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ICIP
2007
IEEE

ML Nonlinear Smoothing for Image Segmentation and its Relationship to the Mean Shift

13 years 11 months ago
ML Nonlinear Smoothing for Image Segmentation and its Relationship to the Mean Shift
This paper addresses the issues of nonlinear edge-preserving image smoothing and segmentation. A ML-based approach is proposed which uses an iterative algorithm to solve the problem. First, assumptions about segments are made by describing the joint probability distribution of pixel positions and colours within segments. Based on these assumptions, an optimal smoothing algorithm is derived under the ML condition. By studying the derived algorithm, we show that the solution is related to a two-stage mean shift which is separated in space and range. This novel ML-based approach takes a new kernel function. Experiments have been conducted on a range of images to smooth and segment them. Visual results and evaluations with 2 objective criteria have shown that the proposed method has led to improved results which suffer from less over-segmentation than the standard mean-shift.
Andy Backhouse, Irene Y. H. Gu, Tiesheng Wang
Added 08 Dec 2010
Updated 08 Dec 2010
Type Conference
Year 2007
Where ICIP
Authors Andy Backhouse, Irene Y. H. Gu, Tiesheng Wang
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