realtime object recognition using decision tree learning
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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. - PowerPoint PPT PresentationTRANSCRIPT
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
1. Abstract/Introduction
2. Problem setup
3. Use of decision tree learning
4. Results
5. Summary/Thoughts
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
Introduction Continued…
Object Recognition Flow
Preprocessing
• “Obvious” features– Colors– Limbs/Head
• Shapes derived from image– Used for feature
extraction
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
180 Degree Memory
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
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
Results
Results Continued
Results Continued
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
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