ask underlying papers of MEAN SHIFT, OPTICAL FLOW, KALMAN FILTER

I need 3 underlying papers / most top tree in regard to MEAN S开发者_如何学PythonHIFT, OPTICAL FLOW, KALMAN FILTER. I've searched in ieee xplore, it showed many related papers.

Any idea?

Thanks in advance.

Do you know about CiteSeerX?

For Mean Shift I get Mean shift: A robust approach toward feature space analysis, which is a very good paper on that topic.

For the other topics I cannot help you, but you generally find good papers by reading papers and looking at the references.

These are old unsolved yet classic Computer Vision problems:

• Mean Shift Mean shift: A robust approach toward feature space analysis [same as bjoernz] but in practice, I would prefer a completely different unsupervised segmentation work from Felzenszwalb et al. Efficient graph-based segmentation (faster + better)

• Optical Flow

  • Sparse reliable points: Good Features to track is a nice summary of what is called the KLT literature (for Kanade-Lucas-Tomasi ... poor Jianbo Shi). In a nutshell, some points (corners) in your images are easier to track than others in uniform regions for example.
  • Dense for each pixel: Horn-Schunk historical paper but check out recent Thomas Brox and Jitendra Malik works and also the one Ce Liu also publish.

• Kalman filter: Historical Paper but I do not think it is still cited a lot because everybody seems to refer to their favorite textbooks instead.

For efficient implementations of almost all these nice articles: OpenCV at the rescue!

Caveat: Machine Learning people who are very trendy in Computer Vision these days are sometimes confused by the word features. Indeed, one can distinguish:

• Detectors: that selects sparse points in the image ( corners like e.g. Hessian, Harris ...)
• Descriptors: that describe these points and also the image through concatenation
• Features space: a fancy way to describe their kernel-SVM stuff for recognition

For example, SIFT is both a detector and a descriptor technique although it is called a feature.