We propose a joint optical flow and principal component analysis (PCA) method for motion detection. PCA is used to analyze optical flows so that major optical flows corresponding to moving objects in a local window can be better extracted. This joint approach can efficiently detect moving objects and more successfully suppress small turbulence. It is particularly useful for motion detection from outdoor videos with low quality. It can also effectively delineate moving objects in both static and dynamic background. Experimental results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms.
In this post you will see 5 recipes of supervised classification algorithms applied to small standard datasets that are provided with the scikit-learn library.
hdr for video? The work presents a system for automatically producing a wide variety of video enhancements and visual effects. Unlike traditional visual effects software (e.g., After Effects, Shake, Boujou, etc), the system is completely automatic and no manual labor is required from the user. The major limitation of the work is that it can currently handle only videos of static scenes (i.e., videos shot with a moving camera but containing no moving objects in the scene). Efforts are being made to lift this restriction in future work. Applications of the system include: High resolution/definition video, High dynamic range video, Removing objects from a video, Creating painterly (NPR) videos, Video stabilization, Easy video editing Project website: grail.cs.washington.edu/projects/videoenhancement/videoEnhancement.htm
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962--964(March 2015)