1. WEEK 1-2 ALEJANDRO TORROELLA

2. CONVERTING AN IMAGE FROM RGB TO HSV AND DISPLAYING THE SEPARATE CHANNELS

3. Hue: Value: Saturation:

4. ROBERTS AND SOBEL EDGE DETECTORS ON BOTH RGB AND HSV CHANNELS

5. LAPLACIAN OF GAUSSIAN AND SEPARABILITY

6. Sigma = 1.2, Kernel Size = 9x9

7. CANNY EDGE DETECTOR

8. Sigma = 1.2, Kernel Size = 9x9, Threshold = 0.05

9. IMPROVING LEGIBILITY

11. HARRIS CORNER DETECTOR

13. SIFT DENSE SAMPLING SCHEME

15. SVM TRAINING AND TESTING

16. Used two different kernels: Chi Squared and Histogram Intersections Histogram Intersection gave better results than Chi Squared. Results were still poor, but that’s probably because of low density SIFT descriptors and the way they were implemented. Increasing the density of the SIFT descriptors significantly improved the accuracy of classification. Accuracy_768 = [ histo, chi2 ] = [ 11.1233, 7.9295 ] Accuracy_3072 = [ histo, chi2 ] = [ 24.5595, 8.8106 ]

17. OPTICAL FLOW

18. =

19. THANK YOU FIN.