Transcript crcv.ucf.edu

What I learned in the first
2 weeks
Wesna LaLanne
Edge Detector
1. Find the gradient
a. Get a image in gray scale.
b. Get the derivative of the kernel in the x and y direction
c. Convolve the derivatives of the kernel in the x and y direction
with the picture
d. Take both convulsions, square them, and add them. Then
take the square root of all that
e. Boom. Gradient.
2. 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
Detector
Gradient + Threshold = Edge
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?
1. Goes through every pixel in the picture to Calculate
‘R’ which is the the measure of corner response.
a. 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
2. We find the points with large corner response, where
R > threshold
3. Take only the points of local maxima R
Box Corner Detection
Original
Where R > threshold
Corner Response R
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.