Computer Vision Interview 20 essential Q&A Updated 2026
morphology

Morphological Operations: 20 Essential Q&A

Set-based operations on binary and grayscale images—building blocks for cleanup and shape analysis.

~11 min read 20 questions Intermediate
erosiondilationopeningSE
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1. What is morphology?
2. Erosion
3. Dilation
4. Opening
5. Closing
6. Structuring element
7. Disk vs rectangle SE
8. Duality erosion/dilation
9. Boundary extraction
10. Morphological gradient
11. Top-hat / black-hat
12. Hit-or-miss
13. Grayscale morphology
14. Composition order
15. Text / stroke cleanup
16. SE size effect
17. Hole filling
18. Skeletonization (idea)
19. OpenCV morphologyEx
20. Morphology vs convolution
1 What is mathematical morphology? ⚡ easy
Answer: Non-linear image ops based on set theory with a structuring element—shape analysis, denoising binary masks, and extracting boundaries.
2 Define erosion (binary). 📊 medium
Answer: SE fits inside foreground: output pixel on only if SE fully contained in foreground—shrinks objects, removes thin protrusions, separates touching objects if SE sized right.
3 Define dilation (binary). 📊 medium
Answer: SE hits foreground: output on if SE overlaps foreground—grows objects, fills small holes and gaps, reconnects broken strokes.
4 What is opening? 📊 medium
Answer: Erosion then dilation with same SE—removes small bright noise (pepper) and smooths boundaries without changing coarse size as much as raw erosion.
5 What is closing? 📊 medium
Answer: Dilation then erosion—fills small dark holes (salt holes in foreground), connects nearby components, smooths inward concavities.
6 What is a structuring element (SE)? ⚡ easy
Answer: Small binary mask defining neighborhood shape—disk is isotropic; rectangle aligns to axes; size controls scale of effect.
7 Disk vs square SE? ⚡ easy
Answer: Disk gives isotropic rounding; axis-aligned square can preserve Manhattan geometry—choice affects anisotropy of shrink/grow.
8 State duality between erosion and dilation. 🔥 hard
Answer: Erosion of foreground equals dilation of background complement (with reflected SE)—lets derive properties and implement efficiently.
9 Extract boundary with morphology? 📊 medium
Answer: Boundary ≈ original − eroded (or XOR variants)—gives outer contour ring depending on SE.
10 What is morphological gradient? 📊 medium
Answer: Dilation − erosion—edge strength similar to gradient magnitude on binary shapes; outer/inner gradients are variants.
11 What is white top-hat? 🔥 hard
Answer: Image − opening—extracts bright small details larger than SE removed by opening; black top-hat is closing − image for dark details.
12 What is hit-or-miss? 🔥 hard
Answer: Template matching for specific pixel configurations—detects corners, endpoints, pruned skeletons using paired SE foreground/background patterns.
13 Grayscale erosion/dilation? 📊 medium
Answer: Min/max filter over SE neighborhood—useful for texture and background estimation; different from binary set interpretation but analogous.
14 Does order of opening and closing commute? ⚡ easy
Answer: No—generally opening∘closing ≠ closing∘opening. Order matters for pipeline design.
15 Clean scanned text binary mask? 📊 medium
Answer: Opening removes speckle; closing fills letter breaks—tune SE smaller than stroke width to avoid destroying characters.
16 Larger SE effect? ⚡ easy
Answer: Removes/fills larger features; too large destroys valid structure—scale SE to minimum noise size you want removed.
17 Fill holes in binary objects? 📊 medium
Answer: Geodesic reconstruction, conditional dilation from border, or flood-fill tricks—classic interview “beyond closing” answer.
18 What is skeletonization? 📊 medium
Answer: Reduce foreground to 1-pixel-wide medial axis preserving topology—sequential morphological thinning or distance-transform methods.
19 OpenCV morphologyEx types? ⚡ easy
Answer: MORPH_OPEN, CLOSE, GRADIENT, TOPHAT, BLACKHAT, HITMISS—single API with MORPH_ERODE/DILATE base. 
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
clean = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
20 Morphology vs linear convolution? 📊 medium
Answer: Morphology is min/max (nonlinear); convolution is weighted sum. Morphology preserves sharp extrema semantics for shapes.

Morphology Cheat Sheet

Core
  • Erode / dilate
  • Open / close
SE
  • Shape + size
  • Reflect for theory
Extras
  • Gradient / tophat
  • Hit-miss

💡 Pro tip: Opening removes pepper; closing fills holes—memorize order.

Full tutorial track

Go deeper with the matching tutorial chapter and code examples.

Morphology Tutorial
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