Prof. John Barron Profile

Prof. John Barron

at Western Univ

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Publications (3)

Proceedings Article | 27 December 2000 Paper

3D foveated visualization on the Web

John Schermann, John Barron, Irene Gargantini

Proceedings Volume 4311, (2000) https://doi.org/10.1117/12.411909

KEYWORDS: Image compression, Wavelets, Image resolution, Java, 3D image processing, Medical imaging, Internet, Visualization, Optical resolution, 3D visualizations

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Recent developments in Internet technology, combined with the computerization of hospital radiology departments, allow the remote viewing of medical data images. Generally, however, medical images are data intensive and the transmission of such images over a network can consumer large amounts of network resources. Previous work by Liptay et al, presented an interactive, progressive program (implemented in JAVA and requiring a web browser) that allowed the transmission of multi-resolution JPEG image data using various ROI (Region of Interest) strategies in order to minimize Internet bandwidth requirements. This work handles both 2D and 3D image data, but 3D data was treated as a sequence of 2D images, where each 2D image had to be individually requested by the system. The work described in this paper replaces the representation of 3D data as a 2D JPEG image sequence with a single block of lossy 3D image data compressed using wavelets. In a similar fashion, 2D image data is wavelet compressed. Wavelet decomposition has been shown to have consistently better image quality at high compression ratios than other lossy compression methods. We use wavelet compression in a JAVA application program on the server side to construct a lossy low resolution version of the data. As well, high resolution difference sub-blocks of data are also created by the JAVA application; a difference sub-block and the corresponding low resolution lossless data. Transmitting the low resolution image and difference sub-blocks (as requested) only requires a small fraction of the network bandwidth compared to that which would otherwise be needed to transmit the entire lossless data set. The user, via a JAVA applet on the client side, is provided with a number of methods to choose a trajectory (sequence) of regions of interest in the low resolution image. Once the region(s) of interest are chosen, the sub-blocks of image data in the various trajectories are then retrieved and integrated into the low resolution display to provide lossless reconstruction in the regions of interest. Our program significantly reduces download time since extraneous information is not transmitted.

Proceedings Article | 20 December 1999 Paper

WWW interactive progressive local image transmission system

Tiffany-Emil Liptay, John Barron, Irene Gargantini

Proceedings Volume 3964, (1999) https://doi.org/10.1117/12.373452

KEYWORDS: Image transmission, Image compression, Internet, Image resolution, Medical imaging, Java, Prototyping, Image processing, 3D image processing, Wavelets

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Proceedings Article | 1 March 1992 Paper

Spatiotemporal edge focusing with multiple-pixel interframe motion

A. Young, John Barron

Proceedings Volume 1708, (1992) https://doi.org/10.1117/12.58617

KEYWORDS: Detection and tracking algorithms, Evolutionary algorithms, Artificial intelligence, Machine vision, Robotics, Edge detection, Adaptive optics, Edge roughness, Sensors, Hough transforms

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