Improving the Performance of Single Chip Image Capture Devices

Single chip charge-coupled devices (CCDs) coupled with filters for isolating red, green, and blue color content are commonly used to capture color images. While this is more cost effective than multiple chip systems, best results are obtained when full RGB color information is obtained for every point in an image. The process of color subsampling in a single chip system degrades the resulting image data by introducing artifacts such as blurry edges and false coloring. We propose an algorithm for enhancing color image data that were captured with a typical single chip CCD array. The algorithm is based on stochastic regularization using a Markov random field model for the image data. This results in a constrained optimization problem, which is solved using an iterative constrained gradient descent computational algorithm. Results of the proposed algorithm show a marked improvement over the original sampled image data

Invariant Reconstruction of Curves and Surfaces with Discontinuities with Applications in Computer Vision

The reconstruction of curves and surfaces from sparse data is an important task in many applications. In computer vision problems the reconstructed curves and surfaces generally represent some physical property of a real object in a scene. For instance, the sparse data that is collected may represent locations along the boundary between an object and a background. It may be desirable to reconstruct the complete boundary from this sparse data. Since the curves and surfaces represent physical properties, the characteristics of the reconstruction process differs from straight forward fitting of smooth curves and surfaces to a set of data in two important areas. First, since the collected data is represented in an arbitrarily chosen coordinate system, the reconstruction process should be invariant to the choice of the coordinate system (except for the transformation between the two coordinate systems). Secondly, in many reconstruction applications the curve or surface that is being represented may be discontinuous. For example in the object recognition problem if the object is a box there is a discontinuity in the boundary curve at the comer of the box. The reconstruction problem will be cast as an ill-posed inverse problem which must be stabilized using a priori information relative to the constraint formation. Tikhonov regularization is used to form a well posed mathematical problem statement and conditions for an invariant reconstruction are given. In the case where coordinate system invariance is incorporated into the problem, the resulting functional minimization problems are shown to be nonconvex. To form a valid convex approximation to the invariant functional minimization problem a two step algorithm is proposed. The first step forms an approximation to the curve (surface) which is piecewise linear (planar). This approximation is used to estimate curve (surface) characteristics which are then used to form an approximation of the nonconvex functional with a convex functional. Several example applications in computer vision for which the invariant property is important are presented to demonstrate the effectiveness of the algorithms. To incorporate the fact that the curves and surfaces may have discontinuities the minimizing functional is modified. An important property of the resulting functional minimization problems is that convexity is maintained. Therefore, the computational complexity of the resulting algorithms are not significantly increased. Examples are provided to demonstrate the characteristics of the algorithm

Improved image decompression for reduced transform coding artifacts

The perceived quality of images reconstructed from low bit rate compression is severely degraded by the appearance of transform coding artifacts. This paper proposes a method for producing higher quality reconstructed images based on a stochastic model for the image data. Quantization (scalar or vector) partitions the transform coefficient space and maps all points in a partition cell to a representative reconstruction point, usually taken as the centroid of the cell. The proposed image estimation technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image which best fits a non-Gaussian Markov random field (MRF) image model. This approach results in a convex constrained optimization problem which can be solved iteratively. At each iteration, the gradient projection method is used to update the estimate based on the image model. In the transform domain, the resulting coefficient reconstruction points are projected to the particular quantization partition cells defined by the compressed image. Experimental results will be shown for images compressed using scalar quantization of block DCT and using vector quantization of subband wavelet transform. The proposed image decompression provides a reconstructed image with reduced visibility of transform coding artifacts and superior perceived quality

Probing the helical content of growth hormone-releasing factor analogs using electrospray ionization mass spectrometry

AbstractA series of growth hormone-releasing factor analogs have been studied by both circular dichroism and electrospray ionization mass spectrometry (ESI/MS). The peptides are 32 residues long and are known to adopt a random-coil structure in aqueous solution but become increasing helical as the proportion of organic solvent is increased. Deuterium exchange was observed as an increase in mass of the peptide, as measured by ESI/MS. Rates of exchange were measured and half-lives calculated for analogs containing amino acid substitutions designed to promote or discourage helix formation. Exchange was slower in peptides that are helical (as shown by circular dichroism) than in randomly coiled peptides. Solution conditions that favor helix formation also produced slower exchange rates. These studies suggest that ESI/MS can provide date about the extent and stability of helix formation

Systems for multivariate monitoring of behavioral status over time

Decision-theoretic criteria are presented for optimizing the information gathered from a series of interviews over time. It is shown that the optimum interviewing strategy depends strongly on assumptions about the covariation of behavior over time. Standard interviewing strategies, including the major-problem/target-complaints approach, are optimal only under extreme assumptions about behavior. An interviewing strategy based on dynamic programming is presented that will provide optimal information return from a series of interviews under assumptions that are realistic for mental health applications. A system using this approach can tailor its interviewing strategy to adapt to differences in interview content, item importance, and individual response patterns, selecting the optimally informative questions to ask each subject at each point in time. Simulation results show that this approach achieves a 34% reduction in the false negatives obtained with the major-problem/target-complaints method, and, depending on the acceptable error rate, a reduction of 47 % or more in the questions that are needed in standard interviewing