An Efficient View-Point Invariant Detector and Descriptor

Abstract:

Many computer vision applications need keypoint correspondence between images under different view conditions. Generally speaking, traditional algorithms target applications with either good performance in invariance to affine transformation or speed of computation. Nowadays, the widely usage of computer vision algorithms on handle devices such as mobile phones and embedded devices with low memory and computation capability has proposed a target of making descriptors faster to computer and more compact while remaining robust to affine transformation and noise. To best address the whole process, this paper covers keypoint detection, description and matching. Binary descriptors are computed by comparing the intensities of two sampling points in image patches and they are matched by Hamming distance using an SSE 4.2 optimized popcount. In experiment results, we will show that our algorithm is fast to compute with lower memory usage and invariant to view-point change, blur change, brightness change, and JPEG compression.

• Periodical: Advanced Materials Research (Volume 659)
• Pages: 143-148
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.659.143
• Online since: January 2013
• Authors: Zheng Guang Xu, Chen Chen, Xu Hong Liu
• Keywords: Binary Image Feature, Keypoint Matching, Viewpoint Invariance

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