Imaging Components, Systems, and Processing

Simple and efficient improvement of spin image for three-dimensional object recognition

[+] Author Affiliations
Rongrong Lu

Shenyang Institute of Automation, Chinese Academy of Sciences, 114 Nanta Street, Shenyang, Liaoning 110016, China

University of Chinese Academy of Sciences, 19 Yuquan Street, Beijing 100049, China

Key Laboratory of Image Understanding and Computer Vision, 114 Nanta Street, Shenyang, Liaoning 110016, China

Chinese Academy of Sciences, Key Laboratory of Optical-Electronic Information, 114 Nanta Street, Shenyang, Liaoning 110016, China

Feng Zhu, Yingming Hao, Qingxiao Wu

Shenyang Institute of Automation, Chinese Academy of Sciences, 114 Nanta Street, Shenyang, Liaoning 110016, China

Key Laboratory of Image Understanding and Computer Vision, 114 Nanta Street, Shenyang, Liaoning 110016, China

Chinese Academy of Sciences, Key Laboratory of Optical-Electronic Information, 114 Nanta Street, Shenyang, Liaoning 110016, China

Opt. Eng. 55(11), 113102 (Nov 09, 2016). doi:10.1117/1.OE.55.11.113102
History: Received July 29, 2016; Accepted October 18, 2016
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Abstract.  This paper presents a highly distinctive and robust local three-dimensional (3-D) feature descriptor named longitude and latitude spin image (LLSI). The whole procedure has two modules: local reference frame (LRF) definition and LLSI feature description. We employ the same technique as Tombari to define the LRF. The LLSI feature descriptor is obtained by stitching the longitude and latitude (LL) image to the original spin image vertically, where the LL image was generated similarly with the spin image by mapping a two-tuple (θ,φ) into a discrete two-dimensional histogram. The performance of the proposed LLSI descriptor was rigorously tested on a number of popular and publicly available datasets. The results showed that our method is more robust with respect to noise and varying mesh resolution than existing techniques. Finally, we tested our LLSI-based algorithm for 3-D object recognition on two popular datasets. Our LLSI-based algorithm achieved recognition rates of 100%, 98.2%, and 96.2%, respectively, when tested on the Bologna, University of Western Australia (UWA) (up to 84% occlusion), UWA datasets (all). Moreover, our LLSI-based algorithm achieved 100% recognition rate on the whole UWA dataset when generating the LLSI descriptor with the LRF proposed by Guo.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Rongrong Lu ; Feng Zhu ; Yingming Hao and Qingxiao Wu
"Simple and efficient improvement of spin image for three-dimensional object recognition", Opt. Eng. 55(11), 113102 (Nov 09, 2016). ; http://dx.doi.org/10.1117/1.OE.55.11.113102


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