.png){kind=icon}
Return
Improved ORB Algorithm Using Three-Patch Method and Local Gray Difference
.png){kind=icon}
OA
AI
DOI：10.3390/s20040975.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray difference (ORB-TPLGD). ORB takes a breakthrough in real-time aspect. However, subtle changes of the image may greatly affect its final binary description. In this paper, the feature description generation is focused. On one hand, instead of pixel patch pairs comparison method used in present ORB algorithm, a three-pixel patch group comparison method is adopted to generate the binary string. In each group, the gray value of the main patch is compared with that of the other two companion patches to determine the corresponding bit of the binary description. On the other hand, the present ORB algorithm simply uses the gray size comparison between pixel patch pairs, while ignoring the information of the gray difference value. In this paper, another binary string based on the gray difference information mentioned above is generated. Finally, the feature fusion method is adopted to combine the binary strings generated in the above two steps to generate a new feature description. Experiment results indicate that our improved ORB algorithm can achieve greater performance than ORB and some other related algorithms.
Keywords:
image registration
three-patch method
local gray difference
feature fusion
Journal
IF:
3.5
Papers:
7.3W
Citations:
20.9W
Organization
Cited Papers
Foresight as a Tool for the Planning and Implementation of Visions for Smart City Development.png)
EnergiesOAAI
EnergiesIF0
OAAIShareSaveShareSaveShareSaveOral Idarubicin as a Single Agent Therapy in Patients with Relapsed or Resistant Multiple Myeloma
PREAI
ShareSave
View more