This paper describes an accurate method for computing the dimensions of boxes directly from perspective projection images acquired by conventional cameras. The approach is based on projective geometry and computes the box dimensions using data extracted from the box silhouette and from the projection of two parallel laser beams on one of the imaged faces of the box. In order to identify the box silhouette, we have developed a statistical model for homogeneous-background-color removal that works with a moving camera, and an efficient voting scheme for the Hough transform that allows the identification of almost collinear groups of pixels. We demonstrate the effectiveness of the proposed approach by automatically computing the dimensions of real boxes using a scanner prototype that implements the algorithms and methods described in the paper. We also present a discussion of the performed measurements, and an error propagation analysis that allows the method to estimate, from each single video frame, the uncertainty associated to all measurements made over that frame, in real-time.
Computing dimensions of boxes; image-based metrology; extraction of geometric information from scenes; uncertainty analysis; real time
