Efficient non-parametric adaptive color modeling using fast Gauss transform
Elgammal, A.
Duraiswami, R.
Davis, L.S.
Comput. Vision Lab., Maryland Univ., College Park, MD, USA;
Abstract
Modeling the color distribution of a homogeneous region is used extensively for object tracking and recognition applications. The color distribution of an object represents a feature that is robust to partial occlusion, scaling and object deformation. A variety of parametric and non-parametric statistical techniques have been used to model color distributions. In this paper we present a non-parametric color modeling approach based on kernel density estimation as well as a computational framework for efficient density estimation. Theoretically, our approach is general since kernel density estimators can converge to any density shape with sufficient samples. Therefore, this approach is suitable to model the color distribution of regions with patterns and mixture of colors. Since kernel density estimation techniques are computationally expensive, the paper introduces the use of the fast Gauss transform for efficient computation of the color densities. We show that this approach can be used successfully for color-based segmentation of body parts as well as segmentation of many people under occlusion.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
