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Proceedings Paper

Performance-scalable volumetric data classification for online industrial inspection
Author(s): Aby Jacob Abraham; Mustapha Sadki; R. M. Lea
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Paper Abstract

Non-intrusive inspection and non-destructive testing of manufactured objects with complex internal structures typically requires the enhancement, analysis and visualization of high-resolution volumetric data. Given the increasing availability of fast 3D scanning technology (e.g. cone-beam CT), enabling on-line detection and accurate discrimination of components or sub-structures, the inherent complexity of classification algorithms inevitably leads to throughput bottlenecks. Indeed, whereas typical inspection throughput requirements range from 1 to 1000 volumes per hour, depending on density and resolution, current computational capability is one to two orders-of-magnitude less. Accordingly, speeding up classification algorithms requires both reduction of algorithm complexity and acceleration of computer performance. A shape-based classification algorithm, offering algorithm complexity reduction, by using ellipses as generic descriptors of solids-of-revolution, and supporting performance-scalability, by exploiting the inherent parallelism of volumetric data, is presented. A two-stage variant of the classical Hough transform is used for ellipse detection and correlation of the detected ellipses facilitates position-, scale- and orientation-invariant component classification. Performance-scalability is achieved cost-effectively by accelerating a PC host with one or more COTS (Commercial-Off-The-Shelf) PCI multiprocessor cards. Experimental results are reported to demonstrate the feasibility and cost-effectiveness of the data-parallel classification algorithm for on-line industrial inspection applications.

Paper Details

Date Published: 8 March 2002
PDF: 12 pages
Proc. SPIE 4664, Machine Vision Applications in Industrial Inspection X, (8 March 2002); doi: 10.1117/12.460203
Show Author Affiliations
Aby Jacob Abraham, Brunel Univ. (United Kingdom)
Mustapha Sadki, Brunel Univ. (United Kingdom)
R. M. Lea, Brunel Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 4664:
Machine Vision Applications in Industrial Inspection X
Martin A. Hunt, Editor(s)

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