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