Robust and Accurate Surface Measurement Using Structured Light IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 57, NO. 6, JUNE 2008 Rongqian Yang, Sheng Cheng, Wei Yang, and Yazhu Chen PPT 製作 100 % Adviser : 謝銘原 Student: 謝琮閔 ID : M

Outline Abstract Introduction System design Structured light exaction and coding procedure Experimental results Discussion and conclusion

Abstract 3-D surfaces using a binocular system Each structured light sheet is fitted to a conicoid Zero-crossing detection algorithm with Steger’s detector Linked line with the gray code to avoid producing outliers caused by erroneous decoding

INTRODUCTION Structured light measurement methods for 3-D coordinate acquisition have been rapidly developing and have been applied in various domains. The accuracy of measurement is related to the distortion of LCD projector lens and the subpixel location of stripes.

INTRODUCTION Classic stereo vision system Steger’s curvilinear structure detection method Gray code Dynamic threshold Per-pixel varying threshold and oversampling

INTRODUCTION Each structured light sheet is regarded as a conicoid Ridgeline linking algorithm is also employed to link the subpixel points in a stripe and construct a data list Gray code separates the measurement area into a great number of divisions

SYSTEM DESIGN System employs two digital cameras to reduce the shading area on the object surface The system model, as shown in Fig. 1, consists mainly of two charge-coupled device (CCD) cameras and one LCD projector.

SYSTEM DESIGN

In Fig. 1, indicate the left, right, and global coordinate systems These coordinate systems can be related to each other using the following formulas:

SYSTEM DESIGN represent the coordinates of an arbitrary point in the three coordinate systems are the rotation matrices and translation vectors from the left or right camera coordinate system to the global coordinate system are the rotation matrix and the translation vector from the left coordinate system to the right coordinate system

SYSTEM DESIGN Each structured light sheet is regarded as a conical surface, which can be approximated using the following conicoid equation: The structured light calibration procedure is followed to ascertain µ.

STRUCTURED LIGHT EXACTION AND CODING PROCEDURE The stripes in an image can be considered as a curvilinear structure, and the subpixel location of stripes can be obtained by the Steger’s curvilinear structure detector Steger’s detector and the zero-crossing detection method are integrated A line linking algorithm is introduced to group the subpixel points at each stripe

A. Subpixel Location Detection Eigenvalues and eigenvectors of the Hessian matrix : The subpixel location is, where :

A. Subpixel Location Detection The ridges at the pixels satisfy (6). An original image and the binary image with the marked ridge points are shown in Fig. 2(a) and (b)

A. Subpixel Location Detection Zero-crossing detection method is combined with Steger’s detector. Let A function is thus constructed as :

A. Subpixel Location Detection If the result is, then the points and are marked as potential ridge points for they have different signs Fig. 2(c) shows the marked image that fuses Fig. 2(b) and the zero-crossing detection result

B. Linking Algorithm From the subpixel location algorithm, the following data are obtained for a pixel : the orientation of the line and the subpixel location of the line

C. Coding Procedure To facilitate correspondence matching, the method proposed by Gühring, which combines gray code and line shifting The gray code is used to design the projected pattern such that the measurement area is divided into a large number of divisions, with each division having a unique identification code.

C. Coding Procedure The identification code possessed by the majority of subpixel points can be treated as that of all subpixel points at a stripe because the subpixel points at a stripe have been linked to a line.

EXPERIMENTAL RESULTS The relative error of the conicoid method is 0.26%, whereas the relative error of the plane method is 0.75%. Fig. 3(a) is an original image, whereas Fig. 3(b) is the detected result. In Fig. 3(b), some stripes are split into several segments because of discontinuities and shading.

EXPERIMENTAL RESULTS

Fig. 4(a) is an original gray code image, whereas Fig. 4(b) is the decoding result of the gray code images

DISCUSSION AND CONCLUSION The subpixel locations of the stripes are detected by the proposed method as it combines the zero- crossing algorithm with Steger’s curvilinear detector In some applications with complicated surface shapes, the extracted center may not be the actual center, as a result of image bias.