Computer Science ›› 2019, Vol. 46 ›› Issue (7): 30-37.doi: 10.11896/j.issn.1002-137X.2019.07.005

• Surveys • Previous Articles     Next Articles

Review of Shape Representation for Objects

WU Gang,XU Li-min   

  1. (Department of Electronic Business,Nanjing University of Finance and Economics,Nanjing 210003,China)
  • Received:2018-08-11 Online:2019-07-15 Published:2019-07-15

Abstract: Shape retrieve and objection are widely applied into medical diagnostics,target recognition,image retrieve and computer vision,etc.The efficient retrieve and objection of shapes completely depend on an excellent shape representation algorithm.This paper proposed the assessment criterion for shape representation.Then,according to the criterion,the existing shape representations were categorized into linear combination representations,spatial association relationship,feature representation based on differential and integral property of shapes and deformation representations.Each of these methods was analyzed and accessed in terms of mathematical principle,the ability of multiscale representation,variants,robust,reconstruction of original shapes,identification of signal and noise,etc.Furthermore,the advantages and disadvantages of each algorithm were discussed,especially,explored from the principle of mathematics.Finally,the suggestions for the future research were also given.

Key words: Shape representation, Shape analysis, Object recognition, Shape retrieve, Image analysis

CLC Number: 

  • TP391
[1] KURNIANGGORO L,WAHYON O,JO K H.A survey of 2d shape representation:Methods,evaluations,and future research directions[J].Neurocomputing,2018,300:1-16.
[2] ZHANG D S,LU G J.Review of shape representation and description techniques[J].Pattern Recognition,2004,37(1):1-19.
[3] KAZMI I K,YOU L,ZHANG J J.A survey of 2d and 3d shape descriptors[C]∥2013 10th International Conference Computer Graphics,Imaging and Visualization.Macau:IEEE Press,2013:1-10.
[4] DUAN L J.A Survey of Shape Feature[J].Computer Secience,2007,34(8):215-218.(in Chinese)段立娟.形状特征的编码描述研究综述[J].计算机科学,2007,34(8):215-218.
[5] DEMISSE G G,AOUADA D,OTTERSTEN B.Deformation based curved shape representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(6):1338-1351.
[6] MASON J C,HANDSCOMB D.Chebyshev Polynomials[M].Berlin:Chapman & Hall/CRC,2003:20-30.
[7] HEIZER A,BARZOHAR M,MALAH D.Stable fitting of 2d curves and 3d surfaces by implicit polynomials[J].IEEE Tran-sactions Pattern Analysis and Machine Intelligence,2004,26(10):1283-1294.
[8] WU G,ZHANG Y C.A novel fractional implicit polynomial approach for stable representation of complex shapes[J].Journal of Mathematical Imaging and Vision,2016,55(1):89-104.
[9] OLIVEIRA A B D,SILVA P,DA C R,et al.A novel 2d shape signature method based on complex network spectrum[J].Pattern Recognition Letters,2015,63:43-49.
[10] NIXON M,AGUADO A S.Feature Extraction and Image Pro- cessing for Computer Vision[M].London:Academic Press,2012:100-121.
[11] ZHANG G,MA Z M,NIU L Q,et al.Modified fourier descriptor for shape feature extraction[J].Journal of Central South University,2012,19(2):488-495.
[12] WU G Y,ZHANG Y C.A new chebyshev polynomials descriptor applicable to open curves[J].Pattern Recognition Letters,2015,62:41-48.
[13] WU H Y,YAN S L.Computing invariants of tchebichef moments for shape based image retrieval[J].Neurocomputing,2016,215(26):110-117.
[14] Mukundan R,Ong S H,Lee P A.Image analysis by tchebichef moments[J].IEEE Transactions on Image Processing,2001,10(9):1357-1364.
[15] PEE C Y,ONG S H,RAVEENDRAN P.Numerically efficient algorithms for anisotropic scale and translation tchebichef moment invariants[J].Pattern Recognition Letters,2017,92:68-74.
[16] CHEN Z,SUN S K.A zernike moment phase-based descriptor for local image representation and matching[J].IEEE Transactions on Image Processing,2010,19(1):205-219.
[17] HU M K.Visual Pattern Recognition by Moment Invariants [J].IRE Transactions on Information Theory,1962,8(2):179-187.
[18] SAJJANHAR A.A technique for similarity retrieval of shapes[D].Melbourne:Monash University,1997:80-90.
[19] ROUHANI M,SAPPA A D.Implicit polynomial representation through a fast fitting error estimation[J].IEEE Transactions on Image Processing,2012,21(4):2089-2098.
[20] TEAGUE M R.Image analysis via the general theory of mo- ments[J].Journal of the Optical Socient of America,1980,70(8):920-930.
[21] WANG K J,ZHANG H G,CHAI L S,et al.A comparative study of moment-based shape descriptors for product image retrieval[C]∥2011 International Conference on Image Analysis and Signal Processing.Hubei:IEEE press,2011:355-359.
[22] ROUHANI M,SAPPA A D,Boyer E.Implicit b-spline surface reconstruction[J].IEEE Transactions on Image Processing,2015,24(1):22-32.
[23] EL-GHAZAL A,BASIR O,BELKASIM S.Farthest point dis- tance:A new shape signature for fourier descriptors[J].Signal Processing:Image Communication,2009,24(7):572-586.
[24] BAI X,RAO C,WANG X.Shape vocabulary:A robust and efficient shape representation for shape matching[J].IEEE Tran-sactions on Image Processing,2014,23(9):3935-3949.
[25] DALIRI M R,TORRE V.Robust symbolic representation for shape recognition and retrieval[J].Pattern Recognition,2008,41(5):1782-1798.
[26] LIN C,PUN C M,VONG C M,et al.Efficient shape classification using region descriptors[J].Multimedia Tools and Applications,2017,76(1):83-102.
[27] MOUINE S,YAHIAOUI I,VERROUST-BLONDET A.A shape-based approach for leaf classification using multiscale triangular representation[C]∥Proceedings of the 3rd ACM Conference on Multimedia Retrieval.Dallas:ACM press,2013:127-134.
[28] ALAJLAN N,RUBE I E,KAMEL M S,et al.Shape retrieval using triangle-area representation and dynamic space warping[J].Pattern Recognition,2007,40(7):1911-1920.
[29] HU R,JIA W,LING H,et al.Multiscale distance matrix for fast plant leaf recognition[J].IEEE Transactions on ImageProces-sing,2012,21(11):4667-4672.
[30] HU R,JIA W,LING H,et al.Angular pattern and binary angular pattern for shape retrieval[J].IEEE Transactions on Image Processing,2014,23(3):1118-1127.
[31] WANG B,GAO Y.Hierarchical string cuts:A translation,rotation,scale,and mirror invariant descriptor for fast shape retrie-val[J].IEEE Transactions on Image Processing,2014,23(9):4101-4111.
[32] LI M,YUAN B Z.2d-lda:A statistical linear discriminant analysis for image matrix[J].Pattern Recognition Letters,2005,26(5):527-532.
[33] BELONGIE S,MALIK J,PUZICHA J.Shape matching and object recognition using shape contexts[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(4):509-522.
[34] ZHAO L,PENG Q Q,HUANG B Q.Shape matching algorithm based on shape contexts[J].IET Computer Vision,2015,9(5):681-690.
[35] AN G,YU W.Captcha recognition algorithm based on the relative shape context and point pattern matching[C]∥2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA).Changsha:IEEE press,2017:168-172.
[36] BOUAGAR S,LARABI S.Discriminative outlines parts for shape retrieval[J].Journal of Visual Communication and Image Representation,2015,33:149-164.
[37] ZHU Z T,WANG X G,BAI S,et al.Deep learning representation using autoencoder for 3d shape retrieval[J].Neurocompu-ting,2016,204:41-50.
[38] ASADA H,BRADY M.The curvature primal sketch[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(1):2-14.
[39] BERRADA F,ABOUTAJDINE D,OUATIK S E,et al.Review of 2d shape descriptors based on the curvature scale space approach[C]∥2011 International Conference on Multimedia Computing and Systems.Ouarzazate:IEEE press,2011:1-6.
[40] ADAMEK T,O’CONNOR N E.A multiscale representation method for nonrigid shapes with a single closed contour[J].IEEE Transactions on Circuits and Systems for Video Techno-logy,2004,14(5):742-753.
[41] HONG B,SOATTO S.Shape matching using multiscale integral invariants[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(1):151-160.
[42] HONG B W,PRADOS E,SOATTO S,et al.Shape representation based on integral kernels:Application to image matching and segmentation[C]∥2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York:IEEE press,2006:833-840.
[43] ANDREA C G.Metrics of Curves in Shape Optimization and Analysis[M].Cham:Springer International Publishing,2013:205-319.
[44] Younes L.Computable elastic distances between shapes[J].SIAM Journal on Applied Mathematics,1998,58(2):565-586.
[45] YOUNES L.Parametrized Plane Curves[M].Berlin:Springer Berlin Heidelberg,2010:1-42.
[46] YOUNES L.Optimal matching between shapes via elastic de- formations[J].Image and Vision Computing,1999,17(5):381-389.
[47] GEMMEKE J F,ELLIS D P W,FREEDMAN D,et al.Audio set:An ontology and human-labeled dataset for audio events[C]∥2017 IEEE International Conference on Acoustics,Speech and Signal Processing.LA:IEEE press,2017:776-780.
[48] ZEILER M D,FERGUS R.Visualizing and understanding con- volutional networks[M]//Computer Vision ECCV 2014.Berlin:Springer,2014:818-833.
[49] LECUN Y,BENGIO Y S.Deep learning[J].Nature,2015,521(7553):436-444.
[50] NASCIMENTO J C,CARNEIRO G.Deep learning on sparse manifolds for faster object segmentation[J].IEEE Transactions on Image Processing,2017,26(10):4978-4990.
[1] PANG Yu, LIU Ping, LEI Yin-jie. Realization of “Uncontrolled” Object Recognition Algorithm Based on Mobile Terminal [J]. Computer Science, 2019, 46(6A): 153-157.
[2] HAO Wen, WANG Ying-hui, NING Xiao-juan, LIANG Wei and SHI Zheng-hao. Survey of 3D Object Recognition for Point Clouds [J]. Computer Science, 2017, 44(9): 11-16.
[3] WANG Jian, BAI He-xiang and LI De-yu. High Resolution Remote Sensing Image Object Recognition Algorithm Based on SIFT and Non-parametric Bayes [J]. Computer Science, 2017, 44(1): 289-294.
[4] LIU Tao, WU Ze-min, JIANG Qing-zhu, ZENG Ming-yong and PENG Tao-pin. Fast Object Recognition Method Based on Objectness [J]. Computer Science, 2016, 43(7): 73-76, 94.
[5] WANG Yan-qing,CHEN De-yun,SHI Chao-xia,LIU Bo,FANG Guo-zhi. Object Recognition Based on a New Method of Edge Crawling [J]. Computer Science, 2010, 37(8): 266-269272.
[6] LEI Bao-quan, YANG Li-hua, CHENG Yong-mei, ZHAO Chun-hui, WU Yan-ru. Natural Object Recognition Algorithm Based on SVM and Coloexture Combination Features [J]. Computer Science, 2009, 36(10): 274-276.
[7] . [J]. Computer Science, 2006, 33(8): 229-231.
[8] WEI Li, WU Zhong-fu, LI Yun ,GU Yi (College of Computer, Chongqing University, Chongqing 400044). [J]. Computer Science, 2006, 33(5): 238-240.
[9] . [J]. Computer Science, 2006, 33(11): 228-232.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[5] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105, 130 .
[6] WANG Zhen-wu, LV Xiao-hua and HAN Xiao-hui. Survey of Terrain LOD Technology Based on Quadtree Segmentation[J]. Computer Science, 2018, 45(4): 34 -45 .
[7] HAN Kui-kui, XIE Zai-peng and LV Xin. Fog Computing Task Scheduling Strategy Based on Improved Genetic Algorithm[J]. Computer Science, 2018, 45(4): 137 -142 .
[8] ZHU Shu-qin, WANG Wen-hong and LI Jun-qing. Chosen Plaintext Attack on Chaotic Image Encryption Algorithm Based on Perceptron Model[J]. Computer Science, 2018, 45(4): 178 -181, 189 .
[9] RAN Zheng, LUO Lei, YAN Hua and LI Yun. Study on Automatic Method for AUTOSAR Runnable Entity-task Mapping[J]. Computer Science, 2018, 45(4): 190 -195, 226 .
[10] HOU Yan-e, KONG Yun-feng and DANG Lan-xue. Greedy Randomized Adaptive Search Procedure Algorithm Combining Set Partitioning for Heterogeneous School Bus Routing Problems[J]. Computer Science, 2018, 45(4): 240 -246 .