Kylie Gorman WEEK 1-2 REVIEW
Jan 29, 2016
Kylie Gorman
WEEK 1-2 REVIEW
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 filter2. Compute derivative of filtered image3. Find magnitude and orientation of gradient4. Apply “Non-maximum 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.