Paper Info
Title
3D-Object Recognition Based on LLC Using Depth Spatial Pyramid
Abstract
Recently introduced high-accuracy RGB-D cameras are capable of providing high quality three-dimension information (color and depth information) easily. The overall shape of the object can be understood by acquiring depth information. However, conventional methods adopted this camera use depth information only to extract the local feature. To improve the object recognition accuracy, in our approach, the overall object shape is expressed by the depth spatial pyramid based on depth information. In more detail, multiple features within each sub-region of the depth spatial pyramid are pooled. As a result, the feature representation including the depth topological information is constructed. We use histogram of oriented normal vectors (HONV) designed to capture local geometric characteristics as 3D local features and locality-constrained linear coding (LLC) to project each descriptor into its local-coordinate system. As a result of image recognition, the proposed method has improved the recognition rate compared with conventional methods.
Year
DOI
Venue
2014
10.1109/ICPR.2014.724
Pattern Recognition
Keywords
DocType
ISSN
feature extraction,geometry,image colour analysis,image representation,linear codes,object recognition,vectors,3D local features,3D-object recognition,HONV,LLC,color information,depth spatial pyramid,depth topological information,feature representation,histogram of oriented normal vectors,image recognition,local feature extraction,local geometric characteristics,local-coordinate system,locality-constrained linear coding,object shape,recognition rate,three-dimension information
Conference
1051-4651
Citations 
PageRank 
References 
2
0.35
9
Authors
4
Name
Order
Citations
PageRank
Toru Nakashika18113.60
Takafumi Hori220.35
Tetsuya Takiguchi3858.77
Yasuo Ariki451988.94