Machine Learning
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
ACM SIGGRAPH 2005 Papers
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Depth Director: A System for Adding Depth to Movies
IEEE Computer Graphics and Applications
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Due to the advances in display technologies and the commercial success of 3D motion pictures in recent years, there is renewed interest in enabling consumers to create 3D content. While new 3D content can be created using more advanced capture devices (i.e., stereo cameras), most people still use 2D capture devices. Furthermore, enormously large collections of captured media exist only in 2D. We present a system for producing stereo images from captured 2D videos. Our system detects "good" stereo frames from a 2D video, which was captured a priori without any constraints on camera motion or content. We use a trained classifier to detect pairs of video frames that are suitable for constructing stereo images. In particular, for a given frame It at time t, we determine if t̂ exists such that It+t̂ and It can form an acceptable stereo image. We verify the performance of our method for producing stereo media from captured 2D videos in a psychovisual evaluation using both professional movie clips and amateur home videos. To the best of our knowledge, detecting good stereo pairs from a captured 2D video has been adequately addressed in the literature.