What I learned in the first 2 weeks Wesna LaLanne

Edge Detector Find the gradient Get a image in gray scale. Get the derivative of the kernel in the x and y direction Convolve the derivatives of the kernel in the x and y direction with the picture Take both convulsions, square them, and add them. Then take the square root of all that Boom. Gradient. Pick an appropriate threshold that will show the right amount of details (not too much) so you can can get an accurate representation of the edges.

My own edge Detector!

Seagull Example Finding the gradient Gradient + Threshold = Edge Detector

Other things I did with Mr. Seagull Gradient Direction Laplacian

Other things I did with Mr. Seagull Original Picture - Gradient Picture =

Pyramids

Pyramid Edges

Harris Corner Detection We use corners because they’re easily identifiable when you look at an image through a small window When using corners, shifting said window in any direction, you would see a large change in intensity.

Harris Corner Detection - What’s it doing? Goes through every pixel in the picture to Calculate ‘R’ which is the the measure of corner response. R = detM - k(traceM)^2, where M is a 2x2 matrix computed from image derivatives and k is an empirical constant between 0.04-0.06 We find the points with large corner response, where R > threshold Take only the points of local maxima R

Box Corner Detection Original Corner Response R Where R > threshold

Lucas-Kanade (Optical Flow) Optical flow is a method that is used for estimating the motion of objects across a series of consecutive frames. Optical flow has two components: normal flow and parallel flow. Normal flow can be computed directly, but Parallel Flow can’t. Lucas-Kanade is one of several method used to solve the parallel flow issue.

SIFT/SVM Sift - is an algorithm in computer vision that detects local features in the images SVM (Support Vector Machine) - a learning algorithm that analyzes the data from the sift algorithm and recognized patterns.

SIFT in action

Bag of Words The algorithm will treat certain features as words and will group the “words” together.