Computer Science ›› 2021, Vol. 48 ›› Issue (8): 175-184.doi: 10.11896/jsjkx.200400064

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Stereo Track Blocks Coding System with Rotational Invariance

ZHOU Jia-li1,2, FENG Yuan-yuan1, WU Min3, WU Chao1   

  1. 1 College of Science,Zhejiang University of Technology,Hangzhou 310023,China;
    2 Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province,Hangzhou 310023,China;
    3 School of Science,Zhejiang University of Science and Technology,Hangzhou 310023,China
  • Received:2020-04-15 Revised:2020-07-26 Published:2021-08-10
  • About author:ZHOU Jia-li,born in 1981,Ph.D,asso-ciate professor.His main research in-terests include computer vision,pattern recognition and image processing,classification and coding,industrial robot.(zhoulue@zjut.edu.cn)WU Chao,born in 1982,Ph.D,lecturer.His main research interests include pattern recognition and image processing,intelligent systems and applications.
  • Supported by:
    Youth Science Fund Project(11301482),Key R&D Projects of Zhejiang Province(2020C01005,2020C01006,2021C03164) and Graduate Teaching Reform Project of Zhejiang University of Technology(2018127).

Abstract: Because the purpose and object of coding problem are different,it is necessary to make adjustments according to diffe-rent problems.For the coding problem of track blocks,a method of representing them by two-dimensional function is proposed,and track blocks are recognized by phase correlation.Firstly,track block is expanded under the two-dimensional polar coordinate system,and it is expressed as a two-dimensional discrete function.Due to the rotational invariance of the track block,the representation of track blocks is not unique,and a parameter matrix is introduced to specify a normal representation.Secondly,the phase correlation algorithm is used to measure the similarity of two track blocks.Finally,according to basic tracks in the block and their relative position,track block is compressed and encoded out of the representation of two-dimensional discrete function.Experiments show that our method has better expression of internal spatial structure and rotational invariance,and it is more extendable than the traditional coding methods.The solution of coding and matching problem is more adaptable for track blocks building and optimizing.

Key words: Classification coding, Phase correlation algorithm, Stereo track block, Two-dimensional function

CLC Number: 

  • TP391
[1]KOZAKI T,TEDENUMA H,MAEKAWA T.Automatic ge-neration of LEGO building instructions from multiple photographic images of real objects[J].Computer-Aided Design,2016,70:13-22.
[2]JIROUT J J,NEWCOMBE N S.Building Blocks for Developing Spatial Skills:Evidence From a Large,Representative U.S.Sample[J].Psychological Science,2015,26(3):302-310.
[3]ZHOU D C.Log mining of enterprise information system bycombining rough set and quotient space[J].Computer Science,2014,41(6A):421-424.
[4]XU L,DING S F.Research on Granularity Clustering Algo-rithms[J].Computer Science,2011,38(8):25-28.
[5]ANWER N,SCOTT P J,Srinivasan V.Toward a Classification of Partitioning Operations for Standardization of Geometrical Product Specifications and Verification[J].Procedia CIRP,2018,75:325-330.
[6]JUNG J Y,AHLUWALIA R S.FORCOD:A coding and classification system for formed parts[J].Journal of Manufacturing Systems,1991,10(3):223-232.
[7]ŠUGÁR P,ŠUGÁROVÁ J,KOLNíK M.Technology-basedsheet metal classification and coding system[J].Journal for Technology of Plasticity,2011,36(1):1-8.
[8]KUZNETSOV A P,KORIATH H J.Development of a classification and generation approach for innovative technologies[J].Procedia Manufacturing,2011,21:798-805.
[9]SORENSEN D G H,BRUNOE T D,NIELSEN K.A classification scheme for production system processes[J].Procedia CIRP,2018,72:609-614.
[10]SORENSEN D G H,BRUNOE T D,NIELSEN K.Brownfield Development of Platforms for Changeable Manufacturing[J].Procedia CIRP,2019,81:986-991.
[11]XU J,JI Y J,QI G N,et al.Classification and coding method of mechanical parts for the design process of mass customization[J].Journal of Mechanical Engineering,2010,11:149-155.
[12]BAI J.Semantic-based automatic extraction of reusable regions in 3D CAD models[J].Computer Science,2013,40(4):275-313.
[13]SMITH S,SMITH G C,JIAO R,et al.Mass customization in the product life cycle[J].Journal of intelligent manufacturing,2013,24(5):877-885.
[14]HOCHDÖRFFER J,LAULE C,LANZA G.Product varietymanagement using data-mining methods—Reducing planning complexity by applying clustering analysis on product portfolios[C]//International Conference on Industrial Engineering and Engineering Management (IEEM).IEEE,2017:593-597.
[15]MOON S K,SIMPSON T W,KUMARA S R T.A methodology for knowledge discovery to support product family design[J].Annals of Operations Research,2010,174(1):201-218.
[16]HUNG W L,YANG M S,LEE E S.Cell formation using fuzzy relational clustering algorithmp[J].Mathematical and Computer Modelling,2011,53(9):1776-1787.
[17]ZHANG Y,BERNARD A.Grouping parts for multiple partsproduction in additive manufacturing[J].Procedia CIRP,2014,17:308-313.
[18]KUGLIN C D.The phase correlation image alignment method[C]//Proceedings of IEEE International Conference on Cybernetics and Society.New York,1975:163-165.
[19]TZIMIROPOULOS G,ARGYRIOU V,ZAFEIRIOUS S,et al.Robust FFT-Based Scale-Invariant Image Registration withIma-ge Gradients[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(10):1899-1906.
[20]THANGAVEL P,KOLLA R.An Extension of FFT BasedImage Registration.In Advances in Intelligent Systems and Computing[C]//2nd International Conference on Advances in Computing and Information Technology.Chennai,India,2013:729-737.
[21]ZHOU J L,WU M,ZHOU H P.Research on Fast Dense Stereo Matching Technique Using Adaptive Mask[J].Pattern Recognition and Artificial Intelligence,2014,1(27):11-20.
[22]WANG H H,ARITSUGI M.An Approximate Matching Preprocessing for Efficient Phase-Only Correlation-Based Image Retrieval[J].Information Science and Applications,2015,339:319-326.
[1] CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long. Survey of Concept Drift Handling Methods in Data Streams [J]. Computer Science, 2022, 49(9): 14-32.
[2] WANG Ming, WU Wen-fang, WANG Da-ling, FENG Shi, ZHANG Yi-fei. Generative Link Tree:A Counterfactual Explanation Generation Approach with High Data Fidelity [J]. Computer Science, 2022, 49(9): 33-40.
[3] ZHANG Jia, DONG Shou-bin. Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer [J]. Computer Science, 2022, 49(9): 41-47.
[4] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[5] SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[6] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[7] ZHENG Wen-ping, LIU Mei-lin, YANG Gui. Community Detection Algorithm Based on Node Stability and Neighbor Similarity [J]. Computer Science, 2022, 49(9): 83-91.
[8] LYU Xiao-feng, ZHAO Shu-liang, GAO Heng-da, WU Yong-liang, ZHANG Bao-qi. Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network [J]. Computer Science, 2022, 49(9): 92-100.
[9] XU Tian-hui, GUO Qiang, ZHANG Cai-ming. Time Series Data Anomaly Detection Based on Total Variation Ratio Separation Distance [J]. Computer Science, 2022, 49(9): 101-110.
[10] NIE Xiu-shan, PAN Jia-nan, TAN Zhi-fang, LIU Xin-fang, GUO Jie, YIN Yi-long. Overview of Natural Language Video Localization [J]. Computer Science, 2022, 49(9): 111-122.
[11] CAO Xiao-wen, LIANG Mei-yu, LU Kang-kang. Fine-grained Semantic Reasoning Based Cross-media Dual-way Adversarial Hashing Learning Model [J]. Computer Science, 2022, 49(9): 123-131.
[12] ZHOU Xu, QIAN Sheng-sheng, LI Zhang-ming, FANG Quan, XU Chang-sheng. Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification [J]. Computer Science, 2022, 49(9): 132-138.
[13] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[14] QU Qian-wen, CHE Xiao-ping, QU Chen-xin, LI Jin-ru. Study on Information Perception Based User Presence in Virtual Reality [J]. Computer Science, 2022, 49(9): 146-154.
[15] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!