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Realtime Object Recognition Using Decision Tree Learning

Realtime Object Recognition Using Decision Tree Learning. Implemented with a Sony AIBO Robot CS 510 Presentation Chris Jorgensen. Realtime Object Recognition Using Decision Tree Learning. Implemented with a Sony AIBO Robot CS 510 Presentation Chris Jorgensen. Presentation.

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Realtime Object Recognition Using Decision Tree Learning

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  1. Realtime Object Recognition Using Decision Tree Learning Implemented with a Sony AIBO Robot CS 510 Presentation Chris Jorgensen

  2. Realtime Object Recognition Using Decision Tree Learning Implemented with a Sony AIBO Robot CS 510 Presentation Chris Jorgensen

  3. Presentation • Abstract/Introduction • Problem setup • Use of decision tree learning • Results • Summary/Thoughts

  4. Abstract/Introduction • Object recognition • Machine learning used to overcome issues: • Domain-specific • Complexity inestimable • Quality of results • Steps • Digital image scanned for features • Combine features into “meaningful” attributes • Attribute classification

  5. Introduction Continued… Object Recognition Flow

  6. Preprocessing • “Obvious” features • Colors • Limbs/Head • Shapes derived from image • Used for featureextraction

  7. Problem Setup • Recognition • Iterate through surfaces • Head, Side, Leg • Generate segments for each surface • Store segments in memory • 180 degree memory takes into account camera angle

  8. 180 Degree Memory

  9. Problem Setup Continued • Segmentation only done on “relevant” pixels • Determined by color • Attribute generation* • Color, # segments, # corners, et al • Continuous values discretization via brute-force generated optimal split

  10. Use of Decision Tree Learning • Classification via Decision Tree Learning! • Algorithm creates a tree consisting of the attributes; leafs are “symbols” • head, side, leg, body, et al • Tree is built by calculating attribute with the highest entropy (depends on # occurrences of each value) • Over-fitting solved by X2-pruning • Determine if attribute really detects a pattern

  11. Results

  12. Results Continued

  13. Results Continued

  14. Results Continued • Decision Tree Learning • Classification (27 ms) “quite fast” • 84% precision on 1080 examples for 5 classes • Even a low number of examples (25) resulted in over 50% precision • Room for improvement noted

  15. Summary/Thoughts • Short/vague paper • Why do they need faster than 27 ms recognition time? Aibos are slow! • Other work on Aibos done at PSU NWCIL • Lendaris/Holmstrom • Aibo uses limb angles, model of motion, to change gait based on floor surface • GA used to generate ideal gait for each surface

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