Commenced in January 2007
Paper Count: 30184
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057061Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1068
 S. Bahadori and L. Iocchi, "Human body detection in the RoboCup rescue scenario rescue", Workshop in RoboCup competitions, Padua, Italy, 2003.
 I. R. Nourbakhsh, K. Scara, M. Koes and M. Yong, "Human-robot teaming for search and rescue", Pervasive Computing, pp. 72-78, 2005.
 A. Birk and S. Carpin, "Rescue robotics: a crucial milestone on the road to autonomous systems", Advanced Robotics, 20(5), pp. 595-605, 2006.
 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.
 A. Ollero and L. Merino, "Control and perception techniques for aerial robotics", Annual Reviews in Control, 28, Elsevier, pp. 167-178, 2004.
 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.
 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.
 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.
 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.
 C. ├£nsalan 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.