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

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

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