A New Method for Detection of Artificial Objects and Materials from Long Distance Environmental Images
The article presents a new method for detection of
artificial objects and materials from images of the environmental
(non-urban) terrain. Our approach uses the hue and saturation (or Cb
and Cr) components of the image as the input to the segmentation
module that uses the mean shift method. The clusters obtained as the
output of this stage have been processed by the decision-making
module in order to find the regions of the image with the significant
possibility of representing human. Although this method will detect
various non-natural objects, it is primarily intended and optimized for
detection of humans; i.e. for search and rescue purposes in non-urban
terrain where, in normal circumstances, non-natural objects shouldn-t
be present. Real world images are used for the evaluation of the
method.
[1] S. Bahadori and L. Iocchi, "Human body detection in the RoboCup
rescue scenario rescue", Workshop in RoboCup competitions, Padua,
Italy, 2003.
[2] I. R. Nourbakhsh, K. Scara, M. Koes and M. Yong, "Human-robot
teaming for search and rescue", Pervasive Computing, pp. 72-78, 2005.
[3] A. Birk and S. Carpin, "Rescue robotics: a crucial milestone on the road
to autonomous systems", Advanced Robotics, 20(5), pp. 595-605, 2006.
[4] H. Yalcin, R. Collins and M. Hebert, "Background estimation under
rapid gain change in thermal imagery", Computer Vision and Image
Understanding, Volume 106, Issues 2-3, Special issue on Advances in
Vision Algorithms and Systems beyond the Visible Spectrum, pp. 148-
161, 2007.
[5] A. Ollero and L. Merino, "Control and perception techniques for aerial
robotics", Annual Reviews in Control, 28, Elsevier, pp. 167-178, 2004.
[6] D. Manolakis, D. Marden and G. A. Shaw, "Hyperspectral image
processing for automatic target detection applications", Lincoln
Laboratory Journal, Volume 14, Number1, pp. 79-116, 2003.
[7] T. Sumimoto et al., "Detection of a particular object from environmental
images under various conditions", Proceedings of the International
Symposium on Industrial Electronics, ISIE, IEEE, vol. 2., pp. 590-595,
2000.
[8] J. Pe├▒a, J. Lozano and P. Larrs├▒aga, "An empirical comparison of four
Initialization methods for the k-means algorithm," Pattern Recognition
Letters, vol. 20, pp. 1027-1040, 1999.
[9] D. Comaniciu and P. Meer, "Mean shift: A robust approach toward
feature space analysis", IEEE Trans. Pattern Anal. Machine Intell, 24,
pp. 603-619, 2002.
[10] C. Ünsalan and K. L. Boyer, "A system to detect houses and residental
street in multispectral satellite images", Computer Vision and Image
Understanding, 98, 423-461, 2005.
[1] S. Bahadori and L. Iocchi, "Human body detection in the RoboCup
rescue scenario rescue", Workshop in RoboCup competitions, Padua,
Italy, 2003.
[2] I. R. Nourbakhsh, K. Scara, M. Koes and M. Yong, "Human-robot
teaming for search and rescue", Pervasive Computing, pp. 72-78, 2005.
[3] A. Birk and S. Carpin, "Rescue robotics: a crucial milestone on the road
to autonomous systems", Advanced Robotics, 20(5), pp. 595-605, 2006.
[4] H. Yalcin, R. Collins and M. Hebert, "Background estimation under
rapid gain change in thermal imagery", Computer Vision and Image
Understanding, Volume 106, Issues 2-3, Special issue on Advances in
Vision Algorithms and Systems beyond the Visible Spectrum, pp. 148-
161, 2007.
[5] A. Ollero and L. Merino, "Control and perception techniques for aerial
robotics", Annual Reviews in Control, 28, Elsevier, pp. 167-178, 2004.
[6] D. Manolakis, D. Marden and G. A. Shaw, "Hyperspectral image
processing for automatic target detection applications", Lincoln
Laboratory Journal, Volume 14, Number1, pp. 79-116, 2003.
[7] T. Sumimoto et al., "Detection of a particular object from environmental
images under various conditions", Proceedings of the International
Symposium on Industrial Electronics, ISIE, IEEE, vol. 2., pp. 590-595,
2000.
[8] J. Pe├▒a, J. Lozano and P. Larrs├▒aga, "An empirical comparison of four
Initialization methods for the k-means algorithm," Pattern Recognition
Letters, vol. 20, pp. 1027-1040, 1999.
[9] D. Comaniciu and P. Meer, "Mean shift: A robust approach toward
feature space analysis", IEEE Trans. Pattern Anal. Machine Intell, 24,
pp. 603-619, 2002.
[10] C. Ünsalan and K. L. Boyer, "A system to detect houses and residental
street in multispectral satellite images", Computer Vision and Image
Understanding, 98, 423-461, 2005.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51489", author = "H. Dujmic and V. Papic and H. Turic", title = "A New Method for Detection of Artificial Objects and Materials from Long Distance Environmental Images", abstract = "The article presents a new method for detection of
artificial objects and materials from images of the environmental
(non-urban) terrain. Our approach uses the hue and saturation (or Cb
and Cr) components of the image as the input to the segmentation
module that uses the mean shift method. The clusters obtained as the
output of this stage have been processed by the decision-making
module in order to find the regions of the image with the significant
possibility of representing human. Although this method will detect
various non-natural objects, it is primarily intended and optimized for
detection of humans; i.e. for search and rescue purposes in non-urban
terrain where, in normal circumstances, non-natural objects shouldn-t
be present. Real world images are used for the evaluation of the
method.", keywords = "Landscape surveillance, mean shift algorithm,
image segmentation, target detection.", volume = "2", number = "7", pages = "1334-5", }