Auto Parts Recognition Method Based on Spatial Shape Context Features

A space context and auto parts technology, applied in computer parts, character and pattern recognition, image analysis, etc., can solve problems such as matching errors, misleading auto repair workers, and inaccurate recognition, so as to improve operating efficiency and accuracy , the effect of saving time
CN105469402BActive Publication Date: 2019-03-29DALIAN ROILAND SCI & TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
DALIAN ROILAND SCI & TECH CO LTD
Publication Date
2019-03-29

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Abstract

An auto parts recognition method based on spatial shape context features, which belongs to the field of auto parts recognition, is used to solve the problem of auto parts recognition. The features are extracted online, and are matched and identified with the auto parts in the offline auto parts feature library. The effect is that the incomplete extraction of image shape features caused by single-direction image acquisition is avoided.
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Description

technical field

[0001] The invention belongs to the field of recognition of auto parts, in particular to an auto part recognition method based on spatial shape context features. Background technique

[0002] With the continuous development of the automobile industry, the types of automobiles and the types of automobile parts have also increased. For auto repair workers, the human brain alone cannot accurately remember the model, price, scope of application, etc. of all automobile parts. information, there is an urgent need for a wearable device to help auto repair personnel identify auto parts. The target recognition algorithm of smart glasses is the most important thing. Correct recognition will bring unprecedented convenience to auto repair workers, and wrong recognition will mislead auto repair workers. Existing auto parts recognition algorithms are generally based on feature recognition of two-dimensional images. Two-dimensional image features are useless for occlusion ...

Claims

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