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In this paper we present a technique that may be applied to surveillance video data to obtain a higher-quality image from a sequence of lower-quality images. The increase in quality is derived through a deconvolution of optical blur and/or an increase in spatial sampling. To process sequences of real forensic video data, three main steps are required: frame and region selection, displacement estimation, and original image estimation. A user-identified region-of-interest (ROT) is compared to other frames in the sequence. The areas that are suitable matches are identified and used for displacement estimation. The calculated displacement vector images describe the transformation .f the desired high-quality image to the observed low quality images. The final stage is based on the Projection Onto Convex Sets (POCS) super-resolution approach of Patti, Sezan, and This stage performs a deconvolution using the observed image sequence, displacement vectors, and anapriori known blur model. A description ofthe algorithmic steps are provided, and an example input sequence with corresponding output image is given.

instrument. 4 Roadside Tracker results for a moving vehicle with two sources. 7 5 Graphical representation of the process of single frame vehicle image capture. 8 6 Block diagram of the overall data acquisition system. 7 Block diagram of s single channel of gamma-ray data acquisition. 8 Schematic diagram of gamma-ray buffer data structure. 9 Illustration of vehicle slices. 10 Progress of video hardware during the project. 11 Bayer image format. 12 Block diagram of video processing data flow. 13 Targets on the trailer roof. 14 Stereo point search approach. 15 Standard and high resolution video images. 16 Project timeline. 17 Major events during design phase. 18 Major events during POC construction phase. 19 Major events during phase 4. 20 RST standard 2-lane development site. 21 RST development site on main ORNL thoroughfare. 22 First offsite data collection site. 23 Major events during improvement, test and evaluation phase. 24 Major events during the limited use exercise. 25 Site at SRNL where high-speed tests were conducted. 26 RST performance vs velocity. 27 RST deployed near Belmont raceway. 28 Improved gamma performance after removal of video frame offset.

The high-speed production of textiles with complicated printed patterns presents a difficult problem for a colormethc measurement system. Accurate assessment of product quality requires a repeatable measurement using a standard color space, such as CIELAB, and the use of a perceptually based color difference formula, e.g.color difference formula.Image based color sensors used for on-line measurement are not colormetric by nature and require a non-linear transformation of the component colors based on the spectral properties of the incident illumination, imaging sensor, and the actual textile color. This research and development effort describes a benchtop, proof-of-principle system that implements a projection onto convex sets (POCS) algorithm for mapping component color measurements to standard thstimulus values and incorporates structural and color based segmentation for improved precision and accuracy. The POCS algorithm consists of determining the closed convex sets that describe the constraints on the reconstruction of the true tristimulus values based on the measured imperfect values. We show that using a simulated D65 standard illuminant, commercial filters and a CCD camera, accurate (under perceptibility limits) per-region based values can be measured on real textile samples.

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