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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Flitton, G. Breckon, T.P. Megherbi, N. |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Engineering, Cranfield University, U.K. (Flitton, G.; Breckon, T.P.; Megherbi, N.) |
| Abstract | We introduce a novel 3D extension to the hierarchical visual cortex model used for prior work in 2D object recognition. Prior work on the use of the visual cortex standard model for the explicit task of object class recognition has solely concentrated on 2D imagery. In this paper we discuss the explicit 3D extension of each layer in this visual cortex model hierarchy for use in object recognition in 3D volumetric imagery. We apply this extended methodology to the automatic detection of a class of threat items in Computed Tomography (CT) security baggage imagery. The CT imagery suffers from poor resolution and a large number of artefacts generated through the presence of metallic objects. In our examination of recognition performance we make a comparison to a codebook approach derived from a 3D SIFT descriptor and demonstrate that the visual cortex method out-performs in this imagery. Recognition rates in excess of 95% with minimal false positive rates are demonstrated in the detection of a range of threat items. |
| Starting Page | 3634 |
| Ending Page | 3641 |
| File Size | 672752 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467312264 |
| ISSN | 10636919 |
| e-ISBN | 9781467312288 |
| e-ISBN | 9781467312271 |
| DOI | 10.1109/CVPR.2012.6248109 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Visualization Computed tomography Brain modeling Vectors Image recognition Solid modeling Object recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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