The surface bi-directional reflectance distribution function (BRDF) can be used to distinguish different materials. The BRDFs of many real materials are near isotropic and can be approximated well by a 2D function. When the camera principal axis is coincident with the surface normal of the material sample, the captured BRDF slice is nearly 1D, which suffers from significant information loss. Thus, improvement in classification performance can be achieved by simply setting the camera at a slanted view to capture a larger portion of the BRDF domain. We further use a handheld flashlight camera to capture a 1D BRDF slice for material classification. This 1D slice captures important reflectance properties such as specular reflection and retro-reflectance. We apply these results on ink classification, which can be used in forensics and analyzing historical manuscripts. For the first time, we show that most of the inks on the market can be well distinguished by their reflectance properties.

Creating uniform lighting for archival-quality document acquisition remains a non-trivial problem. We propose a novel method for automatic photometric correction of non-planar documents by estimating a single, point light-source using a simple light probe. By adding a simple piece of folded white paper with a known 3D surface to a scene, we are able to extract the 3D position of a light source, automatically perform white balance correction, and determine areas of poor illumination. Furthermore, this method is designed with the purpose of adding it to an already implemented document digitization pipeline. To justify our claims, we provide an accuracy analysis of our correction technique using simulated ground-truth data which allows individual sources of error to be determined and compared. These techniques are then applied on real documents that have been acquired using a 3D scanner.

We present a system to restore the 2D content printed on distorted documents. Our system works by acquiring a 3D scan of the document's surface together with a high-resolution image. Using the 3D surface information and the 2D image, we can ameliorate unwanted surface distortion and effects from non-uniform illumination. Our system can process arbitrary geometric distortions, not requiring any pre-assumed parametric models for the document's geometry. The illumination correction uses the 3D shape to distinguish content edges from illumination edges to recover the 2D content's reflectance image while making no assumptions about light sources and their positions. Results are shown for real objects, demonstrating a complete framework capable of restoring geometric and photometric artifacts on distorted documents