TY - CONF
T1 - Iterative figure-ground discrimination
T2 - Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Y1 - 2004
A1 - Zhao, L.
A1 - Davis, Larry S.
KW - algorithm;
KW - analysis;
KW - Bandwidth
KW - calculation;
KW - Color
KW - colour
KW - Computer
KW - density
KW - dimensional
KW - discrimination;
KW - distribution;
KW - distributions;
KW - Estimation
KW - estimation;
KW - expectation
KW - figure
KW - Gaussian
KW - ground
KW - image
KW - initialization;
KW - iterative
KW - Kernel
KW - low
KW - methods;
KW - mixture;
KW - model
KW - model;
KW - nonparametric
KW - parameter
KW - parametric
KW - processes;
KW - sampling
KW - sampling;
KW - segmentation
KW - segmentation;
KW - statistics;
KW - theory;
KW - vision;
AB - Figure-ground discrimination is an important problem in computer vision. Previous work usually assumes that the color distribution of the figure can be described by a low dimensional parametric model such as a mixture of Gaussians. However, such approach has difficulty selecting the number of mixture components and is sensitive to the initialization of the model parameters. In this paper, we employ non-parametric kernel estimation for color distributions of both the figure and background. We derive an iterative sampling-expectation (SE) algorithm for estimating the color, distribution and segmentation. There are several advantages of kernel-density estimation. First, it enables automatic selection of weights of different cues based on the bandwidth calculation from the image itself. Second, it does not require model parameter initialization and estimation. The experimental results on images of cluttered scenes demonstrate the effectiveness of the proposed algorithm.
JA - Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
VL - 1
M3 - 10.1109/ICPR.2004.1334006
ER -