J.W. BRUCE, Peter STUBBERUD and Ashok IYER1 RANGE ESTIMATION AND OBJECT IDENTIFICATION WITH A SINGLE CAMERA MACHINE VISION SYSTEM

Typically machine vision systems estimate ranges using stereoscopic vision systems or specialized range detectors. Although these methods are effective, they are expensive. In this paper, a single camera range estimation algorithm is developed for a low-cost machine vision system that uses off-the-shelf components. This algorithm uses the perspective transformation and a priori knowledge of scene’s objects. The perspective or imaging transformation maps a point, (X, Y, Z), from the three-dimensional world coordinate system into an image point, (u, v, w), in a two-dimensional image plane. Assuming that the single camera’s optical axis lies along the w and Z axes as shown in Figure 1, the image plane lies at (u, v, 0). In the Cartesian coordinate system, the perspective transformation is nonlinear. However, the perspective transformation can be linearized by mapping Cartesian coordinates into homogeneous coordinates. A point, w, with Cartesian coordinates, (X, Y, Z), has the homogeneous coordinates