2. Aim of the project Take your image Submit it to the search engine
Similar images are found
Name of the building appears

3. Landmark Locations

4. Recognition of Buildings Using SIFT (ROBUST)

5. Solution overview Image representation by SIFT keypoints
Sky and hedge detection
Non object features are removed
Similarity matching of features with database

6. Properties of SIFT features
Scale invariance
Rotation invariance
Robustness to illumination changes
Robustness to reasonable perspective transformation

7. SIFT Features

8. SIFT features computation
Keypoint detection (local minima and maxima)
Orientation of intensity gradients in the neighborhood
Detection of significant orientations
Histogram of orientations

9. Keypoint Refinement Sky detection (fast) Hedge detection (slower)
Thresholding, morphological operations
Applied to database and query images
Hedge detection (slower)
Hough transformation
Closing morphology operation
Applied to database images

10. Keypoints refinement

11. Similarity of features
Matching between keypoints
Take only reliable matches
Similarity: number of matched keypoints

12. Distance Matrix

13. Results SIFT Features outperformed PCA and Color Histogram Comparison
Leave-one-out Validation
First match correct: 95.5%
First or second match correct: 98.5%

14. Demo: Obscure Angle

15. Demo: Rotated, Reflected

16. Demo: People in Image

17. Demo: Night

18. Demo: Distant, Busy Scene

19. Demo: Team