Optimal selection of image segmentation algorithms based on performance prediction
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
Learning-based algorithm selection for image segmentation
Pattern Recognition Letters
EURASIP Journal on Applied Signal Processing
ROCOM'06 Proceedings of the 6th WSEAS international conference on Robotics, control and manufacturing technology
An object tracking scheme based on local density
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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A general systematic method for the detection and segmentation of bright targets is developed. We use the term “bright target” to mean a connected, cohesive object which has an average intensity distribution above that of the rest of the image. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayes classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). A performance analysis based on a Gaussian distribution model is used to show that the obtained adaptive threshold is often close to the Bayes threshold. The method has proven robust even when the image distribution is unknown. Examples are presented to demonstrate the efficiency of the technique on a variety of targets