When: Monday, March 24, 3:30 p.m.

Where: 1507Newell-Simon Hall

Sameer Agarwal,
University of Washington

VASC Seminar

Abstract:
As we make progress in measuring and modeling reflectance, it is also important that we develop a better understanding of how the human visual system perceives the reflection of light. Such a development not only has implications for efficient image synthesis, but also for computer vision where an understanding of reflectance perception will give us insight into the priors and constraints used by humans to solve various shading related problems, e.g., shape from shading and object recognition over variable and unknown lighting.

In this talk I will present a study of the perception of reflectance. I will argue that our methodology based on paired comparisons is better suited for capturing human perception and is less susceptible to experimental errors than previously used methods. The analysis of paired comparisons required the development of a new data analysis tool. In the second part of the talk I will present a new multidimensional scaling algorithm for analyzing paired comparisons. Based on semi-definite programming, this algorithm is a more general and efficient replacement for the widely used Non-metric MDS algorithm.

Using this algorithm we obtain a perceptual embedding of BRDFs from the MIT/MERL Database. This embedding, constructed purely from psychophysical data, exhibits some striking correlations with the material appearance standards that have been developed independently in the paper and paint industries. Finally, I will describe a novel perceptual interpolation scheme that uses this embedding to provide the user with an intuitive interface for navigating the space of reflectances and constructing new ones.

Sameer Agarwal received the M.S. degree in Mathematics and Scientific Computation from the Indian Institute of Technology, Kanpur in 2000 and a PhD in Computer Science from the University of California, San Diego in 2006. He is currently a postdoc in the Computer Science and Engineering Department at University of Washington, Seattle. His research interests include computer vision, machine learning and optimization, though not necessarily in that order.

Appointments: jturken@andrew.cmu.edu

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