1. Report 1: Optical Flow and Sift
Billy Timlen

2. Lucas Kanade (u,v) = inv(AtA)*At*Ft
Derived from fx*u +fy*v = -ft (after taking the partial derivative in terms of each variable x,y,t
Analyze the pixels around the point of interest
Requires a degree of padding
Works for slow motion and small areas

3. Results

4. Optical Flow with Gaussian Pyramids
Reduces the original image into different levels
Impyramid(image, ‘reduce’)
Computes Optical flow for each level
Shifts derivative mask by u and v of prior level
Add the optical flows of each level
Should record more detailed results of motion

5. Code

6. Results

7. Sift Input: 18x18 patch, keypoint and orientation angle
Outputs a descriptor
Histogram of orientation magnitudes
Results vary according to the Gaussian used (for smoothing) and the sigma used (which affects the Gaussian)

8. Result

9. What Next? Work with different types of masks
Use different forms of interpolation
MatLab has their own function
Use another form of rounding the non- integer indices from u and v
Gonzalo sent us a bilinear function to look at

10. Possible Projects
Optimal Algorithms for Topologically Constrained Correspondence
Bayesian Formulation for Event Recounting given Event Label
3D Joint Localization for Gesture Recognition
GPS-Tag Refinement