Vision system measures dental morphology

SEPTEMBER 20--Dr. Pertti Pirttiniemi and his colleagues in the department of dentistry and professors Janne Heikkil�nd Olli Silv�in the department of electrical engineering at the University of Oulu (Oulu, Finland) have developed a vision-based system using an off-the-shelf video camera, a frame grabber, a PC, and special measurement software that automates measurement of the distances and angles between morphologically interesting points on dental crowns.

Sep 20th, 2001

SEPTEMBER 20--Dr. Pertti Pirttiniemi and his colleagues in the department of dentistry and professors Janne Heikkil�nd Olli Silv�in the department of electrical engineering at the University of Oulu (Oulu, Finland) have developed a vision-based system using an off-the-shelf video camera, a frame grabber, a PC, and special measurement software that automates measurement of the distances and angles between morphologically interesting points on dental crowns. Based on a 400-MHz Pentium III CPU running Windows 95/NT 4.0, the vision system uses a SSC-C370P color CCD camera from Sony Electronics (Park Ridge, NJ) with a 12-mm lens mounted on a stand above the plaster model. The stand holds the camera in a fixed position and allows a plaster model to be turned in five different orientations.

To digitize the images, the camera is interfaced to a DT3153 frame grabber from Data Translation (Marlboro, MA). The resulting images are displayed at 1024 x 768-pixel resolution on a Sony Multisync monitor.

A crucial problem that confronted the system-design team involved camera calibration. �Modern CCD cameras are usually capable of a spatial accuracy greater than 1/50 of the pixel size. However, such accuracy is not easily attained due to various error sources that can affect the image formation process,� Heikkil�ays. Whereas current calibration methods typically assume that the observations are unbiased and the camera model maps 3-D and image coordinates, this was not the case in this system design. Camera calibration turned out to be less accurate than expected. To overcome this drawback, Heikkil�eveloped a camera-calibration toolbox for MatLab that uses a bias-correction procedure for circular control points and a nonrecursive method for reversing the distortion model.

"Using this calibration method," says Dr. Pirttiniemi, �the accuracy of the resulting point locations in each dimension is better than 0.1 mm and can reach 0.01-mm accuracy; therefore, the 3-D structures, distances, and angles can be computed automatically.�

More information is available in the September issue of Vision Systems Design.

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