1
CSE152, Spr 2011
Intro Computer Vision
Edge Detection, Corner Detection
Lines
Introduction to Computer Vision
CSE 152
Lecture 10
CSE152, Spr 2011
Intro Computer Vision
Announcements
•
Assignment 2 due Tuesday, May 3.
•
Midterm: Thursday, May 5.
CSE152, Spr 2011
Intro Computer Vision
Last Lecture
CSE152, Spr 2011
Intro Computer Vision
Edges
1.
Object boundaries
2.
Surface normal discontinuities
3.
Reflectance (albedo) discontinuities
4.
Lighting discontinuities
(shadow boundaries)
CSE152, Spr 2011
Intro Computer Vision
Edge is Where Change Occurs: 1D
•
Change is measured by derivative in 1D
Smoothed Edge
First Derivative
Second Derivative
Ideal Edge
• Biggest change, derivative has maximum magnitude
• Or 2nd derivative is zero.
CSE152, Spr 2011
Intro Computer Vision
Numerical Derivatives
f(x
)
x
X
0
X
0
+h
X
0
h
Take Taylor series expansion of f(x) about x
0
f(x) = f(x
0
)+f’(x
0
)(xx
0
) +
½
f’’(x
0
)(xx
0
)
2 + …
Consider Samples taken at increments of h and first two terms, we
have
f(x
0
+h) = f(x
0
)+f’(x
0
)h+
½
f’’(x
0
)h
2
f(x
0
h) = f(x
0
)f’(x
0
)h+
½
f’’(x
0
)h
2
Subtracting and adding f(x
0
+h) and f(x
0
h) respectively yields
Convolve with
First Derivative: [1/2h
0 1/2h]
Second Derivative: [1/h
2
2/h
2
1/h
2
]
Can often drop h or h
2
in denominator
Yielding [1 0 1] and [1 2 1]
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
2
CSE152, Spr 2011
Intro Computer Vision
Implementing 1D Edge Detection
1.
Filter out noise: convolve with Gaussian
2.
Take a derivative: convolve with [1 0 1]
–
We can combine 1 and 2.
3.
Find the peak of the magnitude of the convolved
image: Two issues:
–
Should be a local maximum.
–
Should be sufficiently high.
CSE152, Spr 2011
Intro Computer Vision
Canny Edge Detector
1.
Smooth image by filtering with a Gaussian
2.
Compute gradient at each point in the image.
3.
At each point in the image, compute the direction
of the gradient and the magnitude of the gradient.
4.
Perform nonmaximal suppression to identify
candidate edgels.
5.
Trace edge chains using hysteresis tresholding.
CSE152, Spr 2011
Intro Computer Vision
Gradients:
x
y
Is this dI/dx or dI/dy?
∇
I
=
∂
I
/
∂
x
∂
I
/
∂
y
Ρਟ
Σਿ
ਯ
Τ
Φ੯
Υ
CSE152, Spr 2011
Intro Computer Vision
We wish to mark points along the curve where the magnitude is biggest.
This is the end of the preview.
Sign up
to
access the rest of the document.
 Spring '08
 staff
 Computer vision, SPR

Click to edit the document details