Statistical models of pixel value variations have been developed and analyzed. Some of the work focuses on kernel density estimators to develop such models. Consequently, statistical theory of density estimators can be used for various tasks including segmentation of locally/globally parametric image signals; scale estimation and object registration. The main projects of this sub-theme are “Bandwidth Selection for Kernel Density Estimators” and “Estimation and Segmentation of Images Using Parametric Image Models” detailed below.
Bandwidth Selection for Kernel Density Estimators
A regression-based model which admits a realistic framework for automatically choosing bandwidth parameters which minimizes a global error criterion. This is used for automatic segmentation of images at any input resolution scale (for e.g., the wavelet decomposition scale).
- M. Singh and N. Ahuja, Regression based Bandwidth Selection for Segmentation using Parzen Windows, in Ninth IEEE International Conference in Computer Vision, Proceedings, vol. 1, pp. 2-9, Oct. 2003, Nice, France.
- M. Singh and N. Ahuja, Mean-Shift Segmentation with Wavelet-based Bandwidth Selection, IEEE Workshop on Applications in Computer Vision, pp. 43-50, Dec. 3-4, 2002, Florida.