Morphological operations (erosion, dilation, opening, closing) in Computer Vision - Model Pipeline Trace

Model Pipeline - Morphological operations (erosion, dilation, opening, closing)

This pipeline shows how morphological operations change images to highlight or remove small details. These operations help clean images or find shapes by shrinking or growing bright areas.

Data Flow - 5 Stages

1Input Image

1 image x 256 x 256 pixels (grayscale)→Load grayscale image with bright and dark areas→1 image x 256 x 256 pixels

A black and white photo of a leaf with noise spots

↓

2Erosion

1 image x 256 x 256 pixels→Shrink bright areas by sliding a small square kernel and keeping minimum pixel values→1 image x 256 x 256 pixels

Leaf edges become thinner, small white noise spots disappear

↓

3Dilation

1 image x 256 x 256 pixels→Grow bright areas by sliding a small square kernel and keeping maximum pixel values→1 image x 256 x 256 pixels

Leaf edges become thicker, small holes fill in

↓

4Opening

1 image x 256 x 256 pixels→Erosion followed by dilation to remove small bright noise→1 image x 256 x 256 pixels

Leaf image with small white noise removed but shape preserved

↓

5Closing

1 image x 256 x 256 pixels→Dilation followed by erosion to fill small dark holes→1 image x 256 x 256 pixels

Leaf image with small black holes filled in

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |*** 
0.3 |**  
0.2 |*   
0.1 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.60	Initial morphological filter parameters applied, moderate noise removal
2	0.30	0.75	Kernel size adjusted, better noise removal and shape preservation
3	0.20	0.85	Optimal kernel size found, clear edges and minimal noise
4	0.18	0.88	Fine tuning morphological operations, slight improvement
5	0.15	0.90	Stable performance, good balance of noise removal and detail

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Erosion

Layer 3: Dilation

Layer 4: Opening (Erosion + Dilation)

Layer 5: Closing (Dilation + Erosion)

Model Quiz - 3 Questions

What does erosion do to bright areas in an image?

A Fills small dark holes inside bright areas

B Shrinks bright areas by removing pixels at edges

C Grows bright areas by adding pixels at edges

D Removes small dark noise spots

Key Insight

Morphological operations like erosion and dilation help clean images by shrinking or growing bright areas. Combining them as opening or closing removes noise or fills holes while preserving important shapes. Adjusting kernel size improves results, shown by decreasing loss and increasing accuracy during training.