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Segmentation of bright targets using wavelets and adaptive thresholding.

X P Zhang¹, M D Desai

¹Department of Electrical and Computer Engineering, Ryerson Polytechnic University, Toronto, ON, M5B 2K3 Canada. xpzhang@ieee.org

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 6, 2008

Summary

This summary is machine-generated.

A new method accurately detects and segments bright targets in images using adaptive thresholds and multiresolution analysis. This robust technique works even with unknown image distributions, proving efficient across various targets.

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Area of Science:

Image analysis
Computer vision
Signal processing

Background:

Automated detection and segmentation of bright targets are crucial in various imaging applications.
Existing methods may struggle with unknown image distributions or require predefined parameters.
A need exists for a general, robust, and adaptive approach to bright target segmentation.

Purpose of the Study:

To develop a systematic and robust method for detecting and segmenting bright targets in images.
To introduce a novel approach utilizing multiresolution analysis and a Bayes classifier.
To create an adaptive thresholding technique based on image probability density functions.

Main Methods:

A novel multiresolution analysis combined with a Bayes classifier to identify potential target areas.
Adaptive thresholding using multiscale analysis of the image probability density function (PDF).
Performance analysis using a Gaussian distribution model to compare adaptive and Bayes thresholds.

Main Results:

The developed method effectively detects and segments bright targets.
Adaptive thresholds derived from the method closely approximate Bayes thresholds.
The technique demonstrates robustness across various image distributions, including unknown ones.
Efficiency is shown through examples on diverse target types.

Conclusions:

The proposed systematic method provides an efficient and robust solution for bright target detection and segmentation.
The adaptive thresholding approach offers reliable performance without prior knowledge of image distribution.
This technique has broad applicability in image analysis where bright object identification is necessary.