Man-made Object Detection Based on Texture Visual Perception

Full Text (PDF, 679KB), PP.1-8

Views: 0 Downloads: 0


Fei Cai 1,* Honghui Chen 1 Jianwei Ma 1

1. Department of Science and Technology on Information Systems Engineering National University of Defense Technology ChangSha, HuNan Province, China

* Corresponding author.


Received: 23 Feb. 2012 / Revised: 6 Apr. 2012 / Accepted: 16 May 2012 / Published: 29 Jun. 2012

Index Terms

Man-made object detection, image interpretation, feature extraction, clustering


Based on human visual attention mechanism and texture visual perception, this paper proposes a method for man-made object detection by extracting texture and geometry structure features. Followed by clustering the texture feature, geometry structure feature is obtained to realize final detection. Then a man-made object detection scheme is designed, by which typical man-made objects in complex natural background, including airplanes, tanks and vehicles can be detected. The experiments sustain that the proposed method is effective and rational.

Cite This Paper

Fei Cai,Honghui Chen,Jianwei Ma,"Man-made Object Detection Based on Texture Visual Perception", IJEM, vol.2, no.3, pp.1-8, 2012. DOI: 10.5815/ijem.2012.03.01


[1] Li Bo, Chen Qi-mei, Guo Fan. Freeway Auto-surveillance From Traffic Video [C]. 2006 6th International Conference on ITS Telecommunications Proceedings. 2006. pp: 167-170.

[2] Hinz S, Baumgartner A. Automatic extraction of urban road networks from multi-view aerial imagery [J]; ISPRS Journal of Photogrammetry & Remote sensing, 2003, 58:83-98.

[3] BARNIV Y. Dynamic programming solution for detection dim moving target [J]. IEEE, Trans, AES 1994,30(1): 197-212.

[4] Jianxin Mei, Duan Shan, Qianqing Qin. Method for special targets detection based on support vector machines[J]. Geomatics and information science of Wuhan university 2004.29(10): 912-915.(in Chinese)

[5] Caifei, Tudan. Survey on man-made object detection in visible imagery. Application Research of Computers, 2010, 27(7): 2430-2434. (in Chinese)

[6] E.Peli, "Contrast in complex images," J.Opt.Soc.Amer.A, 1990.vol.7,pp.2032–2040,

[7] Jinshan Tang, Scott Acton. Image Enhancement Using a Contrast Measure in the Compressed Domain [J]. IEEE signal processing LETTERS. 10(10). 2003.10. 289-292.

[8] Peter Howarth, Smfan M. Ruger. Evaluation of Texture Feature for Content-based Image Retrieval [C]. Third International Conference,CIVR2004: 326-334

[9] Smith S M. Brady M .Susan –A New Approach to Low Level Image Processing [J]. International Journal of Computer Vision 1997.23(1).

[10] Ridha Touzi, Arand Lopes, Pierre Bousquet. A Statistical and Geometrical Edge Detector for SAR Images [J]. IEEE Transaction on geoscience and remote sensing.1988. 11.26(6). 764-773.

[11] Yingying Chen, Zhaohui Yang, Qun Su. Automatic Recognition of Man-made Objects in SAR Images Using Support Vector Machines [J]. 2009 Urban Remote Sensing Joint Event 9(9): 78-83.

[12] M. J. Carlotto. A Cluster-based Approach for Detecting Manmade Objects and Changes in Imagery [J], IEEE Trans. Geoscience and Remote Sensing, 2005. 43(2):374-387

[13] E. Peli. "Contrast in complex images," J. Opt. Soc. Amer. A. vol. 7, pp. 2032–2040, 1990.