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Aim of the project

Aim of the project. Take your image Submit it to the search engine Similar images are found Name of the building appears. Landmark Locations. Recognition of Buildings Using SIFT (ROBUST). Solution overview. Image representation by SIFT keypoints Sky and hedge detection

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Aim of the project

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

  2. Landmark Locations

  3. Recognition of Buildings Using SIFT (ROBUST)

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

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

  6. SIFT Features

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

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

  9. Keypoints refinement

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

  11. Distance Matrix

  12. 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%

  13. Demo: Obscure Angle

  14. Demo: Rotated, Reflected

  15. Demo: People in Image

  16. Demo: Night

  17. Demo: Distant, Busy Scene

  18. Demo: Team

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