We present two solutions for the scale selection problem in computer vision. The first one is completely nonparametric and is based on the the adaptive estimation of the normalized density gradient. Employing the sample point estimator, we define the Variable Bandwidth Mean Shift, prove its convergence, and show its superiority over the fixed bandwidth procedure. The second technique has a semiparametric nature and imposes a local structure on the data to extract reliable scale information. The local scale of the underlying density is taken as the bandwidth which maximizes the magnitude of the normalized mean shift vector. Both estimators provide practical tools for autonomous image and quasi real-time video analysis and several examples are shown to illustrate their effectiveness.
The variable bandwidth mean shift and data-driven scale selection
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 1 ; 438-445 vol.1
01.01.2001
886671 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
The Variable Bandwidth Mean Shift and Data-Driven Scale Selection
British Library Conference Proceedings | 2001
|Mean-Shift Segmentation with Wavelet-based Bandwidth Selection
British Library Conference Proceedings | 2002
|Designing Transfer Function Based on Adaptive Bandwidth Mean Shift Clustering Algorithm
British Library Online Contents | 2007
|Mean-Shift Blob Tracking through Scale Space
British Library Conference Proceedings | 2003
|