Man-made Object Detection Based on Texture Clustering and Geometric Structure Feature Extracting

Full Text (PDF, 781KB), PP.9-16

Views: 0 Downloads: 0

Author(s)

Fei Cai 1,* Honghui Chen 1 Jianwei Ma 1

1. Key Laboratory for Information System Engineering, School of Information System and Management, National University of Defense Technology, Changsha, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2011.02.02

Received: 3 Aug. 2010 / Revised: 12 Oct. 2010 / Accepted: 2 Jan. 2011 / Published: 8 Mar. 2011

Index Terms

Man-made object detection, image segmentation, object marking, feature extraction, texture clustering

Abstract

Automatic aerial image interpretation is one of new rising high-tech application fields, and it’s proverbially applied in the military domain. Based on human visual attention mechanism and texture visual perception, this paper proposes a new approach for man-made object detection and marking by extracting texture and geometry structure features. After clustering the texture feature to realize effective image segmentation, geometry structure feature is obtained to achieve final detection and marking. Thus a man-made object detection methodology 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 Clustering and Geometric Structure Feature Extracting", International Journal of Information Technology and Computer Science(IJITCS), vol.3, no.2, pp.9-16, 2011. DOI: 10.5815/ijitcs.2011.02.02

Reference

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

[2] S. Alvarez, M.A.Sotelo, M. Ocana et al. Perception Advances in Outdoor Vehicle Detection for Automatic Cruise Control[J]. Robotica. 2010, 28(5):765-779.

[3] 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.

[4] Mourad Bouziani, Kalifa Goita, Dongchen He. Automatic Change Detection of Buildings in Urban Environment from Very High Spatial Resolution Images Using Existing Geodatabase and Prior Knowledge[J]. ISPRS Journal of Photogrammetry and Remote Sensing. 2010,65:143:153.

[5] Turker,M. Sumer.E. Buildings-based Damage Detection Due to Tarthquake Using the Watershed Segmentation of the Post-event Aerial Images[J]. International Journal of Remote Sensing. 2008, 29(11).3073-3089.

[6] Li Peijun, Xu Haiqing, Guo Jiancong. Urban Building Damage Detection from Very High Resolution Imagery Using OCSVM and Spatial Features[J]. International Journal of Remote Sensing, 2010, 31(13): 3393-3409

[7] Supannee Tanathong, Kurt T.Rudahl, Sally E.Goldin. Object Oriented Change Detection of Buildings after the Indian Ocean Tsunami Disaster [C].Proceedings of ECTI-CON. 2008(1): 65-68.

[8] 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.

[9] Cai Fei, Tu Dan. Survey on Man-made Object Detection in Visible Imagery[J]. Application Research of Computers, 2010, 27(7): 2430-2434.

[10] Ruch, O, Dufour, J. Real-time. Automatic Target Recognition and Identification of Ground Vehicles for Airborne Optronic Systems[J]. Proceedings of the SPIE, 2005(5909): 11–20.

[11] Chen JianMing, Han ChinChuan, Fan KaoChin. Aircraft Type Recognition in Satellite Images[J]. IEEE Proceedings: Vision, Image and Signal Processing. 2005, 152(3):307-315.

[12] Wang Wei, Yang Xin. Rapid Man-made Object Morphological Segmentation for Aerial Images Using A Multi-scaled Geometric Image Analysis[J]. Image and Vision Computing 2010(28):626–633.

[13] Jun Yang, Peng Zhang, Runsheng Wang. Extracting Man-made Region(s) Based on Attention Driven Level-set Evolution[C]. Fourth International Conference on Image and Graphics. 2007(117):465-470.

[14] Xavier Perrotton, Marc Sturzel, Michel Roux. Automatic Object Detection on Aerial Images Using Local Descriptors and Image Synthesis[C].ICVS 2008, 2008(5008): 302–311.

[15] Saad Ali, Mubarak Shah. A Supervised Learning Framework for Generic Object Detection in Images[C]. Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005(8):1347-1354.

[16] Laurent Itti, Christof Koch. A Saliency-based Search Mechanism for Overt and Covert Shifts of Visual Attention [J].Vision Research 2000(40):1489–1506.

[17] Itti, L. Visual Attention and Target Detection in Cluttered Natural Scenes[J]. Optical Engineering, 2001, 40(9):1784-1793.

[18] Hae Jong Seo, Peyman Milanfar. Visual Saliency for Automatic Target Detection, Boundary Detection and Image Quality Assessment[C].ICASSP 2010.5578-5581.

[19] Wei Li, Chunhong Pan, Li-xiong Liu. Saliency-based Automatic Target Detection in forward Looking Infrared Images [C]. ICIP, 2009:957-960.

[20] E.Peli, Contrast in Complex Images[J]. Optical Society of America, 1990,7(10):2032–2040,

[21] Jinshan Tang, Scott Acton. Image Enhancement Using a Contrast Measure in the Compressed Domain[J]. IEEE Signal Processing Letters. 2003, 10(10): 289-292.

[22] Peter Howarth, Smfan M. Ruger. Evaluation of Texture Feature for Content-based Image Retrieval[C]. Third International Conference, CIVR, 2004: 326-334

[23] Smith S M, Brady M .Susan. A New Approach to Low Level Image Processing[J]. International Journal of Computer Vision 1997.23(1):45-78.

[24] Ridha Touzi, Arand Lopes, Pierre Bousquet. A Statistical and Geometrical Edge Detector for SAR Images[J]. IEEE Transaction on Geoscience and Remote Sensing.1988, 26(6):764-773.

[25] 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.

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