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A morphological gradient approach to color edge detection.

Adrian N Evans¹, Xin U Liu

¹Department of Electronic and Electrical Engineering, University of Bath, UK. a.n.evans@bath.ac.uk

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

|June 13, 2006

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A novel color edge detection method uses vector differences for efficient image analysis. This computationally efficient technique, enhanced with outlier rejection, outperforms existing methods in detecting edges in noisy images.

Area of Science:

Computer Vision
Image Processing
Digital Signal Processing

Background:

Edge detection is crucial for image analysis.
Existing color edge detectors face challenges with noise and computational efficiency.

Purpose of the Study:

To propose a new, computationally efficient color edge detector.
To enhance edge detection performance in noisy conditions.
To evaluate the proposed method against existing techniques.

Main Methods:

A novel color edge detection algorithm based on vector differences within a mask.
Implementation of a pairwise outlier rejection scheme to mitigate noise.
Quantitative evaluation using Pratt's figure of merit.

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Main Results:

The proposed method reduces to the classic morphological gradient for scalar images.
Outlier rejection significantly improves performance in noisy environments.
The new technique outperforms recent color edge detectors quantitatively.
Effective performance demonstrated on real-world images with low complexity.

Conclusions:

The proposed vector difference-based color edge detector is effective and computationally efficient.
The outlier rejection scheme enhances robustness against noise.
This method offers a promising alternative for color edge detection applications.