We present a semi-automatic system that converts conventional video shots to stereoscopic video pairs. The system requires just a few user-scribbles in a sparse set of frames. The system combines a diffusion scheme, which takes into account the local saliency and the local motion at each video location, coupled with a classification scheme that assigns depth to image patches. The system tolerates both scene motion and camera motion. In typical shots, containing hundreds of frames, even in the face of significant motion, it is enough to mark scribbles on the first and last frames of the shot. Once marked, plausible stereo results are obtained in a matter of seconds, leading to a scalable video conversion system. Finally, we validate our results with ground truth stereo video.

The seminal work of Hubel and Wiesel (1965) and the vast amount of work that followed it prove that hierarchies of increasingly complex cells play a central role in cortical computations. Computational models, pioneered by Fukushima (1980), suggest that these hierarchies contain feature-building cells ("S-cells") and pooling cells ("C-cells"). More recently, Riesenhuber & Poggio have developed the HMAX model (1999), in which S-cells perform linear combinations, while C-cells perform a MAX operation. We note that methods for computing the connectivity of S-cells abound since many algorithms for suggesting informative linear combinations exist. There are, however, only few published methods that are suitable for the construction of C-cells. Here, we build a novel dimensionality reduction algorithm for learning the connectivity of C-cells, using the framework of the max-plus ("tropical") semiring.

Video retargeting is the process of transforming an existing video to fit the dimensions of an arbitrary display. A compelling retargeting aims at preserving the viewers' experience by maintaining the information content of important regions in the frame, whilst keeping their aspect ratio. An efficient algorithm for video retargeting is introduced. It consists of two stages. First, the frame is analyzed to detect the importance of each region in the frame. Then, a transformation that respects the analysis shrinks less important regions more than important ones. Our analysis is fully automatic and based on local saliency, motion detection and object detectors. The performance of the proposed algorithm is demonstrated on a variety of video sequences, and compared to the state of the art in image retargeting.