A new image processing method is described for measuring the 3-D coordinates of a complex, biological surface. One of the problems in stereo vision is known as the accuracy-precision tradeoff problem. This thesis proposes a new method that promises to solve this problem. To do so, two issues are addressed. First, stereo vision instrumentation methods are described. This instrumentation includes a camera system as well as camera calibration, rectification, matching and triangulation. Second, the approach employs an array of cameras that allow accurate computation of the depth map of a surface by propagation of correspondences through pair-wise camera views.

The new method proposed in this thesis employs an array of cameras, and preserves the small baseline advantage by finding accurate correspondences in pairs of adjacent cameras. These correspondences are then propagated along the consecutive pairs of cameras in the array until a large baseline is accomplished. The resulting large baseline disparities are then used for triangulation to achieve advantage of precision in depth measurement.

The matching is done by an area-based intensity correlation function called Sum of Squared Differences (SSD). In this thesis, the feasibility of using these data for further processing to achieve surface or volume measurements in the future is discussed.