1. What I learned in the first 2 weeks
Wesna LaLanne

2. 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.

3. My own edge Detector!

4. Seagull Example
Finding the gradient Gradient + Threshold = Edge Detector

5. Other things I did with Mr. Seagull
Gradient Direction Laplacian

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

7. Pyramids

8. Pyramid Edges

9. 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.

10. 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
We find the points with large corner response, where R > threshold
Take only the points of local maxima R

11. Box Corner Detection
Original Corner Response R Where R > threshold

12. 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.

13. 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.

14. SIFT in action

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