Pattern matching recognition system and implementing method thereof

Abstract

The invention provides a shape matching identification system and the implementation method thereof, aiming at the problems in the prior art after the color and shape identifications are researched. Firstly, a digital camera is adopted to shoot the object adhered with an artificial label for the three-dimensional reconstruction and form the picture of the object; then the shape characteristic and the color characteristic of the picture are extracted through the characteristic extraction module; the shape characteristic and the color characteristic are written into a characteristic vector and normalized through the characteristic vector normalization module; finally, the normalized characteristic vector is recognized by a neural network module. The invention has the advantages that the system and the implementation method adopt the mixed matching method based on color and shape to avoid the gray-scale based matching and the multiple repeated matching acknowledgements with large computational complexity; based on the Cartesian product combination of the angle point number characteristic in the color category and the space characteristic, the artificial label point characteristics with enough quantity are generated to enable the matching operation to be finished in one-step, so as to improve the matching efficiency greatly.

Description

【Technical field】

[0001] The invention relates to a recognition system and its realization method, in particular to a pattern matching recognition system and its realization method. 【Background technique】

[0002] With the development of technology, matching has always played an important role in the 3D reconstruction process based on camera calibration. In the process of 3D reconstruction of large scene objects, in order to facilitate the extraction of object feature points and reduce the difficulty, we will artificially paste some calibration points on the object. Due to the relatively large scene, it is impossible to ensure that the target at a fixed position is captured every time during the shooting process of the camera. To solve this difficulty, these calibration points are divided into two types according to size. The large points are called skeleton points, and the small points are called is an unknown point. First calculate the three-dimensional world coordinates...

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