Categorical object recognition method robust to scale changes using depth data from an RGB-D sensor
Ju Han Yoo, Dong Hwan Kim, Sung-Kee Park · 2015
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.