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Calin Rotaru, Universität Hamburg, TAMS Oberseminar13. November 2007 Konzernforschung
Color Image Segmentation in HSI Space for Automotive Applications
Calin Rotaru, Dr. Thorsten Graf, Prof. Dr. Zhang Jianwei
13. November 2007
2Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Overview
• Motivation
• Description of the goal
• HSI space
• Color composition of an automotive scene
• Projection of scene points (S, I, SI)
• Point classification
• Computation of the segmentation threshold
• Results
3Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Motivation
near distanceradar
far distanceradar
ultrasonicsensors
rearview-camera
vision camera sensor
multibeamlaser sensor
Advanced Driver Assistance Systems require comprehensiveinformation about the ego vehicle and the surrounding environment
4Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Description of the goal
Find a function F(P(x, y), …) that is able to sort scene points into object and non-object by using color features
• single frame analysis
• reduce ambiguity for colors having close intensity values
• divide scene points (image pixels) in road surface, markings and obstacles (objects, vegetation)
• good results for lower part of the objects for distance estimation
5Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
HSI Space
HSI separates color from intensity. Close values for pixels having close colors.
If B > G, H = 360˚ - H
I=13 RGB
S=1−3RGB
min R ,G , B
H=cos−1
12[ R−G R−B ]
R−G 2 R−B G−B
SH
I
Hue, Saturation, Intensity
2. S values tend to be not 0 (and noisy) for achromatic pixels as long as they do have small R,G,B values
4. S values for bright pixels (big R,G,B values) are not noisy in HSI
6Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Automotive scene: typical color composition
1. Most of the infrastructure is achromatic (white or gray pixels)
3. Most of the objects are chromatic or induce chromatic noise due to reflectivity, black tires, etc.
5. Illumination may greatly affect the brightness
Original Image
Saturation
Intensity
Hue
7Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Projections on I, S and SI
Projection of the image points on the I axis (histogram)
-500
0
500
1000
1500
2000
2500
3000
3500
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255
Intensity
Nu
mb
er o
f P
oin
ts
Projection of the image points on the S axis (histogram)
-2000
0
2000
4000
6000
8000
10000
12000
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255
Saturation
Nu
mb
er o
f P
oin
ts
8Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
SI Projection
9Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Quick Review of the State of the Art
• Linear Color Thresholding• Histogram Thresholding
• Nearest Neighbor
• Probabilistic Methods
Sky &
Lane M
arking
s
Ro
ad
10Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Segmentation in the SI Plane
F S,I =S-S p
2 I− I p
2
Divider
F S,I =S-S p 2 I−I p
2
• Using the Euclidian Metric
• Using the Fast Form
(Sp,I
p) (S
p,I
p)
11Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Computation of the Divider, Threshold
F S,I =S-S p
2 I− I p
2
Divider
Divider=S 1 -S2
2 I 1− I 2
2
max F S,I
Threshold=max F S i ,Ii ∀ S,I ∈ road points
(Sp,I
p)
12Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Comparision with other methods
SI Segmentation
Nearest Neighbor Linear Thresholding
• Linear Color Thresholding (with adaptive Thresholds)
• Nearest Neighbor(road, lanes and everything else)
• SI Segmentation (adaptive Divider and Threshold)
Original Image
13Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Point Classification using “Fast Euclidian Metric”
S, I Saturation, Intensity of the scene point
Sp, Ip Saturation, Intensity of the reference point
Divider Updates the sensitivity of the segmentation
Threshold Separates the two classes (obstacle, non obstacle)
F S,I =S-S p
2 I− I p
2
Divider
14Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Conclusion
• distances in the SI plane encode important information about similarity of points
• much more relevant than alone intensity or saturation values
• even a simple metric is powerful enough to classify points for object detection purposes
• good results for lower part of vehicles• similar values outputted for pixels having similar color saturation• minimal errors for the road and lane markings
15Calin Rotaru, Universität Hamburg, TAMS OberseminarStand: 13. November 2007 Konzernforschung
Thank you for your attention.
• Motivation
• Description of the goal
• HSI space
• Color composition of an automotive scene
• Projection of scene points (S, I, SI)
• Point classification
• Computation of the segmentation thresholds
• Results
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