What Characterizes a Shadow Boundary under the Sun and Sky?
Xiang Huang, Gang Hua, Jack Tumblin and Lance Williams, ICCV2011
Paper (PDF)
Poster (PDF)

   Despite decades of study, robust shadow detection remains difficult, especially within a single color image. We describe a new approach to detect shadow boundaries in images of outdoor scenes lit only by the sun and sky. The method first extracts visual features of candidate edges that are motivated by physical models of illumination and occluders. We feed these features into a Support Vector Machine (SVM) that was trained to discriminate between most-likely shadow-edge candidates and less-likely ones. Finally, we connect edges to help reject non-shadow edge candidates, and to encourage closed, connected shadow boundaries. On benchmark shadow-edge data sets, our method showed substantial improvements when compared to other recent shadow-detection methods based on statistical learning.

Sensing Increased Image Resolution Using Aperture Masks
Ankit Mohan, Xiang Huang, Ramesh Raskar and Jack Tumblin, CVPR08 Paper (PDF)
Slides (PDF)
More details...
Discussions on Nuit Blanche

   We investigated a technique to construct increased-resolution images
from multiple photos taken without moving the camera or the sensor. Like other super-resolution techniques, we capture and merge multiple images, but instead of moving the camera sensor by sub-pixel distances for each image, we change masks in the lens aperture and slightly defocus the lens. The resulting capture system is simpler and tolerates modest mask registration errors well.

Deep Shadows in a Shallow Box
Xiang Huang, Ankit Mohan and Jack Tumblin
SPIE Electronic Imaging Conf. 2008
Paper (PDF)  Slides (PDF)

    We present a fast, low-cost technique to gather high-contrast `relightable' photographs of desktop-sized objects. By removing the ambient light computationally, we generate high-contrast deep shallow images from originally captured low-contrast shallow shadow images.

Multiple Description Lattice Vector Quantization (MDLVQ)
Xiang Huang and Xiaolin Wu
DCC'06 Paper (PDF)  Journal Draft (PDF)   Thesis (PDF)

   Multiple description coding is motivated by cooperate and distributed source coding for packet lossy networks. MDLVQ is an eective multiple description coding scheme. We proposed an optimal linear-time MDLVQ index assignment algorithm.