Edge Detection in Image Processing
Edge detection is a cornerstone of computer vision. It finds boundaries in an image — places where brightness or colour changes sharply. These boundaries usually represent object outlines, shapes, or textures. Detecting edges transforms a photo into its most essential form: a sketch of its structure.
Before and After Example
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Why Edge Detection Matters
In everyday life, edge detection powers self-driving cars (detecting road lanes), medical imaging (highlighting tumors), robotics (object boundaries) and even art (turning photos into sketches). It is the first step for many advanced tasks like object recognition, motion tracking and segmentation.
How This Tool Works (Simple)
This tool uses the Canny Edge Detection algorithm. It works in stages:
- Smoothing: Remove noise with a Gaussian filter.
- Gradient calculation: Find where brightness changes most quickly.
- Non-maximum suppression: Thin out thick edges to keep them sharp.
- Double thresholding: Separate strong edges from weak edges.
- Edge tracking: Connect weak edges that touch strong edges, discard the rest.
Mathematical Explanation
At the heart of edge detection is the image gradient — how pixel intensity changes. For an image I(x, y), the gradients are:
These measure horizontal and vertical changes. The edge magnitude and direction are then:
Canny further applies Gaussian smoothing before computing gradients and thresholding after, making the edges clean, thin, and noise-resistant.
Tips for Best Results
- Low Threshold: Controls sensitivity. Lower values pick up faint lines (good for detailed sketches).
- High Threshold: Defines what counts as a "strong" edge. Higher values result in cleaner, simpler outlines.
- Source Image: High-contrast images with clear lighting work best.