Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Missing data correction in still images and image sequences
Proceedings of the tenth ACM international conference on Multimedia
Calibrated, Registered Images of an Extended Urban Area
International Journal of Computer Vision
Mosaics of Scenes with Moving Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Discriminative Training for Object Recognition Using Image Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Video Mosaics for Virtual Environments
IEEE Computer Graphics and Applications
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Fast covariance computation and dimensionality reduction for sub-window features in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Improving spatiotemporal inpainting with layer appearance models
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
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We present a technique for efficiently constructing a “clean” texture map of a partially occluded building facade from a series of images taken by a moving camera. After a robust registration procedure, building regions blocked by trees, signs, people, and other foreground objects are automatically inferred via the median absolute deviation of colors from different source images mapping to the same mosaic pixels. In previous work we extended an existing non-parametric inpainting algorithm for filling such holes to incorporate spatiotemporal appearance and motion cues in order to correctly replace the outlier pixels of the texture map. In contrast to other inpainting techniques that perform an exhaustive search over the image, in this work we introduce a principal components-based method that learns to recognize patches that locally adhere to the properties of the building being mapped, resulting in a significant performance boost with results of indistinguishable quality. Results are demonstrated on sequences where previous stitching and inpainting algorithms fail.