Proceedings of the 27th annual conference on Computer graphics and interactive techniques
I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
Scale-Invariant Contour Completion Using Conditional Random Fields
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Scene completion using millions of photographs
ACM SIGGRAPH 2007 papers
Restoring partly occluded patterns: a neural network model with backward paths
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Simultaneous structure and texture image inpainting
IEEE Transactions on Image Processing
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In this paper, we proposed a bottom-up computational model of visual filling-in to recover not only the texture but also the structure pattern in the unknown area of the images. Different from previous works of image inpainting and texture synthesis, our approach in the first step recovers the structure information of the missing part of an image; and then in the second step, each missing region with homogeneous composition is recovered independently. The structure recovery strategy is based on Gestalt laws of human visual perception, especially the good continuation law that predict the curvilinear continuity in contour completion of human behavior. In the experiment section, we provide the comparative results of our model and other proposed methods. Our model can achieve better performance in recovering images, especially when the scene contains rich structural information.