PRESENTATION REU IN COMPUTER VISION

2014AMARI LEWIS

CRCV

UNIVERSITY OF CENTRAL FLORIDA

IMPLEMENTING DIFFERENT WAYS TO IMPROVE PICTURES…

OriginalThe top image combines

the different channels and uses convolution

F *h= Σ Σ f(k,l)h(-k,-l)

F= imageH=kernel

COMBINE CHANNELS

GAUSSIAN

Type of smoothing, a weighted average of the surrounding pixels

using this formula:

The sigma value determines the amount of

‘blurr’ the image will display.

Gaussian smoothing

Original

‘LAPLACIAN’

Finds the 2nd Derivative of Gaussian

HISTOGRAM – USED TO REPRESENT EACH COLOR IN THE IMAGE

OBSERVE BELOW

EDGE DETECTION-

Roberts

Roberts: finds edges using the Roberts approximation to the derivative. It returns edges at those points where the gradient of I is maximum.

Canny

Uses two thresholds to determine between weak and strong edges

Canny

Roberts

EDGE DETECTION WITH THRESHOLD

Sobel X: [1 0 -1, 2 0 -2, 1 0 -1]Y: [1 2 1, 0 0 0, -1 -2 -1]Calculates: √(d/x)²+(d/dy)²

PYRAMIDS

ADABOOST – FACE DETECTIONBoosting defines a classifier using an additive

modelF(x) = ∂1f1(x) +∂2f2(x)+∂3f3(x)….

F:strong classifierX- feature vectorsSigma= weight

f – weak classifiers

TRIAL 2

SVM • SVM (Support Vector Machine) classifier is able to test trained data to analyze and divide results. (object ore non—object)

• This is an example of linear classification

• Linearsvm calculates : f(x) = w^Tx+b

• where w is the normal line or weight vector and b is the bias

RESIZING MULTIPLE IMAGES THROUGH FOR LOOPS..

LUCAS KANADE (LEAST OF SQUARES)

• Optical flow equation-

• Considers a 3x3 window

Lucas Kanade

OPTICAL FLOW

LUCAS KANADE

WITH PYRAMIDS

CLUSTERING, BAG OF FEATURES

THE PROJECT I’M INTERESTED IN WORKING ON

• THE APPLICATIONS OF LIGHT FIELDS IN COMPUTER VISION

AIDEAN SHARGHI

THANK YOU !!

• I APPRECIATE THE OPPORTUNITY ONCE AGAIN AND I AM LEARNING A LOT FROM THIS EXPERIENCE

THANKS,

OLIVER NINA

DR. LOBO

DR. SHAH