CfP for SPIE conference on "Parallel Processing for Imaging Applications"

[Yen-Kuang Chen]

http://tinyurl.com/ei111ppia

Due Dates:
- Abstract (500 words) and Summary (200 words): 28 June 2010
- Manuscript for On-site Proceedings: 15 November 2010

Papers submitted to this conference should fuse parallel
implementation design principles under physical constraints with an
understanding of imaging applications.

Imaging translates information into and out of the visual system with
today's computation engine of choice: digital electronic systems.
While scalar architectures are no longer scaling at historical rates,
we see a massive explosion in the total number of connected
computation devices and the ways that hardware architectures and
software parallel programming environments use these devices to work
in concert and in parallel. From the computing cloud to map-reduce
programming models and systems to multi-core CPUs to the regular
layout of graphics processing units (GPUs) to the increasing capacity
of FPGA fabrics, a range of parallel architectures and parallel
programming environments are available to designers and researchers to
solve computationally complex problems in efficient (and often
real-time) imaging applications.

Under physical constraints such as power, speed, and/or cost, the data
throughput and degree of data dependence of imaging applications
suggest a good match between parallel architectures and imaging
applications; similarly, the choice of parallel architectures often
reflects the structure of the imaging problem targeted by the
application. Thus, the duality of imaging problem definition and
parallelism implies that the efficient implementation of parallelism
for imaging offers insight into the mind's internal imaging
computation. This duality also implies that measures of parallel
efficiency can formalize the definition of many imaging problems. This
conference explores this duality through new parallel designs for
imaging and architectures and design tools to optimize parallelism in
imaging algorithms.

We expect papers in this conference to combine principles and
techniques for parallelism, such as:
- cloud computing
- GPU computing
- high-level parallel programming constructs
- design tools for extracting parallelism
- efficient, scalable architectures
- memory hierarchy design for parallel systems
- metrics for parallelism and capacity planning
- efficient algorithm mapping onto parallel hardware
- algorithmic classification by efficient parallel architecture
- algorithms for parallel scheduling and resource allocation.

Other novel parallel programming techniques, constructs, abstractions,
and implementations with an understanding of imaging applications,
such as:
- teleconferencing
- medical imaging
- remote sensing
- image fusion
- spectral imaging
- volumetric imaging
- compression
- halftoning
- color rendering
- raster image processing
- image analysis
- computer vision
- document analysis
- forensics
- resampling
- computational optics
- other novel imaging applications.