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Published byFrieda Bergmann Modified over 6 years ago
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Aim of the project Take your image Submit it to the search engine
Similar images are found Name of the building appears
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Landmark Locations
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Recognition of Buildings Using SIFT (ROBUST)
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Solution overview Image representation by SIFT keypoints
Sky and hedge detection Non object features are removed Similarity matching of features with database
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Properties of SIFT features
Scale invariance Rotation invariance Robustness to illumination changes Robustness to reasonable perspective transformation
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SIFT Features
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SIFT features computation
Keypoint detection (local minima and maxima) Orientation of intensity gradients in the neighborhood Detection of significant orientations Histogram of orientations
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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
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Keypoints refinement
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Similarity of features
Matching between keypoints Take only reliable matches Similarity: number of matched keypoints
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Distance Matrix
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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%
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Demo: Obscure Angle
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Demo: Rotated, Reflected
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Demo: People in Image
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Demo: Night
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Demo: Distant, Busy Scene
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Demo: Team
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