Threshold value dividing method based on single-pixel in three-dimensional scanning system

Abstract

The single-pixel-based threshold partition method in 3D scanning system comprises: 1. designing a group of grating projection patterns for successive dimidiate; wherein, using the nth coarse grating image to dimidiate the stripes in the (n-1)th image and obtain 2n black-white alternative stripes; projecting patterns to target object, using CCD to take shoot back to a computer; 2. as the binary code should meet selfstandarization request in space, searching gray value for every pixel point in projection patterns, and determining the optimal threshold T by gradual approach and iterative means; 3. taking threshold partition.

Description

technical field [0001] The invention is a single-pixel-based threshold value segmentation method realized by a three-dimensional scanning system according to spatial binary codes in reverse engineering. The invention belongs to the technical field of image three-dimensional information reconstruction. Background technique [0002] Reverse Engineering (Reverse Engineering, RE) technology is a new technology that appeared in the field of advanced manufacturing in the late 1980s. It generally includes four basic links: three-dimensional shape detection and conversion (obtaining physical data), data preprocessing (point Cloud processing, recognition, multi-view splicing), CAD model establishment (surface reconstruction), CAM part molding. Most of the research on reverse engineering mainly focuses on the reverse reconstruction of the object, that is, the reconstruction of the CAD model of the product object and the manufacturing of the final product, which is called "reverse eng...

AI Technical Summary

Problems solved by technology

Some of these threshold segmentation algorithms mainly focus on the distinction between the foreground and the background, but do not distinguish the subtleties of the image well, and some emphasize the division of details, but do not have enough grasp of the whole.

Moreover, most image segmentation technologies are based on the grayscale or other parameters of adjacent pixels in a picture. The segmentation of a certain pixel depends on the characteristics of the whole image, and the anti-noise ability is not strong.

For fringe images with poor contrast, general threshold segmentation is applied to raster fringe images, and it is difficult to obtain accurate segmentation results that distinguish black and white stripes.

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