Report 1: Optical Flow and Sift Billy Timlen

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

Results

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

Code

Results

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)

Result

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

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