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User Intervention Based Detection Removal of Cracks from Digitized Paintings

机译:基于用户干预的数字化绘画裂缝检测与消除

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An user intervention based advanced technique for the detection and elimination of cracks in digitized paintings and images is proposed in this paper. Usually cracks degrade the quality of painting as well as authenticity of painting becomes questionable. In the proposed method the cracks are detected by thresholding the result of the morphological top-hat transform. Further, misidentified cracks are detected either by involving user intervention or by using a semi-automatic procedure based on region growing technique. Finally, crack interpolation also called crack filling is performed using order statistics filters so as to restore the cracked image. The true positive rate and false positive rate are used to evaluate the performance of the proposed technique. We collected 2000 paintings & images classified as cracked and un-cracked online digital art database for experimental purpose. The result shows achievement of true positive rate of about 98.3% at the rate of 0.1 false positive per image. This is because of providing user intervention during module called identifying mis-identified cracks.
机译:本文提出了一种基于用户干预的先进技术,用于检测和消除数字化绘画和图像中的裂缝。通常,裂纹会降低绘画质量,并且绘画的真实性会受到质疑。在提出的方法中,通过对形态学礼帽变换的结果进行阈值检测来检测裂纹。此外,通过涉及用户干预或通过使用基于区域增长技术的半自动程序来检测出错误识别的裂纹。最后,使用顺序统计过滤器执行裂缝插值(也称为裂缝填充),以恢复裂缝图像。真阳性率和假阳性率用于评估所提出技术的性能。为了实验目的,我们收集了2000幅分类为破裂和未破裂的在线数字艺术数据库的绘画和图像。结果显示,每幅图像的假阳性率为0.1,假阳性率约为98.3%。这是因为在称为识别错误裂纹的模块期间提供了用户干预。

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