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An Innovative Method for Tracking and Interpolating of Cracks to Recover Real Image Feature


Affiliations
1 Department of computer Applications, GIET, Rajahmundry.
2 Department of EXTC, SFIT, Mumbai.
     

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A methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterwards, the thin dark brush strokes which have been misidentified as cracks are removed using Median Radial Basis Function (MRBF) neural network on hue and saturation data or a semi-automatic procedure based on region growing. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks. Within limited testing, the accuracy of the detection and analysis of cracks is better with the proposed method than with conventional methods.

Keywords

Thresholding, Cracks, Digitized Paintings, Neural Networks, Median Radial Basis Function, Top Hat Transform, Statistics Filters, Cracks Detection
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  • An Innovative Method for Tracking and Interpolating of Cracks to Recover Real Image Feature

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Authors

R. Tamilkodi
Department of computer Applications, GIET, Rajahmundry.
K. Valli Madhavi
Department of computer Applications, GIET, Rajahmundry.
K. Jayasudha
Department of EXTC, SFIT, Mumbai.

Abstract


A methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterwards, the thin dark brush strokes which have been misidentified as cracks are removed using Median Radial Basis Function (MRBF) neural network on hue and saturation data or a semi-automatic procedure based on region growing. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks. Within limited testing, the accuracy of the detection and analysis of cracks is better with the proposed method than with conventional methods.

Keywords


Thresholding, Cracks, Digitized Paintings, Neural Networks, Median Radial Basis Function, Top Hat Transform, Statistics Filters, Cracks Detection

References