Categorical Object Recognition Method Robust to Scale Changes Using Depth Data From an RGB-D Sensor

Abstract

We propose a new categorical object recognition algorithm robust to scale changes. We first partition an input image into k regions by using depth data from an RGB-D sensor, and then we estimate the object scale for each partitioned region. Finally, scaled model is applied to recognize the object.