1. Page: 1 PHAM VAN Tien Real-Time Approach for Auto-Adjusting Vision System Reading Class International Graduate School of Dynamic Intelligent Systems

2. Page: 2 Objectives of the real-time approach Not too heavy computation. Power of processors and memory in robots are limited Adaptive to changes of lighting condition during run-time Pre-run calibration should be avoided Recognition process should not depend only on color segmentation Devices for image processing and computation : - Camera - Sensor

3. Page: 3 Color-coded environment Two color-coded flags (pink and yellow/green/skyblue) for localization Two goals (skyblue and yellow) Ball (orange) Robots (wearing red and blue tricots)

4. Page: 4 Guiding attention More attention is guided on areas of image where small objects are expected Not all, but only pixels at grid point is considered Image sequences: looking for objects around the previous detection (e.g. ball) Iterative processing: first, prominent features are searched, if found, it will hint to the other features Other sensor: reading data from distance or tilt sensors to guide visual attention Knowledge about environment: heuristics can be used to simplify image processing

5. Page: 5 Scan Lines Horizon is determined first Grid lines above and below the horizon are then set GT2004ImageProcessor.cpp

6. Page: 6 Vertical lines Bellow lines: for determination of the ball, field lines/borders, and lower half of the goals Above lines: mainly for finding the flags. Lines paralle to the horizon may be used if prediction fails

7. Page: 7 Color classification  A sub cube is the reference (green of carpet). Limited number of colors are defined (class CorlorTableReference)  Auto-adaption of reference and color segmentation of the cube improve identification ColorClasses ColorTableReference

8. Page: 8 Adaptation to lighting condition

9. Page: 9 Color adaptation Analysis of scan lines over goals and field border will help determining the reference cube (green) Update is made as every image green enough appears.

10. Page: 10 Edge detection Finding characteristic changes in YUV channels Two criteria to identify edges: Three dimensional constrast pattern: Surrounding color classes: pixels surrounding detected edges are considered to resolve ambiguities of constrast pattern classification and to filter edges caused by noise REdgeDetection : detection SUSANEdgeDetectionLite: edge filter

11. Page: 11 Open question Why need to predefine color of objects: goal, border, ball, etc ? Is that possible for robots to self-identify the object right before the match ? The goalkeeper is supposed to be more idle than other robots, why not impose more computation load on him, and then let him tell other players ?