Jiuai Sun
Concealed object perception and recognition using a photometric stereo strategy
Sun, Jiuai; Smith, Melvyn; Farooq, Abdul; Smith, Lyndon
Authors
Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof
Abdul Farooq
Lyndon Smith Lyndon.Smith@uwe.ac.uk
Professor in Computer Simulation and Machine
Abstract
Following a review of current hidden objects detection techniques in a range of security applications, a strategy based on an innovative, low-cost photometric stereo technique is proposed to reveal concealed objects. By taking advantage of information rapidly acquired under different illumination conditions, various enhanced real time images can be produced, free from the confusion of textured camouflage. The perception of hidden objects is enhanced through the suppression of 2D reflectance patterns, optimization of synthetic illumination conditions and employment of more realistic artificial reflection models, designed to significantly enhance the visibility of concealed 3D shapes. The extracted surface normals can be used for the calculation of curvature and flatness attributes, and providing clues for subsequent hidden object detection and recognition tasks. Experiments on both simulated and real data have verified the strategy is useful for stealthy objects detection and may provide another modality of data for current monitoring system. The results demonstrate good potential application in the detection of concealed objects in security and military applications through the deployment of image enhancement and augmented reality devices.
Presentation Conference Type | Conference Paper (unpublished) |
---|---|
Conference Name | Advanced Concepts for Intelligent Vision Systems |
Start Date | Jun 8, 2009 |
End Date | Jun 8, 2009 |
Publication Date | Sep 1, 2009 |
Peer Reviewed | Peer Reviewed |
Pages | 445-455 |
Keywords | concealed object, perception, enhancement, photometric stereo |
Public URL | https://uwe-repository.worktribe.com/output/993520 |
Publisher URL | http://dx.doi.org/10.1007/978-3-642-04697-1_41 |
You might also like
Machine vision and deep learning for robotic harvesting of shiitake mushrooms
(2024)
Presentation / Conference Contribution
3D Machine vision and deep learning for enabling automated and sustainable assistive physiotherapy
(2024)
Presentation / Conference Contribution
Estimating water storage from images
(2024)
Presentation / Conference Contribution
Maize yield predictive models and mobile-based decision support system for smallholder farmers in Africa
(2022)
Presentation / Conference Contribution
A robust machine learning framework for diabetes prediction
(2021)
Presentation / Conference Contribution
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search