Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
A Comparative Assessment of Three Approaches to Pixel-Level Human Skin-Detection
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photo Defect Detection for Image Inpainting
ISM '05 Proceedings of the Seventh IEEE International Symposium on Multimedia
Supervised texture detection in images
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Digital color restoration of old paintings
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Lacunas are a common form of the damage that can occur to paintings and more often to murals. Taking Dunhuang murals as research background, a new algorithm to detect and segment the lacuna area from mural images is proposed, which consists of a training phase and a runtime phase. In the training phase, a Bayesian classifier is trained. At runtime, the Bayesian classifier is first applied to perform the rough lacuna regions detection. Then, a graph representing the mural image is built with output of the Bayesian classifier. The domain knowledge of murals is incorporated into the graph in this step. At last, the image segmentation using graph cut is done based on the minimal cut/maximal flow algorithm. The outputs of the image segmentation are lacuna regions and background regions. About 250 high resolution Dunhuang mural images are collected to test the proposed method's performance. Experimental results have demonstrated its validity under certain variations. This research has the potential to provide a computer aided tool for mural protectors to restore damage mural paintings.