Dikpal Reddy

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We describe an imaging architecture for compressive video sensing termed programmable pixel compressive camera (P2C2). P2C2 allows us to capture fast phenomena at frame rates higher than the camera sensor. In P2C2, each pixel has an independent shutter that is modulated at a rate higher than the camera frame-rate. The observed intensity at a pixel is an(More)
Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reconstructed from a small set of random projections, provided that the signal is sparse in some basis, e.g., wavelets. In this paper, we describe a method to directly recover background(More)
We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera. We perform pose estimation by registering a morphable face model to the measured depth data, using a combination of particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm, which minimizes a cost function that includes a 3D(More)
We study the problem of adaptive compressive sensing (CS) of a time-varying signal with slowly changing sparsity and rapidly varying support. We are specifically interested in visual surveillance applications such as background subtraction and tracking. Classical CS theory assumes prior knowledge of signal sparsity in order to determine the number of sensor(More)
Surface reconstruction from gradient fields is an important final step in several applications involving gradient manipulations and estimations. Typically, the resulting gradient field is nonintegrable due to linear/non-linear gradient manipulations, or due to presence of noise/outliers in gradient estimation. In this paper, we analyze integrability as(More)
We demonstrate that layered spatial light modulators (SLMs), subject to fixed lateral displacements and refreshed at staggered intervals, can synthesize images with greater spatiotemporal resolution than that afforded by any single SLM used in their construction. Dubbed <i>cascaded displays</i>, such architectures enable superresolution flat panel displays(More)
We present a theory that addresses the problem of determining shape from the (small or differential) motion of an object with unknown isotropic reflectance, under arbitrary unknown distant illumination, for both orthographic and perpsective projection. Our theory imposes fundamental limits on the hardness of surface reconstruction, independent of the method(More)
Compressed sensing (CS) suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply the CS theory on sparse background-subtracted silhouettes and show the usefulness of such an approach in various multi-view estimation problems. The sparsity of the silhouette images corresponds to(More)
We show that, under spatially varying illumination, the light transport of diffuse scenes can be decomposed into direct, near-range (subsurface scattering and local inter-reflections) and far-range transports (diffuse inter-reflections). We show that these three component transports are redundant either in the spatial or the frequency domain and can be(More)
Surface reconstruction from gradient fields is an important final step in several applications involving gradient manipulations and estimation. Typically, the resulting gradient field is non-integrable due to linear/non-linear gradient manipulations, or due to presence of noise/outliers in gradient estimation. In this paper, we analyze integrability as(More)