The paper introduces the vector-gradient Hough transform (VGHT), a modified version of the gradient weighted Hough transform (GWHT), defined in vector space and able to exploit all the vector information of the gradient of luminosity. The new formulation, directly derived from the Radon transform, is analyzed and compared with the GWHT, in order to point out the improvement in selectivity provided by the VGHT in a strictly polar parametric space, without any relevant increase in computational complexity. This approach can be very suitable for identifying a specifically defined model of shapes in gray level images, ideally generated by a translation in the 2D space of a 1D luminosity profile. Finally, the suitability of the VGHT in real applications is shown with examples in the area of defect identification for automated visual inspection. © 1996 IEEE.
The vector-gradient Hough transform for identifying straight-translation generated shapes / Cucchiara, R.; Filicori, F.. - 2:(1996), pp. 502-510. (Intervento presentato al convegno 13th International Conference on Pattern Recognition, ICPR 1996 tenutosi a Vienna, aut nel 1996) [10.1109/ICPR.1996.546876].
The vector-gradient Hough transform for identifying straight-translation generated shapes
Cucchiara R.;
1996
Abstract
The paper introduces the vector-gradient Hough transform (VGHT), a modified version of the gradient weighted Hough transform (GWHT), defined in vector space and able to exploit all the vector information of the gradient of luminosity. The new formulation, directly derived from the Radon transform, is analyzed and compared with the GWHT, in order to point out the improvement in selectivity provided by the VGHT in a strictly polar parametric space, without any relevant increase in computational complexity. This approach can be very suitable for identifying a specifically defined model of shapes in gray level images, ideally generated by a translation in the 2D space of a 1D luminosity profile. Finally, the suitability of the VGHT in real applications is shown with examples in the area of defect identification for automated visual inspection. © 1996 IEEE.
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