ML Nonlinear Smoothing for Image Segmentation and Its Relationship to The Mean Shift
Paper in proceeding, 2007

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.

spatial-range mean shift

image segmentation

ML nonlinear image smoothing


Andrew Backhouse

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Irene Yu-Hua Gu

Department of Applied Electronics, Signal Processing

IEEE International conf. on Image Processing (ICIP '07)

Subject Categories

Computer Engineering

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

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