Morphological Operations in Computer Vision
Morphological operations are powerful image-processing techniques that focus on the shape and structure of objects in an image. They are especially useful for cleaning, refining, and improving binary images.
If contours help us understand shapes, morphological operations help us fix and prepare those shapes.
Why Do We Need Morphological Operations?
Real-world images are rarely perfect. After thresholding or edge detection, images often contain:
- Small noise dots
- Broken edges
- Gaps inside objects
- Thin unwanted lines
Morphological operations help us:
- Remove noise
- Fill gaps
- Strengthen object boundaries
- Simplify shapes for better analysis
Key Idea Behind Morphology
Morphological operations work on binary images and are based on two things:
- The image
- A small shape called a structuring element
Think of the structuring element as a tiny probe that moves over the image and modifies pixels based on shape.
What Is a Structuring Element?
A structuring element is a small matrix (kernel) that defines how the image is examined.
Common shapes include:
- Square
- Rectangle
- Circle
- Cross
The size and shape of this element directly affect the result.
Basic Morphological Operations
There are two fundamental operations from which everything else is built:
- Erosion
- Dilation
Erosion – Shrinking Objects
Erosion removes pixels from object boundaries. It makes objects thinner and smaller.
Conceptually:
- Foreground pixels near edges are removed
- Small noise disappears
- Thin connections break
Erosion is useful when you want to:
- Remove small white noise
- Separate connected objects
Dilation – Growing Objects
Dilation adds pixels to object boundaries. It expands shapes and fills small gaps.
Conceptually:
- Edges grow outward
- Small holes are filled
- Broken parts reconnect
Dilation is useful when:
- Objects are broken
- Edges are too thin
Erosion vs Dilation (Clear Comparison)
| Operation | Effect | Primary Use |
|---|---|---|
| Erosion | Shrinks objects | Remove noise, detach objects |
| Dilation | Expands objects | Fill gaps, strengthen edges |
Opening – Clean Small Noise
Opening is erosion followed by dilation.
Why this order?
- Erosion removes small noise
- Dilation restores object size
Opening is commonly used to:
- Remove small dots
- Smooth object outlines
Closing – Fill Small Holes
Closing is dilation followed by erosion.
Why this order?
- Dilation fills gaps
- Erosion restores shape
Closing is ideal for:
- Filling small holes
- Connecting broken parts
Opening vs Closing
| Operation | Sequence | Best For |
|---|---|---|
| Opening | Erosion → Dilation | Noise removal |
| Closing | Dilation → Erosion | Gap filling |
Why Morphology Is Critical Before Contours
Contour detection works best when shapes are:
- Clean
- Connected
- Noise-free
Morphological operations are often applied right before contour detection to improve accuracy.
Real-World Applications
- Document scanning (clean text)
- Medical image preprocessing
- License plate detection
- Object counting systems
- Industrial defect inspection
Common Beginner Mistakes
- Using morphology on color images
- Choosing wrong kernel size
- Over-eroding objects
- Applying operations blindly
Always inspect results visually.
Practice Questions
Q1. What is the main purpose of erosion?
Q2. Which operation is best for filling small holes?
Homework / Practice Task
- Take a noisy binary image
- Apply opening to remove noise
- Apply closing to fill gaps
- Compare results visually
Use Google Colab or your local OpenCV setup. Focus on understanding results, not speed.
Quick Recap
- Morphology focuses on shape
- Erosion shrinks objects
- Dilation grows objects
- Opening removes noise
- Closing fills holes
In the next lesson, we will move into OpenCV itself and start hands-on image processing.