Wednesday, January 19, 2011

Parallel Processing for Image Recognition

In a few days, imaging technologists from around the world will be flocking to the San Francisco Airport Hyatt to attend the Electronic Imaging Symposium.

Monday 24 January from 10:40 AM to 11:10 AM many delegates will fasten their seat-belts in Sandpebble Room D, where IS&T Fellow and HP Labs Director and Distinguished Technologist Dr. Steven J. Simske will be giving his Invited Talk on Parallel Processing Considerations for Image Recognition Tasks in the Conference on Parallel Processing for Imaging Applications.

Many image recognition tasks are well-suited to parallel processing. The most obvious example is that many imaging tasks require the analysis of multiple images. From this standpoint, then, parallel processing need be no more complicated than assigning individual images to individual processors. However, there are three less trivial categories of parallel processing that will be considered in this paper: parallel processing (1) by task; (2) by image region; and (3) by meta-algorithm.

Parallel processing by task allows the assignment of multiple workflows—as diverse as optical character recognition [OCR], document classification and barcode reading—to parallel pipelines. This can substantially decrease time to completion for the document tasks. For this approach, each parallel pipeline is generally performing a different task. Parallel processing by image region allows a larger imaging task to be sub-divided into a set of parallel pipelines, each performing the same task but on a different data set. This type of image analysis is readily addressed by a map-reduce approach. Examples include document skew detection and multiple face detection and tracking. Finally, parallel processing by meta-algorithm allows different algorithms to be deployed on the same image simultaneously. This approach may result in improved accuracy.

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