Biomedical subjects

R Chellappa

Publications and source records attributed to R Chellappa.

6 recordsLinked to original sources

PubMed2001

The membrane proteins, Spt23p and Mga2p, play distinct roles in the activation of Saccharomyces cerevisiae OLE1 gene expression. Fatty acid-mediated regulation of Mga2p activity is independent of its proteolytic processing into a soluble transcription activator.

The Saccharomyces OLE1 gene encodes the Delta-9 fatty acid desaturase, an enzyme that converts saturated fatty acyl-CoAs into cis-Delta-9 unsaturated fatty acids. OLE1 gene expression is regulated by unsaturated fatty acids, which repress transcription and destabilize the OLE1 mRNA. Expression of OLE1 is activated by N-terminal proteolytic fragments of two homologous endoplasmic reticulum membrane proteins, Spt23p and Mga2p. Disruption of either gene does not significantly affect cell growth or fatty acid metabolism; cells that contain null alleles of both genes, however, are unsaturated fatty acid auxotrophs. An analysis of spt23Delta and mga2Delta strains shows that Spt23p and Mga2p differentially activate and regulate OLE1 transcription. In glucose-grown cells, both genes activate transcription to similar levels of activity. Expressed alone, Mga2p induces high levels of OLE1 transcription in cells exposed to cobalt or grown in glycerol-containing medium. Spt23p expressed alone activates OLE1 transcription to levels similar to those in wild type cells. OLE1 expression is strongly repressed by unsaturated fatty acids in spt23Delta or mga2Delta cells, under all growth conditions. To test if OLE1 expression is controlled by fatty acids at the level of membrane proteolysis, soluble N-terminal fragments of Spt23p and Mga2p that lack their membrane-spanning regions (Deltatm) were expressed under the control of their native promoters in spt23Delta;mga2Delta cells. Under those conditions, Mga2pDeltatm acts as a powerful transcription activator that is strongly repressed by unsaturated fatty acids. By comparison, the Spt23pDeltatm polypeptide weakly activates transcription and shows little regulation by unsaturated fatty acids. Co-expression of the two soluble fragments results in activation to levels observed with the Mga2pDeltatm protein alone. The fatty acid repression of transcription under those conditions is attenuated by Spt23Deltatm, however, suggesting that the two proteins may interact to modulate OLE1 gene expression.

Base Sequence↗

PubMed2001

Face verification through tracking facial features.

We propose an algorithm for face verification through tracking facial features by using sequential importance sampling. Specifically, we first formulate tracking as a Bayesian inference problem and propose to use Markov chain Monte Carlo techniques for obtaining an empirical solution. A reparameterization is introduced under parametric motion assumption, which facilitates the empirical estimation and also allows verification to be addressed along with tracking. The facial features to be tracked are defined on a grid with Gabor attributes (jets). The motion of facial feature points is modeled as a global two-dimensional (2-D) affine transformation (accounting for head motion) plus a local deformation (accounting for residual motion that is due to inaccuracies in 2-D affine modeling and other factors such as facial expression). Motion of both types is processed simultaneously by the tracker: The global motion is estimated by importance sampling, and the residual motion is handled by incorporating local deformation into the measurement likelihood in computing the weight of a sample. Experiments with a real database of face image sequences are presented.

Journal Article↗

PubMed2001

Robust structure from motion estimation using inertial data.

The utility of using inertial data for the structure-from-motion (SfM) problem is addressed. We show how inertial data can be used for improved noise resistance, reduction of inherent ambiguities, and handling of mixed-domain sequences. We also show that the number of feature points needed for accurate and robust SfM estimation can be significantly reduced when inertial data are employed. Cramér-Rao lower bounds are computed to quantify the improvements in estimating motion parameters. A robust extended-Kalman-filter-based SfM algorithm using inertial data is then developed to fully exploit the inertial information. This algorithm has been tested by using synthetic and real image sequences, and the results show the efficacy of using inertial data for the SfM problem.

Journal Article↗

PubMed2001

Higher-order-statistics-based detection of vehicles in still images.

We present a statistical pattern recognition scheme for detecting vehicles in still images. The methodology involves pattern classification using higher-order statistics (HOS) in a clustering framework. The proposed method approximately models the unknown distribution of the image patterns of vehicles by learning HOS information about the vehicle class from sample images. Given a test image, statistical information about the background is learned "on the fly." An HOS-based decision measure derived from a series expansion of the multivariate probability density function in terms of the Gaussian function and Hermite polynomials is used to classify test patterns as vehicles or otherwise. Experimental results on real images with cluttered background are given to demonstrate the performance of the proposed method. When tested on real aerial images, the method gives good results, even for complicated scenes. The detection rate is found to be quite good, while the false alarms are very few. The method can serve as an important step toward building an automated traffic monitoring system.

Journal Article↗

PubMed2000

Quantitative analysis of error bounds in the recovery of depth from defocused images.

Depth from defocus involves estimating the relative blur between a pair of defocused images of a scene captured with different lens settings. When a priori information about the scene is available, it is possible to estimate the depth even from a single image. However, experimental studies indicate that the depth estimate improves with multiple observations. We provide a mathematical underpinning to this evidence by deriving and comparing the theoretical bounds for the error in the estimate of blur corresponding to the case of a single image and for a pair of defocused images. A new theorem is proposed that proves that the Cramér-Rao bound on the variance of the error in the estimate of blur decreases with an increase in the number of observations. The difference in the bounds turns out to be a function of the relative blurring between the observations. Hence one can indeed get better estimates of depth from multiple defocused images compared with those using only a single image, provided that these images are differently blurred. Results on synthetic as well as real data are given to further validate the claim.

Journal Article↗

PubMed1999

Noise-resilient estimation of optical flow by use of overlapped basis functions.

Conventional techniques for the computation of optical flow from image gradients are used to formulate the problem as a nonlinear optimization that comprises a gradient constraint term and a field smoothness factor. The results of these techniques are often erroneous, highly sensitive to noise and numerical precision, determined sparsely, and computationally expensive. We regularize the gradient constraint equation by modeling optical flow as a linear combination of a set of overlapped basis functions. We develop a theory for estimating model parameters robustly and reliably. We prove that the extended-least-squares solution proposed here is unbiased and robust to small perturbations in the gradient estimates and to mild deviations from the gradient constraint. Our solution is obtained with a numerically stable sparse matrix inversion, which gives a reliable flow-field estimate over the entire frame. To validate our claims, we perform a series of experiments on standard benchmark data sets at a range of noise levels. Overall, our algorithm outperforms by a wide margin the others considered in the comparison. We demonstrate the applicability of our algorithm to image mosaicking and to motion superresolution through experiments on noisy compressed sequences. We conclude that our flow-field model offers greater accuracy and robustness than conventional optical flow techniques in a variety of situations and permits real-time operation.

Algorithms↗