Making large-scale support vector machine learning practical
Advances in kernel methods
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Making Good Features Track Better
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ACM SIGGRAPH 2003 Papers
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
A Two Level Approach for Scene Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Video Mosaics for Virtual Environments
IEEE Computer Graphics and Applications
PCA-Based recognition for efficient inpainting
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
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The problem of removing blemishes in mosaics of building facades caused by foreground objects such as trees may be framed in terms of inpainting. Affected regions are first automatically segmented and then inpainted away using a combination of cues from unoccluded, temporally adjacent views of the same building patch, as well as surrounding unoccluded patches in the same frame. Discriminating the building layer from those containing foreground features is most directly accomplished through parallax due to camera motion over the sequence. However, the intricacy of tree silhouettes often complicates accurate motion-based segmentation, especially along their narrower branches. In this work we describe methods for automatically training appearance-based classifiers from a coarse motion-based segmentation to recognize foreground patches in static imagery and thereby improve the quality of the final mosaic. A local technique for photometric adjustment of inpainted patches which compensates for exposure variations between frames is also discussed.