The Community for Technology Leaders
Green Image
<p><b>Abstract</b>—This paper makes two contributions to the problem of needle-map recovery using shape-from-shading. First, we provide a geometric update procedure which allows the image irradiance equation to be satisfied as a hard constraint. This not only improves the data closeness of the recovered needle-map, but also removes the necessity for extensive parameter tuning. Second, we exploit the improved ease of control of the new shape-from-shading process to investigate various types of needle-map consistency constraint. The first set of constraints are based on needle-map smoothness. The second avenue of investigation is to use curvature information to impose topographic constraints. Third, we explore ways in which the needle-map is recovered so as to be consistent with the image gradient field. In each case we explore a variety of robust error measures and consistency weighting schemes that can be used to impose the desired constraints on the recovered needle-map. We provide an experimental assessment of the new shape-from-shading framework on both real world images and synthetic images with known ground truth surface normals. The main conclusion drawn from our analysis is that the data-closeness constraint improves the efficiency of shape-from-shading and that both the topographic and gradient consistency constraints improve the fidelity of the recovered needle-map. </p>
Index Terms–Shape-from-shading, hard constraints, curvature consistency, gradient consistency, robust statistics.
Edwin R. Hancock, Philip L. Worthington, "New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 1250-1267, December 1999, doi:10.1109/34.817406
89 ms
(Ver 3.3 (11022016))