Edge Detection in Color Images

Edge detection in color images represents a crucial task in computer vision, requiring additional considerations for color channel processing compared to grayscale images. The Canny operator, as a classical algorithm, achieves high-precision edge extraction through four sequential steps: Gaussian filtering for noise reduction, gradient computation, non-maximum suppression, and dual-threshold detection.

In color image processing, two primary implementation approaches exist: first, performing edge detection separately on each RGB channel and then merging the results; second, converting the color image to grayscale before processing. The former preserves more color boundary information but involves higher computational complexity, while the latter offers greater efficiency but may miss edges caused by color differences.

The selection of Gaussian window parameters (σ value) directly impacts detection performance: larger σ values smooth more noise but blur edges, whereas smaller σ values maintain finer edge structures but show higher noise sensitivity. In practical implementations, experimentation typically leads to selecting 3×3 or 5×5 window sizes with σ values between 1.0-1.5 as standard parameters.

Toolkits like OpenCV provide built-in interfaces for color edge detection, employing channel-merging strategies by default. Notably, for specialized applications such as medical imaging or satellite imagery, color space conversion to HSV/Lab before detection may be necessary to enhance edges associated with specific color characteristics.