Mean Shift: from Theory to Application

نویسنده

  • Kazunori Okada
چکیده

Mean shift is a popular robust framework for statistical data analysis using kernel density estimation, originally proposed by Fukunaga and Hostetler in 70’s. Recently, due to the work by Cheng and Comaniciu, this method has been re-discovered and successfully applied to a wide range of vision applications. This article provides a comprehensive overview of the basic theory and applications of mean shift, highlighting its practical and theoretical advantages, recent theoretical extensions, as well as vision applications such as image segmentation and object tracking.

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تاریخ انتشار 2008