Research Output per year
Intrinsic images represent the underlying properties of a scene such as illumination (shading) and surface reflectance. Extracting intrinsic images is a challenging, ill-posed problem. Human performance on tasks such as shadow detection and shape-from-shading is improved by adding colour and texture to surfaces. In particular, when a surface is painted with a textured pattern, correlations between local mean luminance and local luminance amplitude promote the interpretation of luminance variations as illumination changes. Based on this finding, we propose a novel feature, local luminance amplitude, to separate illumination and reflectance, and a framework to integrate this cue with hue and texture to extract intrinsic images. The algorithm uses steerable filters to separate images into frequency and orientation components and constructs shading and reflectance images from weighted combinations of these components. Weights are determined by correlations between corresponding variations in local luminance, local amplitude, colour and texture. The intrinsic images are further refined by ensuring the consistency of local texture elements. We test this method on surfaces photographed under different lighting conditions. The effectiveness of the algorithm is demonstrated by the correlation between our intrinsic images and ground truth shading and reflectance data. Luminance amplitude was found to be a useful cue. Results are also presented for natural images.
The Birmingham Object Lighting Database: a stereoscopic database of objects, faces, surfaces and outdoor scenes captured under characterised lightingSchofield, A., Cleaver, C. & Jiang, X., Sep 2010
Research output: Other contribution
Jiang, X., Schofield, A., & Wyatt, J. (2010). Correlation-Based Intrinsic Image Extraction from a Single Image. Lecture Notes in Computer Science, 6314, 58-71. https://doi.org/10.1007/978-3-642-15561-1_5