Linear feature detection and enhancement in noisy images via the Radon transform
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of Three-Dimensional Object Location and Orientation from Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Hough Transform-An Efficient Method of Detecting Patterns in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized Hough transform: improved ellipse detection with comparison
Pattern Recognition Letters
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Randomized Generalized Hough Transform for 2-D Grayscale Object Detection
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Detection of linear and planar structures in 3D subsurface images by iterative dimension reduction
Digital Signal Processing
Ground penetrating radar image preprocessing for embedded object in media
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Ground penetrating radar slice reconstruction for embedded object in media with target follow
WSEAS Transactions on Computers
Compressive sensing of underground structures using GPR
Digital Signal Processing
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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In this paper, we propose a semi-automatic approach to the detection of linear scattering objects in geo-radar data sets, based on the 3D radon transform. The method that we propose is iterative, as each detected object is removed from the data set before the next iteration, in order to avoid mutual interference or masking: In addition, the algorithm is able to further analyze the data set in a local fashion in order to eliminate spurious targets from the set of lines of maximum consensus.Our algorithm proved robust and reliable even in the presence of data affected by heavy noise, artifacts and other undesired scattering objects.Although the application scenario of the proposed algorithm is that of the analysis of data sets generated by a ground penetrating radar, the method is general enough to apply to any problems where linear objects needs to be identified and localized in volumetric data.