Dr. V. Grayson CuQlock-Knopp Profile

Dr. V. Grayson CuQlock-Knopp

Research Psychlolgist at Army Research Lab

SPIE Involvement:

Author

Publications (10)

Proceedings Article | 1 May 2007 Paper

Hyperstereo algorithms for the perception of terrain drop-offs

Arunkumar Mohananchettiar, Volkan Cevher, Grayson CuQlock-Knopp, Rama Chellappa, John Merritt

Proceedings Volume 6557, 65570L (2007) https://doi.org/10.1117/12.721066

KEYWORDS: Cameras, Machine vision, Detection and tracking algorithms, Sensors, Video, Direct methods, Navigation systems, Imaging systems, Optimal filtering, Image fusion

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Proceedings Article | 18 May 2006 Paper

Binocular depth acuity research to support the modular multi-spectral stereoscopic night vision goggle

John Merritt, V. Grayson CuQlock-Knopp, Peter Paicopolis, Jennifer Smoot, Mark Kregel, Bernard Corona

Proceedings Volume 6224, 622403 (2006) https://doi.org/10.1117/12.667315

KEYWORDS: Eye, Video, Cameras, Thermography, Sensors, Image fusion, Goggles, Biomedical optics, Image resolution, Night vision goggles

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Proceedings Article | 19 May 2005 Paper

Perception of terrain drop-offs as a function of L-R viewpoint separation in stereoscopic video

John Merritt, V. Grayson CuQlock-Knopp, Mark Kregel, Jennifer Smoot, William Monaco

Proceedings Volume 5800, (2005) https://doi.org/10.1117/12.608151

KEYWORDS: Video, Cameras, 3D displays, Eye, Visualization, Sensors, Digital video recorders, Computing systems, Projection systems, Analytical research

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Proceedings Article | 3 November 2000 Paper

Object recognition and contrast sensitivity with image intensifiers employing white phosphor versus green phosphor displays

V. Grayson CuQlock-Knopp, John Merritt, Edward Bender, LaShawna Wright-Hector

Proceedings Volume 4128, (2000) https://doi.org/10.1117/12.405879

KEYWORDS: Object recognition, Contrast sensitivity, Image intensifiers, Goggles, Sensors, Night vision goggles, Visualization, Visual process modeling, Image retrieval, Image processing

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Proceedings Article | 24 July 2000 Paper

Sensor fusion: a preattentive vision approach

Wendell Watkins, V. Grayson CuQlock-Knopp, Jay Jordan, Anthony Marinos, Michael Phillips, John Merritt

Proceedings Volume 4029, (2000) https://doi.org/10.1117/12.392556

KEYWORDS: Image fusion, Sensors, RGB color model, Sensor fusion, Image sensors, Visible radiation, Infrared radiation, Human vision and color perception, Image processing, Infrared imaging

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Because different imaging sensors provide different signature cues to distinguish targets from backgrounds there has been a substantial amount of effort put into how to merge the information from different sensors. Unfortunately, when the imagery from two different sensors is combined the noise from each sensor is also combined in the resultant image. Additionally, attempts to enhance the target distinctness from the background also enhance the distinctness of false targets and clutter. Even so there has been some progress in trying to mimic the human vision capability by color contrast enhancement. But what has not been tried is how to mimic how the human vision system inherently does this sensor function of our color cone sensors. We do our sensor fusion in the pre- attentive phase of human vision. This requires the use of binocular stereo vision because we do have two eyes. In human vision the images from each eye are split in half, and the halves are sent to opposite sides of the brain for massively parallel processing. We don't know exactly how this process works, but the results is a visualization of the world that is 3D in nature. This process automatically combines the color, texture, and size and shape of the objects that make up the two images that our eyes produce. It significantly reduces noise and clutter in our visualization of the world. In this pre-attentive phase of human vision tha takes just an instant to accomplish, our human vision process has performed an extremely efficient fusion of cone imagery. This sensor fusion process has produced a scene where depth perception and surface contour cues are used to orient and distinguish objects in the scene before us. It is at this stage that we begin to attentively sort through the scene for objects or targets of interest. In many cases, however, the targets of interest have already been located because of their depth or surface contour cues. Camouflaged targets that blend perfectly into complex backgrounds may be made to pop out because of their depth cues. In this paper we will describe a new method termed RGB stereo sensor fusion that uses color coding of the separate pairs of sensor images fused to produce wide baseline stereo images that are displayed to observers for search and target acquisition. Performance enhancements for the technique are given as well as rationale for optimum color code selection. One important finding was that different colors (RGB) and different spatial frequencies are fused with different efficiencies by the binocular vision system.

Proceedings Article | 23 June 2000 Paper

Navigation through fog using stereoscopic active imaging

Wendell Watkins, David Tofsted, V. Grayson CuQlock-Knopp, Jay Jordan, John Merritt

Proceedings Volume 4023, (2000) https://doi.org/10.1117/12.389357

KEYWORDS: Fiber optic gyroscopes, Cameras, Retroreflectors, Backscatter, Imaging systems, Visibility through fog, Visibility, Image fusion, Scattering, Patents

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Proceedings Article | 19 July 1999 Paper

Active imaging applied to navigation through fog

Wendell Watkins, David Tofsted, V. Grayson CuQlock-Knopp, Jay Jordan, Mohan Trivedi

Proceedings Volume 3749, (1999) https://doi.org/10.1117/12.354991

KEYWORDS: Fiber optic gyroscopes, Backscatter, Image fusion, Analytical research, Reflectors, Visibility through fog, Visibility, Radar, Laser scattering, Night vision

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Navigation, especially in aviation, has been plagued since its inception with the hazards ofpoor visibility conditions. Vehicular ground movement is also hampered at night or in low visibility even with night vision augmentation because of the lack of contrast and depth perception. For landing aircraft in fog, the visible and near-infrared have been discounted because of the large backscatter coefficients in favor of primarily radar that penetrates waterladen atmospheres. Aircraft outfitted with an Instrumentation Landing System (ILS) can land safely on an aircraft carrier in fog. Landing at an airport with an ILS is not safe because there is no way to detect small-scale obstacles that do not show up on radar but can cause a landing crash. We have developed and tested a technique to improve navigation through fog based on chopped active visible laser illumination and wide baseline stereo (hyperstereo) viewing with real-time image correction of backscatter radiation and forward scattering blur. The basis of the approach to developing this active hyperstereo vision system for landing aircraft in fog is outlined in the invention disclosure ofthe Army Research Laboratory (ARL) patent application ARL-97-72, filed Dec. 1997. Testing this concept required a matched pair of laser illuminators and cameras with synchronized choppers, a computer for near real-time acquisition and analysis of the hyperstereo imagery with ancillary stereo display goggles, a set of specular reflectors, and a fog generator/characterizer. The basic concept of active hyperstereo vision is to compare the imagery obtained from alternate wings ofthe aircraft while illuminating only from the opposite wing. This produces images with a backscatter radiation pattern that has an increasing gradient towards the side with the illumination source. Flipping the imagery from one wing left to right and comparing it to the opposite wing imagery will allow the backscattered radiation pattern to be subtracted from both sets of imagery. Use of specular reflectors along the sides of the runway will allow the human stereo fusion process to fuse the forward scatter blurred hyperstereo imagery of the array of specular reflectors with backscatter eliminated and allow the appropriate amount of inverse point spread function deblurring to be applied for optimum resolution of scene content (i.e., obstacles on the runway). Results of this testing will be shown.