Computing Disparity on Demand: Disparity based Classification using Error-Tolerant Decision Tree Ensembles

Most range-based recognition systems require the calculation of a full disparity map at adequate resolutions prior to the recognition step. There also exist range-based systems that only require the computation of a sparse disparity map. We introduce a 3D shape classification method in which the disparity calculation is guided by the needs of the classification process. The method uses decision trees for shape classification and calculates the disparity only at certain locations in the image, as required by the tree structure. The calculation is very efficient as only the minimum number of disparity values are calculated. To render the classification robust, we use an ensemble of trees. The proposed ensemble method is different from the currently known ensemble methods and makes the classification system more robust to errors in the disparity calculation. The method was applied to a real world problem and good classification results were obtained.