the method includes:
[0026] S1, image and label preprocessing;
[0027] S2, extracting the wrinkle feature center of the image based on the preprocessing result;
[0028] S3, encode the images in the data set based on the feature center;
[0029] S4, the establishment and verification of the rating reference system.
[0030] Step S1, specificall...
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The embodiment of the invention discloses a method for grading the flatness of white gray cloth based on low-dimensional image coding and integrated learning. This method mainly generates image codes through feature extraction, then generates learners through machine learning, and finally uses the idea of integrated learning to synthesize the results of multiple base learners to obtain the final result. Implement the embodiment of the present invention, use the method of computer automation to carry out objective and accurate rating to image flatness, use feature center histogram as image coding, greatly reduce coding dimension, reduce the amount of calculation of learner; use integrated learning The strategy provides a reliable guarantee for the final result, thereby reducing subjective errors while saving labor costs, and can achieve the rating ability of senior engineers in the grading results.
Description
technical field [0001] The invention relates to the field of pattern recognition and machine learning, in particular to a method for grading the flatness of white gray cloth based on low-dimensional image coding and integrated learning. Background technique [0002] Fabric flatness is an important index to measure the quality and appearance of fabrics, so it is very important to correctly evaluate the flatness of fabrics. At present, the textile industry can only evaluate fabrics through manual comparison with standard templates. However, because the relevant standards only provide 6 levels of 3D templates, and the subjective errors of manual evaluation and the differences between different people, the evaluation results The results have certain errors, and the reliability is not high. The flatness rating of the fabric image is a gradual and continuous process. The flatness level is from 3.2 to 1.0, and the wrinkles on the image are getting bigger and bigger. During the gra...
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IPC IPC(8): G06K9/62
CPCG06V10/40G06F18/23213G06F18/2415
Inventor 谢铮王若梅周凡林格
Owner SUN YAT SEN UNIV