Edge Detection
After smoothing and blurring, we now have cleaner images with reduced noise.
The next major question in Computer Vision is: Where are the object boundaries?
Edge detection answers this question. It helps the computer identify where one object ends and another begins.
What Is an Edge?
An edge is a location in an image where there is a sharp change in intensity.
In simple terms:
- Dark → Light transition
- Light → Dark transition
These transitions usually correspond to object boundaries, shapes, or structural details.
Why Edge Detection Is So Important
Edges carry most of the meaningful information in an image.
- Shapes are defined by edges
- Objects are separated by edges
- Contours are built from edges
Many advanced tasks depend on good edge detection:
- Object detection
- Image segmentation
- Contour extraction
- Shape analysis
Why We Smoothed Before Edge Detection
Edge detectors are extremely sensitive.
They respond to any sudden change — including noise.
If we skip smoothing:
- Noise appears as false edges
- Edges become broken or scattered
- Results become unreliable
This is why smoothing (Lesson 9) is almost always applied before edge detection.
How Edge Detection Works (Core Idea)
Edge detection works by measuring intensity differences between neighboring pixels.
If the difference is large, an edge exists.
If the difference is small, the region is likely flat.
Gradients: The Heart of Edge Detection
A gradient measures how quickly pixel values change.
In images, gradients are computed in two directions:
- Horizontal (X direction)
- Vertical (Y direction)
Strong gradients indicate edges.
Horizontal vs Vertical Edges
Different edges respond to different directions:
- Horizontal edges → intensity changes vertically
- Vertical edges → intensity changes horizontally
By combining both directions, we detect edges of all orientations.
Basic Edge Detection Operators
Several operators exist to compute gradients. Each has different characteristics.
1. Roberts Operator
One of the earliest edge detectors.
- Uses very small kernels
- Fast but sensitive to noise
- Rarely used in modern systems
2. Prewitt Operator
Improves over Roberts by using larger kernels.
- Detects horizontal and vertical edges
- Moderate noise sensitivity
- Simple and intuitive
3. Sobel Operator
Sobel is one of the most widely taught edge detectors.
- Uses weighted kernels
- Emphasizes central pixels
- More robust to noise than Prewitt
Sobel is often used as an educational foundation for understanding gradients.
Comparison of Edge Operators
| Operator | Noise Resistance | Accuracy | Common Use |
|---|---|---|---|
| Roberts | Low | Low | Historical learning |
| Prewitt | Medium | Medium | Basic edge detection |
| Sobel | High | High | Practical pipelines |
What Edge Detection Produces
The output of edge detection is:
- A binary or grayscale image
- Edges appear bright
- Flat regions appear dark
This output is not the final goal — it is an intermediate step.
Edge Detection vs Thresholding
These two techniques serve different purposes.
- Thresholding: separates regions
- Edge detection: finds boundaries
Both are often used together in pipelines.
Where Edge Detection Is Used
- Document scanning
- Lane detection in autonomous driving
- Medical image analysis
- Industrial inspection
- Object shape recognition
Common Mistakes to Avoid
- Skipping smoothing before edge detection
- Using weak operators on noisy images
- Expecting edges to directly represent objects
Edges are building blocks — not final answers.
Practice Questions
Q1. What defines an edge in an image?
Q2. Why is smoothing applied before edge detection?
Q3. Which operator is most robust among basic edge detectors?
Quick Quiz
Q1. What do gradients measure?
Q2. Are edges final objects?
Key Takeaways
- Edges represent intensity transitions
- They define shapes and boundaries
- Smoothing improves edge quality
- Gradients are the foundation of edge detection
- Edge detection is a critical preprocessing step
In the next lesson, we will use edges to extract Contours — turning boundaries into meaningful shapes.