Automatic Feature Recognition for GPR Image Processing
This paper presents an automatic feature recognition
method based on center-surround difference detecting and fuzzy logic
that can be applied in ground-penetrating radar (GPR) image
processing. Adopted center-surround difference method, the salient
local image regions are extracted from the GPR images as features of
detected objects. And fuzzy logic strategy is used to match the
detected features and features in template database. This way, the
problem of objects detecting, which is the key problem in GPR image
processing, can be converted into two steps, feature extracting and
matching. The contributions of these skills make the system have the
ability to deal with changes in scale, antenna and noises. The results of
experiments also prove that the system has higher ratio of features
sensing in using GPR to image the subsurface structures.
[1] Daniels DJ, Ground Penetrating Radar (2nd Edition). Knoval (Institution
of Engineering and Technology). pp. 1-4, (2004).
[2] Miao JIN, Yonghui ZHAO, Jiansheng WU and Xiongyao XIE, "Gradient
Method to Extract ROI of GPR Image", Journal of Image and Graphics,
Vol. 14, No. 4, pp. 579-584, April 2009.
[3] Jin-feng Hu, Zheng-ou Zhou "Target Detection and Orientation in
Subsurface Penetrating Radar Data " , Chinese Journal of Scientific
Instrument, Vol. 27, No. 4, pp. 372-375, April 2006.
[4] M Heisenberg and R Wolf, Studies of Brain Function, Springer-Verlag
Press, Berlin, 1984.
[5] T Eduardo and C Torras, "Detection of Nature Landmarks through
Multiscale Opponent Features", Proceedings of International Conference
on Pattern Recognition, Barcelona, pp. 976-979, September 2000.
[6] W Sheng and B Xia, "Texture segmentation method based on Gabor ring
filtering", Infrared and Laser Engineering (in Chinese), Vol. 32, No. 5, pp.
484-488, 2003.
[7] K Mikolajczyk and C Schmid, "Scale & affine Invariant Interest Point
Detectors", International Journal of Computer Vision, Vol. 60, No. 1, pp.
63-86, 2004.
[8] L Wang and Z X Cai, "Saliency based natural landmarks detection under
unknown environments", Pattern recognition and artificial intelligence (in
Chinese), Vol. 31, No. 1, pp. 46-51, 2006.
[9] D Lowe, "Object Recognition from Local Scale Invariant Features",
Proceedings of the International Conference on Computer Vision, Greece,
pp. 1150-1157, September 1999.
[1] Daniels DJ, Ground Penetrating Radar (2nd Edition). Knoval (Institution
of Engineering and Technology). pp. 1-4, (2004).
[2] Miao JIN, Yonghui ZHAO, Jiansheng WU and Xiongyao XIE, "Gradient
Method to Extract ROI of GPR Image", Journal of Image and Graphics,
Vol. 14, No. 4, pp. 579-584, April 2009.
[3] Jin-feng Hu, Zheng-ou Zhou "Target Detection and Orientation in
Subsurface Penetrating Radar Data " , Chinese Journal of Scientific
Instrument, Vol. 27, No. 4, pp. 372-375, April 2006.
[4] M Heisenberg and R Wolf, Studies of Brain Function, Springer-Verlag
Press, Berlin, 1984.
[5] T Eduardo and C Torras, "Detection of Nature Landmarks through
Multiscale Opponent Features", Proceedings of International Conference
on Pattern Recognition, Barcelona, pp. 976-979, September 2000.
[6] W Sheng and B Xia, "Texture segmentation method based on Gabor ring
filtering", Infrared and Laser Engineering (in Chinese), Vol. 32, No. 5, pp.
484-488, 2003.
[7] K Mikolajczyk and C Schmid, "Scale & affine Invariant Interest Point
Detectors", International Journal of Computer Vision, Vol. 60, No. 1, pp.
63-86, 2004.
[8] L Wang and Z X Cai, "Saliency based natural landmarks detection under
unknown environments", Pattern recognition and artificial intelligence (in
Chinese), Vol. 31, No. 1, pp. 46-51, 2006.
[9] D Lowe, "Object Recognition from Local Scale Invariant Features",
Proceedings of the International Conference on Computer Vision, Greece,
pp. 1150-1157, September 1999.
@article{"International Journal of Information, Control and Computer Sciences:59548", author = "Yi-an Cui and Lu Wang and Jian-ping Xiao", title = "Automatic Feature Recognition for GPR Image Processing", abstract = "This paper presents an automatic feature recognition
method based on center-surround difference detecting and fuzzy logic
that can be applied in ground-penetrating radar (GPR) image
processing. Adopted center-surround difference method, the salient
local image regions are extracted from the GPR images as features of
detected objects. And fuzzy logic strategy is used to match the
detected features and features in template database. This way, the
problem of objects detecting, which is the key problem in GPR image
processing, can be converted into two steps, feature extracting and
matching. The contributions of these skills make the system have the
ability to deal with changes in scale, antenna and noises. The results of
experiments also prove that the system has higher ratio of features
sensing in using GPR to image the subsurface structures.", keywords = "feature recognition, GPR image, matching strategy,salient image", volume = "4", number = "1", pages = "132-4", }