Dr. Neil J. Gunther Profile

Dr. Neil J. Gunther

Principal consultant at Performance Dynamics Co

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Proceedings Article | 5 September 2008 Paper

Experiments supporting the concept of a g(2)-camera

Dmitri Boiko, Neil Gunther, Maximilian Sergio, Cristiano Niclass, Giordano Beretta, Edoardo Charbon

Proceedings Volume 7092, 709216 (2008) https://doi.org/10.1117/12.795166

KEYWORDS: Sensors, Photons, Polaritons, Correlation function, Light sources, Lamps, Imaging devices, Cameras, Optical microcavities, Interferometers

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Proceedings Article | 30 August 2006 Paper

Design rules for quantum imaging devices: experimental progress using CMOS single-photon detectors

Edoardo Charbon, Neil Gunther, Dmitri Boiko, Giordano Beretta

Proceedings Volume 6305, 63050M (2006) https://doi.org/10.1117/12.681235

KEYWORDS: Sensors, Imaging devices, Single photon, Quantum communications, Single photon detectors, CMOS technology, Imaging systems, Photodetectors, CMOS sensors, Quantum information

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Proceedings Article | 25 August 2005 Paper

Towards practical design rules for quantum communications and quantum imaging devices

Neil Gunther, Giordano Beretta

Proceedings Volume 5893, 58930W (2005) https://doi.org/10.1117/12.615761

KEYWORDS: Sensors, Interferometers, Particles, Quantum communications, Electrons, Visualization, Imaging devices, Geometrical optics, Mirrors, Quantum computing

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Proceedings Article | 27 December 2000 Paper

Benchmark for image retrieval using distributed systems over the Iinternet: BIRDS-I

Neil Gunther, Giordano Beretta

Proceedings Volume 4311, (2000) https://doi.org/10.1117/12.411898

KEYWORDS: Image retrieval, Internet, Databases, Distributed computing, Image processing, Taxonomy, Computing systems, Content based image retrieval, Telecommunications, Systems modeling

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Comparing the performance of CBIR (Content-Based Image Retrieval) algorithms is difficult. Private data sets are used so it is controversial to compare CBIR algorithms developed by different researchers. Also, the performance of CBIR algorithms is usually measured on an isolated, well- tuned PC or workstation. In a real-world environment, however, the CBIR algorithms would only constitute a minor component among the many interacting components needed to facilitate a useful CBIR application e.g., Web-based applications on the Internet. The Internet, being a shared medium, dramatically changes many of the usual assumptions about measuring CBIR performance. Any CBIR benchmark should be designed form a networked systems standpoint. Networked system benchmarks have been developed for other applications e.g., text retrieval, and relational database management. These benchmarks typically introduce communication overhead because the real systems they model are distributed applications e.g., and airline reservation system. The most common type of distributed computing architecture uses a client/server model. We present our implementation of a client/server CBIR benchmark called BIRDS-I (Benchmark for Image Retrieval using Distributed Systems over the Internet) to measure image retrieval performance over the Internet. The BIRDS-I benchmark has been designed with the trend toward the use of small personalized wireless-internet systems in mind. Web-based CBIR implies the use of heterogeneous image sets and this, in turn, imposes certain constraints on how the images are organized and the type of performance metrics that are applicable. Surprisingly, BIRDS-I only requires controlled human intervention for the compilation of the image collection and none for the generation of ground truth in the measurement of retrieval accuracy. Benchmark image collections need to be evolved incrementally toward the storage of millions of images and that scaleup can only be achieved through the use of computer-incrementally toward the storage of millions of images and that scaleup can only be achieved through the use of computer-aided compilation. Finally, the BIRDS-I scoring metric introduces a tightly optimized image-ranking window, which is important for the future benchmarking of large- scale personalized wireless-internet CBIR systems.

Proceedings Article | 20 December 1999 Paper

Seeing the forest in the tree: applying VRML to mathematical problems in number theory

Neil Gunther

Proceedings Volume 3964, (1999) https://doi.org/10.1117/12.373461

KEYWORDS: Fractal analysis, Mathematics, Visualization, Zoom lenses, Computer programming, Virtual reality, Mathematical modeling, Computer programming languages, Internet, Standards development

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Conference Committee Involvement (1)

Internet Imaging IV

21 January 2003 | Santa Clara, CA, United States

Course Instructor

SC082: Images Over the Internet: The Performance Picture

The World Wide Web has made the internet image-intensive. The development of new imaging algorithms tends to focus on local performance improvements. The web is a system technology where the interaction of its components (e.g., browser, network, website) determines global performance. Image compression for storage capacity and transfer performance on a website can be reduced by decompression and rendering performance on the browser side. This course considers the big picture on image performance. Topics covered include: introduction to computer performance and capacity planning terminology, techniques for estimating web server capacity, internet-related impact on web server performance, transfer of images and video over the internet, methods for estimating image transfer performance, and image transfers over the web.

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