Issue No. 05 - May (2009 vol. 31)
Soma Biswas , University of Maryland, College Park
Gaurav Aggarwal , Univ. of Maryland, College Park
Rama Chellappa , University of Maryland, College Park
We present a nonstationary stochastic filtering framework for the task of albedo estimation from a single image. There are several approaches in the literature for albedo estimation, but few include the errors in estimates of surface normals and light source direction to improve the albedo estimate. The proposed approach effectively utilizes the error statistics of surface normals and illumination direction for robust estimation of albedo, for images illuminated by single and multiple light sources. The albedo estimate obtained is subsequently used to generate albedo-free normalized images for recovering the shape of an object. Traditional Shape-from-Shading (SFS) approaches often assume constant/piecewise constant albedo and known light source direction to recover the underlying shape. Using the estimated albedo, the general problem of estimating the shape of an object with varying albedo map and unknown illumination source is reduced to one that can be handled by traditional SFS approaches. Experimental results are provided to show the effectiveness of the approach and its application to illumination-invariant matching and shape recovery. The estimated albedo maps are compared with the ground truth. The maps are used as illumination-invariant signatures for the task of face recognition across illumination variations. The recognition results obtained compare well with the current state-of-the-art approaches. Impressive shape recovery results are obtained using images downloaded from the Web with little control over imaging conditions. The recovered shapes are also used to synthesize novel views under novel illumination conditions.
Albedo estimation, shape recovery, image estimation, illumination-invariant matching.
G. Aggarwal, R. Chellappa and S. Biswas, "Robust Estimation of Albedo for Illumination-Invariant Matching and Shape Recovery," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 884-899, 2008.