TY  - JOUR
AB  - Purpose Robot vision is a fundamental device for human–robot interaction and robot complex tasks. In this paper, the authors aim to use Kinect and propose a feature graph fusion (FGF) for robot recognition.Design/methodology/approach The feature fusion utilizes red green blue (RGB) and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and word embedding method to enhance the recognition results.Findings The authors also collect DUT RGB-Depth (RGB-D) face data set and a benchmark data set to evaluate the effectiveness and efficiency of this method. The experimental results illustrate that FGF is robust and effective to face and object data sets in robot applications.Originality/value The authors first utilize Jaccard similarity to construct a graph of RGB and depth images, which indicates the similarity of pair-wise images. Then, fusion feature of RGB and depth images can be computed by the Extended Jaccard Graph using word embedding method. The FGF can get better performance and efficiency in RGB-D sensor for robots.
VL  - 37
IS  - 3
SN  - 0144-5154
DO  - 10.1108/AA-01-2017-005
UR  - https://doi.org/10.1108/AA-01-2017-005
AU  - Liu Shenglan
AU  - Sun Muxin
AU  - Huang Xiaodong
AU  - Wang Wei
AU  - Wang Feilong
PY  - 2017
Y1  - 2017/01/01
TI  - Feature fusion using Extended Jaccard Graph and word embedding for robot
T2  - Assembly Automation
PB  - Emerald Publishing Limited
SP  - 278
EP  - 284
Y2  - 2024/09/19
ER  -