1. Adaptive object recognition in RGBz images
Motivations
Adaptive object recognition in RGBz images
Depth information can disambiguate color images
Depth detectors are becoming more prevalent
Number of depth datasets is “small”
Harder to acquire depth images (vs. Google image search)
Andrew Duchi, Stanford University 1

2. Adaptive object recognition in RGBz images
Approach
Adaptive object recognition in RGBz images
Acquire RGB training data
Learn RGB model
Classify RGBz test data
Use additional depth data to remove some incorrect detections
Reclassification
Outlier detection
Andrew Duchi, Stanford University 2

3. Adaptive object recognition in RGBz images
Results
Outlier detection provided no benefit
Relearning SVM gave 3% improvement with validated parameters
>10% improvement with optimal test parameters
Improvement consistent across parameters – not due to lucky parameter choice
Accuracy of retrained SVM across varied parameterizations
Andrew Duchi, Stanford University 3