1. 3-D Computer Vision Using Structured Light Prepared by Burak Borhan

2. Machine Vision For many applications three dimensional (3-D) descriptors of the scene are required. Passive Approaches Using two or more cameras in binocular and photometric stereo Active Approaches Use specialized illumination sources and detectors Overcome the fundamental ambiguities associated with passive approaches. STRUCTURED LIGHT

3. Structured Light System Structured Light systems use triangulation to acquire range measurements. In the upper system the ranging geometry is formed by a laser emission, the reflected light observed by the camera and by a rigid backbone. Optical measurements capture range data in a plane. A conveyer produces the necessary motion for 3-D range data.

4. Three Main Challenges in Developing a S.L. System CalibrationAccuracy Acquisition Speed

5. Calibration Calibration models are needed to relate image to world coordinates. Challenges in calibration also arise from the complexity of models that are required for sensor kinematics, which describe the geometrical relationship between the camera and the laser plane.

6. Ranging Accuracy Ranging accuracy is highly dependent on calibration models. However even the most careful calibration effort can be fruitless if the ranging geometry is unfavorable.

7. Acquisition Speed Range Acquisition involves locating the laser profile within camera images This requires pixel examination and processing in order to precisely locate image coordinates that reside at the center of the laser profile.

8. Analysis of an Existing S.L. System The camera is outfitted with an optical filter that is matched to the laser optical frequency. These matched optics produce very distinct imagery of the laser profile.

9. Block Diagram of Processing Steps

10. Image Processing Operations (1) The rough position estimates are achieved by thresholding and then run length coding. The RLC array provides a brief description of the rough location of the laser line.

11. Image Processing Operations (2) The upper figure illustrates typical pixel intensities, as sampled along a path roughly orthogonal to the laser line. In this system the pixel intensity profiles are 5 - 7 pixels wide with roughly Gaussian shape. The main processor examines gray scale pixel values in a 20x1 window, W, centered at locations given by the RLC array.

12. Image Processing Operations (3) The mean and the variance of the row at the center of the laser line are found with, The points above the threshold are used to calculate the mean and variance.

13. Calibration Process (X) z = f(r,c) calibration. Generate examples of |z i r i c i | matrix.

14. Calibration Process (Y) y = f(r,c) calibration. Generate examples of |y i r i c i | matrix.

15. Image Processing Operations (4) A calibration model is used to convert the image coordinates (r,c) to world coordinates (y,z). And the X components is found by monitoring the motion of the conveyor belt using a wheel encoder.

16. Calibration Process

17. 3- models

18. 3-D Image Reconstruction Geometric model of a polyhedral object is represented by

19. QUESTIONS ? QUESTIONS ?