1. Why Local Invariant Features?
Local invariant features describe small image regions that remain recognizable under transformations.
Local features are more robust than global image representations.
🧠Match small regions instead of entire images.
2. Motivation
Image matching becomes difficult when images differ in scale, rotation, viewpoint or lighting.
Local features improve robustness against these changes.
3. General Matching Pipeline
- Detect keypoints
- Define local regions
- Extract descriptors
- Match descriptors
🧠Detect → Describe → Match
4. Main Components of Local Features
| Stage |
Purpose |
| Detection |
Find interest points |
| Description |
Create feature vectors |
| Matching |
Find correspondences |
5. Requirement: Repeatability
The same keypoints should be detected independently in different images.
Repeatable detectors are necessary for successful matching.
6. Requirement: Distinctiveness
Each feature should have a unique description.
Distinctive descriptors reduce false matches.
7. Desired Properties
- Repeatability
- Distinctiveness
- Compactness
- Efficiency
- Locality
Exam Keyword:
Invariance
8. Invariance
Features should remain stable under image transformations.
Types:
- Translation
- Rotation
- Scale
- Illumination
9. Interest Point Detection
Detection identifies important image locations suitable for matching.
Corners are among the most useful interest points.
10. Why Corners?
Corners contain strong intensity changes in multiple directions.
Corners are repeatable and distinctive.
11. Good Corner Characteristics
- Easy to recognize locally
- Large intensity change when shifted
- Well localized
12. Harris Corner Detector
Harris detects corners using image gradient information.
Uses a 2×2 matrix of image derivatives.
Important Method:
Harris Corner Detector
13. Harris Corner Principle
A corner exists when intensity changes significantly in two directions.
🧠Large changes in X and Y → Corner
14. Eigenvalues Interpretation
| Eigenvalues |
Meaning |
| Both Small |
Flat Region |
| One Large |
Edge |
| Both Large |
Corner |
15. Harris Detection Steps
- Compute corner response
- Apply threshold
- Perform non-maximum suppression
🧠Response → Threshold → Local Maxima
16. Harris Properties
| Property |
Supported? |
| Translation Invariant |
Yes |
| Rotation Invariant |
Yes |
| Scale Invariant |
No |
Exam Favourite:
Harris is NOT scale invariant.
17. Need for Scale Invariance
The same object may appear at different sizes in different images.
Interest points must be detectable across scales.
18. Scale-Space Concept
Features are searched across multiple image scales.
🧠Search in position AND scale.
19. Blob Detection
Blob detectors locate regions with strong contrast differences.
Method:
Laplacian of Gaussian (LoG)
20. Characteristic Scale
The best scale is the one producing the maximum Laplacian response.
Interest points are local maxima in both position and scale.
21. Difference of Gaussians (DoG)
DoG approximates the Laplacian of Gaussian efficiently.
🧠DoG ≈ LoG but faster.
22. Feature Description
A descriptor summarizes the appearance around a keypoint.
Descriptors are used for matching.
23. Raw Patch Descriptors
A simple descriptor uses pixel intensities directly.
Highly sensitive to rotation and translation.
24. SIFT
Scale Invariant Feature Transform (SIFT) is a robust local descriptor.
Important Method:
SIFT
25. SIFT Procedure
- Detect keypoints using DoG
- Find dominant orientation
- Rotate patch to canonical orientation
- Normalize scale
- Create descriptor
🧠Detect → Orient → Normalize → Describe
26. Dominant Orientation
SIFT computes a histogram of gradient directions.
Orientation with highest vote becomes the dominant direction.
27. SIFT Descriptor Structure
4 × 4 histogram grid
8 orientation bins per grid
Descriptor Length:
4 × 4 × 8 = 128 values
Exam Favourite:
SIFT Descriptor = 128 Dimensions
28. Properties of SIFT
- Scale invariant
- Rotation invariant
- Partially illumination invariant
- Robust to viewpoint changes
🧠One of the most successful feature descriptors.
29. Applications of Local Features
- Stereo matching
- Motion tracking
- Panorama stitching
- Robot navigation
- 3D reconstruction
- Object recognition
30. Final Exam Summary
Most Important Points
- Local Features: Detect, describe and match image regions.
- Requirements: Repeatability and distinctiveness.
- Invariance: Translation, rotation, scale and illumination.
- Corners: Strong intensity changes in multiple directions.
- Harris: Corner detector using image gradients.
- Harris Properties: Translation & rotation invariant but NOT scale invariant.
- Scale-Space: Detect features across multiple scales.
- LoG: Blob detector.
- DoG: Efficient approximation of LoG.
- SIFT: Scale Invariant Feature Transform.
- Dominant Orientation: Provides rotation invariance.
- SIFT Descriptor: 4×4×8 = 128 dimensions.
- Applications: Recognition, tracking, mosaicing, 3D reconstruction.