Research
Training Many-Parameter Shape-from-Shading Models Using a Surface Database
Shape-from-shading (SFS) methods tend to rely on models with few parameters because these parameters need to be hand-tuned. This limits the number of different cues that the SFS problem can exploit. In this paper, we show how machine learning can be applied to an SFS model with a large number of parameters. Our system learns a set of weighting parameters that use the intensity of each pixel in the image to gauge the importance of that pixel in the shape reconstruction process. We show empirically that this leads to a significant increase in the accuracy of the recovered surfaces. Our learning approach is novel in that the parameters are optimized with respect to actual surface output by the system. In the first, offline phase, a hemisphere is rendered using a known illumination direction. The isophotes in the resulting reflectance map are then modelled using Gaussian mixtures to obtain a parametric representation of the isophotes. This Gaussian parameterization is then used in the second phase to learn intensity-based weights using a database of 3D shapes. The weights can also be optimized for a particular input image.
N. Khan and M.F. Tappen, Training Many-Parameter Shape-from-Shading Models Using a Surface Database, 3DIM 2009 Workshop at ICCV 2009. [Paper] [Presentation]
3D Pose Estimation Using Implicit Algebraic Surfaces
2D-3D pose estimation deals with estimating the relative position and orientation of a known 3D
model from a 2D image of the model. Common explicit approaches to the problem involve registering
the 3D model points to image data in order to reveal the optimal pose parameters. In contrast, this
work presents an implicit approach by representing the 3D model and the image silhouette as zero-
sets of implicit polynomials and then minimising the distance between image outline pixels and
the zero-set of the silhouette equation to reveal the optimal pose parameters. This work deals
with representing 3D models as implicit polynomials, then computing sillhouette equations using
elimination theory and finally estimating pose parameters. (Work done under Bodo Rosenhahn at the Graphics department of Max-Planck Institute for Computer Science).
N. Khan, Implicit 2D-3D Pose Estimation, Masters Thesis, Universitaet des Saarlandes 2006. [PDF] [Thesis]
I am currently a Ph.D student at UCF under Marshall Tappen. Before coming to UCF, I was a research associate at the Computer Vision Lab at LUMS. I completed my Masters degree in Computer Science from Universitaet des Saarlandes in Saarbruecken, Germany and my Bachelors in Computer Science from LUMS.
