Using vision, acoustics, and natural language for disambiguation
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Learning Pairwise Dissimilarity Profiles for Appearance Recognition in Visual Surveillance
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Simultaneous appearance modeling and segmentation for matching people under occlusion
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Overlapped text segmentation using Markov random field and aggregation
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Arbitrary body segmentation in static images
Pattern Recognition
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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.