Mr. Charles M. Ciany Profile

Charles M. Ciany

Engineering Fellow at RTX Corp

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Author

Publications (10)

Proceedings Article | 29 April 2010 Paper

Enhanced ATR using Fisher fusion techniques with application to side-looking sonar

Charles Ciany, William Zurawski

Proceedings Volume 7664, 76640Z (2010) https://doi.org/10.1117/12.852401

KEYWORDS: Automatic target recognition, Detection and tracking algorithms, Image processing, Target detection, Image fusion, Image segmentation, Image processing algorithms and systems, Data fusion, Image classification, Algorithm development

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Proceedings Article | 5 May 2009 Paper

Application of Fisher fusion techniques to improve the individual performance of sonar computer-aided detection/computer-aided classification (CAD/CAC) algorithms

Charles Ciany, William Zurawski

Proceedings Volume 7303, 73030F (2009) https://doi.org/10.1117/12.819419

KEYWORDS: Detection and tracking algorithms, Image processing, Target detection, Image fusion, Image classification, Image processing algorithms and systems, Image segmentation, Feature extraction, Data fusion, Receivers

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Proceedings Article | 7 May 2007 Paper

Impact of image normalization and quantization on the performance of sonar computer-aided detection/computer-aided classification (CAD/CAC) algorithms

Charles Ciany, William Zurawski

Proceedings Volume 6553, 65530T (2007) https://doi.org/10.1117/12.718764

KEYWORDS: Image processing, Quantization, Detection and tracking algorithms, Image segmentation, Image classification, Signal processing, Statistical analysis, Computer aided diagnosis and therapy, Data processing, Image fusion

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Proceedings Article | 18 May 2006 Paper

Application of fusion algorithms for computer aided detection and classification of bottom mines to synthetic aperture sonar test data

Charles Ciany, William Zurawski

Proceedings Volume 6217, 62171D (2006) https://doi.org/10.1117/12.666100

KEYWORDS: Image processing, Detection and tracking algorithms, Image fusion, Data fusion, Image classification, Image segmentation, Computer aided diagnosis and therapy, Land mines, Classification systems, Image processing algorithms and systems

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Proceedings Article | 10 June 2005 Paper

Further improvements in computer aided detection/computer aided classification (CAD/CAC) of bottom mines

Charles Ciany, William Zurawski

Proceedings Volume 5794, (2005) https://doi.org/10.1117/12.603937

KEYWORDS: Detection and tracking algorithms, Data fusion, Image fusion, Image classification, Image processing, Image segmentation, Environmental sensing, Image processing algorithms and systems, Computer aided diagnosis and therapy, Data processing

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Proceedings Article | 21 September 2004 Paper

Improvements in computer-aided detection/computer-aided classification (CAD/CAC) of bottom mines through post analysis of a diverse set of very shallow water (VSW) environmental test data

Charles Ciany, William Zurawski

Proceedings Volume 5415, (2004) https://doi.org/10.1117/12.542525

KEYWORDS: Detection and tracking algorithms, Image processing, Image fusion, Image classification, Data fusion, Image processing algorithms and systems, Image segmentation, Mining, Computer aided diagnosis and therapy, Data processing

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In 1999 Raytheon adapted its shallow-water Side-Looking Sonar (SLS) Computer Aided Detection/Computer Aided Classification (CAD/CAC) algorithm to process side-scan sonar data obtained with the Woods Hole Oceanographic Institute's Remote Environmental Monitoring Units (REMUS) autonomous underwater vehicle (AUV). To date, Raytheon has demonstrated the ability to effectively execute mine-hunting missions with the REMUS vehicle through the fusion of its CAD/CAC algorithm with several other CAD/CAC algorithms to achieve low false alarm rates while maintaining a high probability of correct detection/classification. Mine-hunting in the very shallow water (VSW) environment poses a host of difficulties including such issues as: a higher incidence of man made clutter, significant interference due to biological sources (such as kelp or silt), the scouring of mines into the bottom, interference from surface/bottom bounce, and image distortion due to vehicle motion during image generation. These issues coupled with highly variable bottom conditions and small bottom targets make reliable hunting in the VSW environment very difficult. In order to be operationally viable, the individual CAD/CAC algorithms must demonstrate robustness over these very different mine-hunting environments. A higher normalized false alarm rate per algorithm is considered acceptable based on the false alarm reduction achieved through multi-algorithm fusion. Raytheon's recent CAD/CAC algorithm enhancements demonstrate a significant improvement in overall CAD/CAC performance across a diverse set of environments, from the relatively benign Gulf of Mexico environment to the more challenging areas off the coast of southern California containing significant biological and bottom clutter. The improvements are attributed to incorporating an image normalizer into the algorithm's pre-processing stage in conjunction with several other modifications. The algorithm enhancements resulted in an 11% increase in overall correct classification probability with an accompanying 17% reduction in false alarm rate, when averaged over the multiple environments. The paper discusses the algorithm enhancements and presents the detailed performance results.

Proceedings Article | 11 September 2003 Paper

Application of fusion algorithms for computer-aided detection and classification of bottom mines to shallow water test data from the battle space preparation autonomous underwater vehicle (BPAUV)

Charles Ciany, William Zurawski, Gerald Dobeck

Proceedings Volume 5089, (2003) https://doi.org/10.1117/12.487581

KEYWORDS: Detection and tracking algorithms, Image fusion, Data fusion, Image classification, Image processing, Image segmentation, Image processing algorithms and systems, Land mines, Algorithm development, Classification systems

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Proceedings Article | 13 August 2002 Paper

Application of fusion algorithims for computer-aided detection and classification of bottom mines to shallow-water test data

Charles Ciany, William Zurawski, Gerald Dobeck

Proceedings Volume 4742, (2002) https://doi.org/10.1117/12.479113

KEYWORDS: Detection and tracking algorithms, Image classification, Image fusion, Data fusion, Target detection, Image processing, Image segmentation, Land mines, Classification systems, Image processing algorithms and systems

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Proceedings Article | 18 October 2001 Paper

Performance of fusion algorithms for computer-aided detection and classification of mines in very shallow water obtained from testing in navy Fleet Battle Exercise-Hotel 2000

Charles Ciany, William Zurawski, Ian Kerfoot

Proceedings Volume 4394, (2001) https://doi.org/10.1117/12.445439

KEYWORDS: Detection and tracking algorithms, Image fusion, Data fusion, Image classification, Image processing, Image segmentation, Binary data, Image processing algorithms and systems, Classification systems, Target detection

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Proceedings Article | 22 August 2000 Paper

Data fusion of computer-aided detection/computer-aided classification algorithms for classification of mines in very shallow water environments

Jim Huang, Charles Ciany, Michael Broadman, Sheri Doran

Proceedings Volume 4038, (2000) https://doi.org/10.1117/12.396224

KEYWORDS: Data fusion, Image fusion, Image processing, Image classification, Detection and tracking algorithms, Tellurium, Computer aided diagnosis and therapy, Target detection, Image segmentation, Classification systems

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Showing 5 of 10 publications

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