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A novel real-time local visual feature, namely FAST+LBP, is proposed in this paper for omnidirectional vision. It combines the advantages of two computationally simple operators by using Features from Accelerated Segment Test (FAST) as the feature detector and Local Binary Patterns (LBP) operator as the feature descriptor. The matching experiments of the panoramic images from the COLD database are performed to determine its best parameters, and to evaluate and compare its performance with SIFT. The experimental results show that our algorithm performs better, and features can be extracted in real-time.

A Novel Real-Time Local Visual Feature for Omnidirectional Vision Based on FAST and LBP