Line-Based Modified Iterated Hough Transform for Autonomous Single-Photo Resection

<p><i>Automatic single photo resection (SPR) remains to be one of the challenging problems in digital photogrammetry. Earlier attempts to automate the space resection task were mainly point-based, where image-point primitives are first extracted and matched with their object counterparts. The matched primitives are then used to estimate the exterior orientation parameters (EOP). However, visibility and uniqueness of distinct control points in the input imagery limit robust automation of the pose estimation procedure. Recent advances in digital photogrammetry mandate adopting higher-level primitives such as control linear features replacing traditional control points. Linear features can be automatically extracted in image space. On the other hand, object-space control linear features can be extracted from an existing GIS layer containing</i> 3D <i>vector data such as a road network and/or terrestrial mobile mapping systems (MMS). In this paper, we present a line-based approach for simultaneously determining the position and attitude of the involved imagery as well as establishing the correspondence between image- and object-space features. This approach is motivated by the fact that captured imagery over a man-made environment is rich in straight-line segments. Moreover, free-form linear features can be reliably represented with sufficient accuracy by a sequence of straight-line segments (i.e., polylines). The suggested methodology starts by establishing a general mathematical model for relating conjugate straight-line segments to the EOP of the image under consideration. Then, a Modified Iterated Hough Transform (MIHT) strategy is adopted to derive the correspondence between image and object primitives as well as the position and the attitude of the involved imagery. This approach does not necessitate having one-to-one correspondence between image- and object-space primitives, which makes it robust against changes and/or discrepancies between the primitives. The parameter estimation and matching processes follow an optimum sequential procedure, which depends on the sensitivity of the mathematical model, relating corresponding primitives with different orientation at various image regions, to incremental changes in the EOP. Experimental results using real data proved the feasibility and robustness of the proposed approach even in the presence of a large percentage of outliers and/or discrepancies between the image-and object-space features.</i></p>