corner detection & color segmentation cse350/450-011 9 sep 03
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Corner Detection & Color Segmentation
CSE350/450-0119 Sep 03
Administration
• Clarifications to Homework 1
• Questions?
Class Objectives
• Linear Algebra Review
• Review how corners can be extracted from computer images
• Review how color is represented and can be segmented in a computer image
Supporting References
• “A Tutorial on Linear Algebra” by Professor C. T. Abdallah, University of New Mexico
• Edge & Corner Detection: Introductory Techniques for 3-D Computer Vision, Trucco & Verri, 1998
• CVOnline “Color Image Processing” Lecture Notes
• Poynton's Color FAQ
Edge Detection Review
INPUT IMAGE
1) NoiseSmoothing
EDGE IMAGE
2) EdgeEnhancement
Horizontal [-1 0 1]
Vertical [-1 0 1]T
),( yxI
x
yxI
),(
y
yxI
),(
2
122 ),(),(
),(
y
yxI
x
yxIyxI
“GRADIENT” IMAGE
3)Threshold
16/
121
242
121
Linear Algebra Review
Corner Detection Motivation
• Corners correspond to point in the both the world and image spaces
• Tracking multiple point across consecutive images allows us to estimate the relative rotation and translation of the camera
– Hartley’s 8-point algorithm
• Since the camera moves with our robot, we can infer robot motion “simply” by tracking eight or more corners
Corner Detection AlgorithmTrucco & Verri, 1998
6160531918
5855531513
5555501313
1010101111
1012121110
y
yxII
x
yxII yx
),(,),(
1. Compute the image gradients
2. Define a neighborhood size as an area of interest around each pixel
3x3 neighborhood
3. For each image pixel (i,j), construct the following matrix from it and its neighborhood values
e.g.
Corner Detection Algorithm (cont’d)
6160531918
5855531513
5555501313
1010101111
1012121110
xI
2
2
),(yyx
yxxji III
IIIC
22222
2222)3,3(
5553155550
13101011]1,1[
C
3. For each matrix C(i,j), determine the 2 eigenvalues λ(i.j)= [λ1, λ2].
4. Construct Λ-image where Λ(i,j)=min(λ(i.j)).
5. Threshold Λ-image. Anything greater than threshold is a corner.
Corner Detection Algorithm (cont’d)
ISSUE: The corners obtained will be a function of the threshold !
Corner Detection Sample Results
Threshold=25,000 Threshold=10,000
Threshold=5,000
Color Segmentation Motivation
• Computationally inexpensive (relative to other features)
• “Contrived” colors are easy to track
• Combines with other features for robust tracking
What is Color?
• Color is the perception of light in the visible region of the spectrum
• Wavelengths between 400nm - 700nm
• Imagers
– Retina (humans)
– CCD/CMOS (cameras)
RGB Color Space• Motivated by human visual system
– 3 color receptor cells (rods) in the retina with different spectral response curves
• Used in color monitors and most video cameras
YCbCr (YUV/YIQ) Color Space
“Greyscale”Y= 0.30*R+0.59*G+0.11*B
B
G
R
V
U
Y
081.0419.0500.0
500.0331.0169.0
114.0587.0299.0
• Separates luma (“brightness”) from the chroma (“color”) channels:
Y = 0.30*R+0.59*G+0.11*B, Cb = B-Y, Cr=R-Y
• YUV/YIQ are similar variants based upon NTSC/PAL television signals
Defining Colors in an RGB Image
Red Green Blue
How do we represent a “single” color?
Sample set for orange hat
Simple RGB Color Segmentation
)1.1,5.254( )8.14,6.103( )07.6,1.45(
256),(251 yxIR 135),(73 yxIG 58),(32 yxIB
& &
Red Green Blue
SegmentedColor Image
Color Tracking Demo
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