Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Learning How to Inpaint from Global Image Statistics
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Image completion with structure propagation
ACM SIGGRAPH 2005 Papers
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Image inpainting by global structure and texture propagation
Proceedings of the 15th international conference on Multimedia
Image Repairing: robust image synthesis by adaptive ND tensor voting
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Covariant derivatives and vision
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Evaluation of color image segmentation algorithms based on histogram thresholding
VLBV'05 Proceedings of the 9th international conference on Visual Content Processing and Representation
Filling-in by joint interpolation of vector fields and gray levels
IEEE Transactions on Image Processing
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
An automatic structure-aware image extrapolation applied to error concealment
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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A fully automatic algorithm for substitution of missing visual information is presented. The missing parts of a picture may have been caused by damages to or transmission loss of the physical picture. In the former case, the picture is scanned and the damage is considered as holes in the picture while, in the latter case, the lost areas are identified. The task is to derive subjectively matching contents to be filled into the missing parts using the available picture information. The proposed method arises from the observation that dominant structures, such as object contours, are important for human perception. Hence, they are accounted for in the filling process by using tensor voting, which is an approach based on the Gestalt laws of proximity and good continuation. Missing textures surrounding dominant structures are determined to maximize a new segmentation-based plausibility criterion. An efficient post-processing step based on a cloning method minimizes the annoyance probability of the inpainted textures given a boundary condition. The experiments presented in this paper show that the proposed method yields better results than the state-of-the-art.