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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Roadmap Introduction to object detection What kind of object do we want to detect? Circle/ellipse detection Least-Square fitting based detector Line detection Hough transform-based detector Corner detection Harris corner detector Face/pedestrian/vehicle detection* EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Beyond Edge Detection Edges are among the earliest primitives that have been studied in computer vision, but their definition remains fuzzy There are many other primitives that can be more rigorously defined or at least conceptually easier to define Corner, line, circle, ellipse, square, triangle, … The ultimate vision tasks involve the detection of general objects Face, pedestrian, vehicle, mouth, eyes, door EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Two Paradigms Model-based approaches Build an explicit model to characterize the objects we want to detect Suitable for the class of simple objects for which good models are relatively easy to find Examples: corner/line/circle detection Data-driven (machine learning) approaches Provide a training set (e.g., manually detected results) and detector works like a black box Suitable for the class of complex objects for which good models are NOT easy to find Examples: neural network, support vector machine EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Roadmap Introduction to object detection What kind of object do we want to detect? Circle/ellipse detection Least-Square fitting based detector Line detection Hough transform-based detector Corner detection Harris corner detector Face/pedestrian/vehicle detection* EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Recall: Edge Detection in Iris Image
>I=imread(‘iris.bmp’); >c=edge(I,’canny’); >[x,y]=find(c==1); EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Line/Polynomial Fitting
x x y=ax+b y=ax2+bx+c MATLAB function: P = POLYFIT(X,Y,N) Polynomial of degree N EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Circle/Ellipse Fitting
b r a (xo,yo) (xo,yo) Min E=(x-xo)2+(y-y0)2-r2 Min E=(x-xo)2/a2+(y-y0)2/b2-1 MATLAB function: circle_detect.m MATLAB function: fitellipse.m EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Image Examples EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Roadmap Introduction to object detection What kind of object do we want to detect? Circle/ellipse detection Least-Square fitting based detector Line detection Hough transform-based detector Corner detection Harris corner detector Face/pedestrian/vehicle detection* EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Basic Idea Behind y ? x Question: Can we find a transform that maps a line to a point? EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Radon/Hough Transform: Mathematics*
min max min Sample of two lines (x1y1) max Two of an infinite number of lines through point (x1,y1) - Space EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Radon/Hough Transform: Image Examples
Conclusion: Line detection can be implemented by point (peak) detection in the Radom/Hough transform domain EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
MATLAB Functions >> [H,theta,rho]=hough(f); >> figure, imshow(theta,rho,H, [ ], 'notruesize') >> axis on, axis normal; >> xlabel('\theta'),ylabel('\rho'), >> colorbar >> colormap(jet) >> [H,theta,rho]=hough(f); >> figure, imshow(theta,rho,H, [ ], 'notruesize') >> axis on, axis normal; >> xlabel('\theta'),ylabel('\rho'), >> colorbar >> colormap(jet) >>[r,c]=houghpeaks(H,5); >> hold on >> plot(theta(c), rho(r), 'linestyle', 'none','marker' , ... 's', 'color', 'w') EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Line Detection from Real-world Images
Gray Scale Image Edge Image (Canny) EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Peak Finding EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Roadmap Introduction to object detection What kind of object do we want to detect? Circle/ellipse detection Least-Square fitting based detector Line detection Hough transform-based detector Corner detection Harris corner detector Face/pedestrian/vehicle detection* EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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An introductory example:
Harris corner detector C.Harris, M.Stephens. “A Combined Corner and Edge Detector”. 1988 EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
The Basic Idea We should easily recognize the point by looking through a small window Shifting a window in any direction should give a large change in intensity EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Basic Idea
“flat” region: no change in all directions “edge”: no change along the edge direction “corner”: significant change in all directions EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Mathematics
Change of intensity for the shift [u,v]: Intensity Window function Shifted intensity or Window function w(x,y) = Gaussian 1 in window, 0 outside EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Mathematics*
For small shifts [u,v] we have a bilinear approximation: where M is a 22 matrix computed from image derivatives: EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Eigenvalue Analysis
Intensity change in shifting window: eigenvalue analysis 1, 2 – eigenvalues of M direction of the fastest change direction of the slowest change Ellipse E(u,v) = const (max)-1/2 (min)-1/2 EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Eigenvalues Carry Clues
2 Classification of image points using eigenvalues of M: “Edge” 2 >> 1 “Corner” 1 and 2 are large, 1 ~ 2; E increases in all directions 1 and 2 are small; E is almost constant in all directions “Edge” 1 >> 2 “Flat” region 1 EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Corner Response Measure
Measure of corner response: (k – empirical constant, k = ) Note that other definition of corner response measure also exists EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Mathematics
2 “Edge” “Corner” R depends only on eigenvalues of M R is large for a corner R is negative with large magnitude for an edge |R| is small for a flat region R < 0 R > 0 “Flat” “Edge” |R| small R < 0 1 EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Workflow
EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Workflow
Compute corner response R EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Workflow
Find points with large corner response: R>threshold EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Workflow
Take only the points of local maxima of R EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Harris Detector: Final Results
EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2009
Roadmap Introduction to object detection What kind of object do we want to detect? Circle/ellipse detection Least-Square fitting based detector Line detection Hough transform-based detector Corner detection Harris corner detector Face/pedestrian/vehicle detection* EE465: Introduction to Digital Image Processing Copyright Xin Li'2009
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Face Detection Face detection problem: do you see any human faces in an image? (trivial for human eyes but difficult for computers) EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Challenges with FD pose variation lighting condition variation facial expression variation EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Eigenfaces Training data Eigenface images ~ EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Neural Network based FD Cited from “Neural network based face detection”, by Henry A. Rowley, Ph.D. thesis, CMU, May 1999 EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
FD Examples EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Pedestrian Detection Why do we need to detect pedestrians? Security monitoring Intelligent vehicles Video database search Challenges Uncertainty with pedestrian profile, viewing distance and angle, deformation of human limb EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Learning-based Pedestrian Detection
EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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Thermal (Infrared) Imaging
Visible-spectrum image Infrared image EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Image Examples EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Vehicle Detection Intelligent vehicles aim at improving the driving safety by machine vision techniques EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
Summary Object detection remains one of the grand challenges in computer vision The most intriguing question lies in how to define object (or not-an-object) This is the task that human vision system (HVS) is good at; and the currently most effective machine vision is based on supervised learning principles. EE465: Introduction to Digital Image Processing Copyright Xin Li'2003
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