Structured light vision measurement method for 3D surface profiles of objects on the basis of K-means color clustering

A technology for object surface and color clustering, applied in measuring devices, optical devices, image data processing, etc., can solve problems such as strong adaptability and good robustness

Inactive Publication Date: 2013-09-11
HARBIN ENG UNIV
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Problems solved by technology

Based on the classic k-means clustering algorithm, it has been widely used in color classi

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  • Structured light vision measurement method for 3D surface profiles of objects on the basis of K-means color clustering
  • Structured light vision measurement method for 3D surface profiles of objects on the basis of K-means color clustering
  • Structured light vision measurement method for 3D surface profiles of objects on the basis of K-means color clustering

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Embodiment Construction

[0069] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0070] combine Figure 1-14 , the present invention proposes a structured light vision measurement method based on k-means color clustering of the three-dimensional contour of the object surface, which basically meets the requirements of large range, large field of view, high precision and simple information extraction for three-dimensional measurement of structured light vision . Specifically include the following steps:

[0071] A visual measurement method of colored structured light based on De Bruijn sequence, the required equipment and specific experimental steps are as follows:

[0072] Required equipment: 1. Projector 2. Camera 3. Computer 4 Object to be measured 5. The projector and camera are connected to computer 4 through cables.

[0073] Specific experimental steps:

[0074] Step 1: Use four colors of red, green, blue and magenta (n=4), select the ...

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Abstract

The invention aims to provide a structured light vision measurement method for 3D surface profiles of objects on the basis of K-means color clustering. The method includes: determining spatial lines in each color in the RGB space, calculating Euclidean distances of each pixel point to the spatial color lines, selecting the minimum distance, and ensuring that a corresponding color number is a most approximate color value of the current pixel point; adaptively regulating parameters by the pixel points with the same number, and fitting new spatial color lines; repeatedly iterating by the K-means clustering algorithm until the color value of each pixel point tends to be stable; sorting colors, acquiring a decoding sequence, determining the position of each matching point, and generating a matching point coordinate matrix; and acquiring depth information by triangulation so as to reconstruct the 3D surface of an object. The method has the advantages that accuracy in color sorting is improved, robustness and adaptive capability are high, and the problem of matching difficulty in binocular stereoscopic vision is solved.

Description

technical field [0001] The invention relates to a measurement method, in particular to a structured light vision measurement method. Background technique [0002] Structured light vision measurement is a new method for measuring the three-dimensional surface shape of objects. It has been widely used in industrial inspection, virtual reality, cultural relics protection and medical engineering in recent years due to its characteristics of non-contact, convenience, speed and high precision. Applications. A structured light vision measurement system generally consists of a projection device, a camera, and an image processing system. The principle of structured light vision measurement is to project one or more encoding patterns onto the measurement scene, capture the projected image of the scene with a camera, and then use triangulation to obtain depth information by matching the corresponding points of the projection pattern and the decoding pattern, thereby Get 3D informatio...

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Application Information

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IPC IPC(8): G01B11/25G06T9/00
Inventor 陆军高乐张鑫宋成业孙晓玲李积江宋景豪彭仲涛
Owner HARBIN ENG UNIV
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