Report 1: Optical Flow and Sift

Report 1: Optical Flow and Sift

Billy Timlen

(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

Lucas Kanade

Results

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

Optical Flow with Gaussian Pyramids

Code

Results

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)

Sift

Result

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

What Next?

Optimal Algorithms for Topologically Constrained Correspondence

Bayesian Formulation for Event Recounting given Event Label

3D Joint Localization for Gesture Recognition

GPS-Tag Refinement

Possible Projects