Transcript Week 1-2 Review - UCF Center for Research in Computer Vision

WEEK 1-2 REVIEW
Kylie Gorman
CONVERTING AN
IMAGE FROM RGB TO
HSV AND DISPLAY
CHANNELS
Original
HSV Version
Hue
Saturation
Value
EDGE DETECTION
Sobel and Roberts
Sobel X
Sobel Y
Roberts X
Roberts Y
Canny Edge Detector
1. Smooth image with
Gaussian filter
2. Compute derivative
of filtered image
3. Find magnitude and
orientation of gradient
4. Apply “Nonmaximum Suppression”
5. Apply “Hysteresis
Threshold”
HARRIS CORNER
DETECTOR
Harris Corner Steps
• Compute x and y derivatives of image
• Compute products of derivatives at every pixel: Ix2
Ixy Iy2
• Compute the sums of the products of the derivatives
at each pixel
• Place each pixel into a matrix H
• Compute R = Det(H) – k(Trace(H))^2
• Threshold on value of R
My Own Implementation
Harris Corner Function in MATLAB
SCALE INVARIANT
FEATURE TRANSFORM
(SIFT)
SIFT Algorithm: Finding Keypoints
• Use Difference-of-Gaussian Function
• Good approximation of Laplacian of Gaussian, but faster to compute
• Construct Scale Space
• Key Point Localization
• Use Scale Space to Find Extrema
• Throw Out Poorly Defined Peaks
• Orientation Assignment
• Multiple Orientations Improves Stability of Matching
• Keypoint Descriptor
• Computed from Local Image Gradients
SIFT using Vl_feat
Using SIFT to Match Same Image
Different Images
SUPPORT VECTOR
MACHINES (SVM)
Linear SVM
Multi-Class SVM
OPTICAL FLOW
Optical Flow with Lucas-Kanade
• The Optical Flow Equation fxu + fyv = -ft has 2 unknown variables
• 3x3 window gives 9 equations with 2 unknown variables
• Put equations into matrix to get Au = ft
• To solve, multiply by the transpose of A:
• ATAu = ATft
• u = (ATA)-1AT ft
• Least Square Fit
• Solve for u and v
Lucas-Kanade with Images
Lucas-Kanade with Video
Original Clip:
http://www.youtube.com/watch?v=y6r8i_008SU
Lucas-Kanade with Vector Results
With Roberts Derivative
Resized Image
to ½ Original
Resized Image
to ¼ Original
With Sobel Derivative
Resized Image to
½ Original
Resized Image to
¼ Original
ADA BOOST
ADA Boost
• Expert is a pattern and a threshold
• Convolve an image with pattern and plot value on a number line
• Search for threshold
Face Detection
BAG OF WORDS/
FEATURES
Bag of Words/ Features
• Step One: Feature Extraction
• Extract Regions (SIFT, Harris)
• Compute Descriptors (SIFT)
• Step Two: Quantization
• Find Clusters and Frequencies (K-means)
• Step Three: Classification
• Compute Distance Matrix
• Classification (SVM)
PROJECT
POSSIBILITIES
Final Project
• Project: Color-Attributes-Related Image Retrieval
• Graduate Student: Yang Zhang
• Goal: Enable people to retrieve an image according to an object
with attributes or attributes alone. The project will focus on color as
the starting attribute.
• Program: MATLAB
Steps
• 1. Validating Model: Download other code and compare it to our
own code.
• 2. Coding: Add more features to the system the improve its
performance.
• 3. Collecting Dataset: There are not any existing color image
datasets on the Internet. Use automatic image collecting tool to
create our own color object dataset.
• 4. Possible Bonus: Implement novel ideas about general attribute
image retrieval system. Determine if it is effective or not.