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Method for generating labeled sample based on computer graphic technology

A computer graphics and labeling technology, applied in computer parts, computing, instruments, etc., can solve the problems of low accuracy of data models and insufficient number of training samples, and achieve the goal of improving recognition efficiency, richness, and generation speed. Effect

Active Publication Date: 2022-06-17
深圳市华世智能科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] For this reason, the present invention provides a method for generating marked samples based on computer graphics technology, which can solve the technical problem of low accuracy of data models caused by insufficient training samples in prior art centers

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  • Method for generating labeled sample based on computer graphic technology

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Embodiment Construction

[0032] In order to make the purpose and advantages of the present invention clearer, the present invention will be further described below with reference to the embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0033] Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principle of the present invention, and are not intended to limit the protection scope of the present invention.

[0034] It should be noted that, in the description of the present invention, the terms “upper”, “lower”, “left”, “right”, “inner”, “outer” and other terms indicated in the direction or the positional relationship are based on the drawings. The direction or positional relationship shown is only for the conve...

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Abstract

The invention relates to a method for generating a marked sample based on a computer graphic technology, and the method comprises the steps: constructing a virtual scene according to preset scene units, each scene unit is provided with an attribute information array, and the scene units operate in an attribute range in the virtual scene; setting a virtual motion unit, and determining attribute information of the scene unit under the action of the virtual motion unit; recording image information of the virtual motion unit in the virtual scene at any moment, and recording motion attribute information corresponding to the image information and attribute information of the corresponding scene unit; and generating sample information, wherein the sample information comprises image information, motion attribute information and attribute information. By comparing the motion attribute information of the virtual motion unit with the information in the existing data sample, the actual motion attribute information is adjusted according to the relationship between the operation attribute information and the attribute value of the actual motion attribute information, so that the generation speed of the sample information is greatly improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method for generating marked samples based on computer graphics technology. Background technique [0002] Deep learning is a field driven by big data, and all current deep learning neural network algorithms will face the problem of training data noise. If there is too much noisy data in the training data, it is impossible to train a deep learning algorithm with good effect. Therefore, high-quality data has become a necessary condition for AI and deep learning systems, and it generally takes a lot of manpower and material resources to process the data. Denoise. [0003] However, in the prior art, a large amount of data is required for building a data model and training the data model. However, due to the limited historical data of some models, the amount of data for training the data model is insufficient, which will affect the data model. The accuracy ...

Claims

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Application Information

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IPC IPC(8): G06F16/583G06F16/215G06K9/62
CPCG06F16/583G06F16/215G06F18/214
Inventor 勾佳祺饶大林
Owner 深圳市华世智能科技有限公司
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