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E. Itır Karaç

Techniques for Improving Vision and Locomotion on the Sony AIBO Robot by Quinlan M., Chalup S., Middleton R. E. Itır Karaç. Outline. Introduction Hardware Environment Techniques and Tasks Color detection using SVMs Collusion detection using SVMs Conclusion. Introduction.

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E. Itır Karaç

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  1. Techniques for Improving Vision andLocomotion on the Sony AIBO Robotby Quinlan M., Chalup S., Middleton R. E. Itır Karaç

  2. Outline • Introduction • Hardware Environment • Techniques and Tasks • Color detection using SVMs • Collusion detection using SVMs • Conclusion

  3. Introduction

  4. Hardware and Environment • Sony AIBO entertainment models ERS-210 or ERS-210A • 64-bit RISC processor with clock speed 192 MHz and 384 MHz • programmed in C++ using the Sony’s OPEN-R environment • the use of servos gives the robot 20 degrees of freedom • RoboCup Legged League

  5. Techniques • Support Vector Machines • Multi-class SVMs • One-class SVMs

  6. One-Class SVMs • Idea: try to find a sphere with minimum volume, containing most of the data objects

  7. Formulation of One-class SVM • describe the sphere with center a and radius R. • The center of the sphere is a linear combination of some of the data objects, called support objects. • Support objects and corresponding weights are obtained by solving this optimization problem

  8. Tasks • Vision system for most teams consists of 4 main tasks: • Color Classification • Run Length Encoding • Blob Formation • Object Recognition

  9. Color Classification • Color Classification Task • Images are taken from the camera in YUV bitmap format • Each pixel in the image is assigned a color label using a lookup table. • Initial generation of the LUT is critical and a new LUT has to be generated with any change in the lighting condition. • Currently this is done manually by taking hundred of images and assigning a color label pixel-by-pixel-basis • This process is time consuming and may still contain holes and classification errors

  10. Method by Shapiro & Stockman • Convert existing LUT values from YUV to the HSI color space • Fit an ellipsoid E, which can be represented by the quadratic form: • This problem is linear in the unknowns and leads to the convex optimization problem • This formulation tries to find the ellipsoid such that the sum of the squares of the lengths of the principle axis is minimum • Disadvantage: • restricting the shape of possible regions • duplicates and potential outliers should be removed manually before the ellipsoid is fitted • Advantage: a simple representation

  11. Proposed Method • An individual one-class SVM is created for each color. • With an extremely low υ, and large γ the boundary formed by the desicion function contains (1- υ) of the training points • Advantage: SVM simultaneously removes the outliers • SVM can be used in to situations • Set up phase at a competion • Updating an existing LUT

  12. Results

  13. Results

  14. Results

  15. Another Task: Collusion Detection • Previously statistical methods are used • Requires 6MB of memory • It relies on domain knowledge • Extremely low computational expense • One-class SVM is employed as a novelty detection mechanism • SVM decision function will return +1 for normal step, -1 for vaulty steps • Aim: minimize falkse positives

  16. Questions ?

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