FEATURE MATCHING OF MULTI-VIEW 3D MODELS BASED ON HASH BINARY ENCODING

被引：16

作者：

Li, H. ^{[1
]}

Zhao, T. ^{[1
]}

Li, N. ^{[2
]}

Cai, Q. ^{[1
]}

Du, J. ^{[3
]}

机构：

[1] Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing Key Lab Big Data Technol Food Safety, 11 Fucheng Rd, Beijing, Peoples R China

[2] Beijing Technol & Business Univ, Sch Mat Sci & Mech Engn, 11 Fucheng Rd, Beijing, Peoples R China

[3] Beijing Univ Posts & Telecommun, Sch Comp Sci & Technol, 10 Xitucheng Rd, Beijing, Peoples R China

来源：

NEURAL NETWORK WORLD | 2017年 / 27卷 / 01期

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

multi-view 3D models; feature matching; depth image; feature extraction; hash binary encoding; RETRIEVAL;

D O I：

10.14311/NNW.2017.27.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Image data and 3D model data have emerged as resourceful foundation for information with proliferation of image capturing devices and social media. In this paper, a feature matching method based on hash binary encoding for multi view 3D models in social media is proposed. SIFT algorithm is first used to extract features of the depth image, and then RANSAC is utilized as a filter. Finally, a cascade hash binary encoding algorithm is adapted to match the feature of multi-view models. Experimental results on SHREC2014 dataset have shown the effectiveness of the proposed method.

引用

页码：95 / 105

页数：11

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