Title :
Categorical object recognition method robust to scale changes using depth data from an RGB-D sensor
Author :
Ju Han Yoo ; Dong Hwan Kim ; Sung-Kee Park
Author_Institution :
Korea Inst. of Sci. & Technol., Seoul, South Korea
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.
Keywords :
image sensors; object recognition; RGB-D sensor; categorical object recognition method; depth data; k regions; Computational modeling; Computer vision; Conferences; Object recognition; Partitioning algorithms; Robustness; Shape;
Conference_Titel :
Consumer Electronics (ICCE), 2015 IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4799-7542-6
DOI :
10.1109/ICCE.2015.7066335
