اسلوب جديد لتميز انماط الاشكال اعتمادا على صفات محيط الشكل المستخرجة بواسطة خوارزمية تبسيط الخط

Wang x, Ding w, Liu H, Huang x. Shape recognition through multi-level fusion of features and classifiers. Granul Comput. 2019. April 5: 437–448. https://doi.org/10.1007/s41066-019-00164-8

Yang C,Yu Q. Invariant multiscale triangle feature for shape recognition. Appl Math Comput. 2021. Augest; 403(6): https://doi.org/10.1016/j.amc.2021.126096 .

Cong v, Hanh L,Phuong L, Duy D . Design and development of robot arm system for classification and sorting using machine vision. FME Trans .2022; 50: 181-192. http://dx.doi.org/10.5937/fme2201181C.

Yadav A S, Kumar S, Karetla, G R, Cotrina-Aliaga J C, Arias-Gonzáles J L, Kumar V, et al. feature extraction using probabilistic neural network and BTFSC-net model with deep learning for brain tumor classification. J Imaging.2022; 9(1): 1-22 https://doi.org/10.3390/jimaging9010010

Mannan A, Babri H, Saeed M. Offline shape recognition using flexible DCT grid. Sci Iran. 2012 June; 19 (6): 1722–1730. https://doi.org/10.1016/j.scient.2012.10.0

Patel M, Tandel P. A Survey on Feature Extraction Techniques for Shape based Object Recognition. Int J Comput Appl. 2016; 137(6): 16-20. https://doi.org/10.5120/ijca2016908782

Ahmed M, Aradhya M. 2D Shape Recognition and Retrieval Using Shape Contour Based on the 8-Neighborhood Patterns Matching Technique. IGI Global- (JACHI). 2019 july-Dec; 10: 49-61 http://dx.doi.org/10.4018/IJSE.2019070104.

Yan X, Yang M. A Comparative Study of Various Deep Learning Approaches to Shape Encoding of Planar Geospatial Objects. ISPRS Int J Geoinf. 2022 Oct; 11: 527. https://doi.org/10.3390/ijgi11100527

Basavanna M, Prem Singh M, Prakash Raje Urs M, Chandraiah T. Recognition of Geometrical Shapes Using Inclination and Statistical Features. IJNIET. 2020 Jan; 12: 21-26.

Kaur R, Devendran V. Content Based Image Retrieval. IJITEE. 2020 Aug; 9: 222-228. https://doi.org/10.1080/23311916.2021.1927469

YANG C, Fang l, Wei h . Learning Contour-based Mid-level Representation for Shape Classification. IEEE Access. .2020 Aug 8: 157587 - 157601. https://doi.org/10.1109/ACCESS.2020.3019800.

Mori G, Belongie S, Malik J. Efficient shape Matching Using shape Contexts. IEEE Trans. Pattern Anal. Mach. Intell. 2005; 27(11): 1832 – 1837. https://doi.org/10.1109/TPAMI.2005.220

Zhu C, Yang J. Vision Based Hand Gesture Recognition Using 3D Shape Context. IEEE/CAA J. Autom. Sin. 2021; 8(9): 1600-1613. https://doi.org/10.1109/JAS.2019.1911534

Xu C, Liu J,Tang X . 2D Shape Matching by Contour Flexibility. IEEE Trans Pattern Anal Mach Intell. .2009; 31(1). https://doi.org/10.1109/TPAMI.2008.199

Xu G, Li C. Plant leaf classification and retrieval using multi-scale shape descriptor. J Eng. 2021; 467–475. https://doi.org/10.1049/tje2.12050

Yang C, Fang L, Fei B, Yu Q, Wei H. Multi-level contour combination features for shape recognition. Comput Vis Image Underst. 2023 .March;229:https://doi.org/10.1016/j.cviu.2023.103650

Yan T,LiuY,Wei D,Sun X,Liu Q. Shape analysis of sand particles based on Fourier descriptors. Environ Sci Pollut Res. 2023. Mar; 30: 62803-62814. https://doi.org/10.1007/s11356-023-26388-5

Yen Wu W. Shape Recognition Using Segmenting and String Matching. Asian J Appl Sci. 2022 Dec; 10: 528-536. https://doi.org/10.24203/ajas.v10i6.7138

Li Z, Guo B, Meng. Fast Shape Recognition Method Using Feature Richness Based on the Walking Minimum Bounding Rectangle over an Occluded Remote Sensing Target. MDPI. Remote Sens. 2022 Nov; 14(22),1-21. https://doi.org/10.3390/rs14225845

González J, Navarro M, Hernández H. Shape Descriptor Based on Curvature. OALib J. 2022 Mar; 9(3). https://doi.org/10.4236/oalib.1108422

Mirehi N,Tahmasbi M,Targhi A. New graph-based features for shape recognition. Soft Computing: Methodologies and Applications. 2021 Mar; 25: 7577–7592. https://doi.org/10.1007/s00500-021-05716-2

Paramarthalingam A, Thankanadar M. Extraction of compact boundary normalization based geometric descriptors for affine invariant shape retrieval. WILEY. IET Image Process. 2020 Aug;15: 1093–1104. https://doi.org/10.1049/ipr2.12088

Aswathi A S, India K. Curvature Bag of Words Model for Shape Based Recognition and Image Retrieval. Int J Innov Res Sci Eng Technol .2020 jul; 9: 5469-5505.

Zheng Y, Meng F, Liu J, Guo B, Song Y, Zhang X, et al. Fourier Transform to Group Feature on Generated Coarser Contours for Fast 2D Shape Matching .IEEE Access .2020 may; 8: 90141 – 90152. https://doi.org/10.1109/ACCESS.2020.2994234.

Ahmed M, Aradhya M. 2D Shape Recognition and Retrieval Using Shape Contour Based on the 8-Neighborhood Patterns Matching Technique. Int J. Softw Eng. 2020. Dec; 10:49-60. https://doi.org/10.4018/IJSE.2019070104

Zheng Y, GUO B, Li C, Yan Y. A Weighted Fourier and Wavelet-Like Shape Descriptor Based on IDSC for Object Recognition. MDPI. Symmerty.2019 May; 11: 693. https://doi.org/10.3390/sym11050693

Zheng Y, Guo B, Chen Z, Li C. A Fourier Descriptor of 2D Shapes Based on Multiscale Centroid Contour Distances Used in Object Recognition in Remote Sensing Images. MDPI, Sensors. 2019 Jan; 19: 3-19. https://doi.org/10.3390/s19030486

Rababaah A, Rabaa’I A. Geometric 2D Shapes Recognition with Polar Signature Characterization and Template Matching. Review of Business and Technology Research. 2017; 14(2): 7-12.

Shen Y, Ai T, He Y. A New Approach to Line Simplification Based on Image Processing: A Case Study of Water Area Boundaries. ISPRS Int J Geo-Inf.2018. Jan; 7(2):1-25. https://doi.org/10.3390/ijgi7020041

Visvalingam M, Whyatt’J D. The Douglas-Peucker Algorithm for Line Simplification: Re-evaluation through Visualization. Comput Graph Forum. 2007; 9(3): 213-225. https://doi.org/10.1111/j.1467-8659.1990.tb00398.x

Kolanowsk B, Augustyniak J, Latos D. Cartographic Line Generalization Based on Radius of Curvature Analysis. Geo-Inf.2018;7(12): 1-21. https://doi.org/10.3390/ijgi7120477

Ekdemir S. Efficient Implementation of Polyline Simplification for Large Datasets and Usability Evaluation. Master. Sweden: Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology :2011. 1-53

Wang W, Yang W, Liu Y, Sun R, Hu J, Yang L. Segmented Douglas-Peucker Algorithm Based on the Node Importance. KSII Trans. Internet Inf. Syst.2020;1562:1577. http://doi.org/10.3837/tiis.2020.04.009

Li Z, Xin Q, Sun Y, Cao M. A Deep Learning-Based Framework for Automated Extraction of Building Footprint Polygons from Very High-Resolution Aerial Imagery. MDPI, Remote Sens. 2021;13(18):1-25. https://doi.org/10.3390/rs13183630.

Jung LIM S. A Chain-Based Wireless Sensor Network Model Using the Douglas-Peucker Algorithm in the IoT Environment. Tehnički vjesnik. 2021; 28(6): 1825-1832. https://doi.org/10.17559/TV-20200916075229.

LIU.j, LiH, Yang Z, WU K, Liu Y, Liu A , Adaptive Douglas-Peucker Algorithm With Automatic Thresholding for AIS-Based Vessel Trajectory Compression. IEEE Access. 2019;7: 150677 – 150692. https://doi.org/10.1109/ACCESS.2019.2947111

Zambrano G, Sentí V. Comparison analysis on noise reduction in GPS trajectories simplification. 19th LACCEI. 2021:1-6 https://doi.org/10.18687/LACCEI2021.1.1.96

Tienaah T, Stefanakis E, Coleman, D. Contextual Douglas-Peucker Simplification. Geomatica. 2015; 69: 327-338. https://doi.org/10.5623/cig2015-306

Mirvahabi1 S, Abbaspour1 R, Claramunt C. A Flexible Trajectory Compression Algorithm for Multi-Modal Transportation. ISPRS. Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2023; X-4/W1-2022: 501-508. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-501-2023.

Liu B, Liu X, Li D, Shi Y, Fernandez G, Wang Y. A Vector Line Simplification Algorithm Based on the Douglas–Peucker Algorithm, Monotonic Chains and Dichotomy. MDPI. Geo-Inf. 2020;9(4):1-14. https://doi.org/10.3390/ijgi9040251.

Devikanniga1 D, Ramu1 A, Haldorai A. Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm. EAI Endorsed Trans Energy Web.. 2020; 7(29):1-10. http://dx.doi.org/10.4108/eai.13-7-2018.164177.

Deiss L, Margenotb A, Culman S, Demyan M. Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy. Geoderma. 2020;365: 1-12 http://dx.doi.org/10.1016/j.geoderma.2020.114227.

Yassin1 B, Mohamed C, Yassine3 A. A Nonlinear Support Vector Machine Analysis Using Kernel Functions for Nature and Medicine. EDP Sciences. https://doi.org/10.1051/e3sconf/202131901103.

Mahdi G. A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset. Baghdad Sci J. .2020; 17(4): 1255-1266. https://orcid.org/0000-0003-4870-4034.

Jun z. The Development and Application of Support Vector Machine. IOP. J Phys. 2021: 1748: 1742-1748. https://doi.org/10.1088/1742-6596/1748/5/052006

Khanday A, Khan Q, Rabani S. Detecting Textual Propaganda Using Machine Learning Techniques. Baghdad Sci J. 2021; 18(1):199-209. https://doi.org/10.21123/bsj.2021.18.1.0199

Kumar S, Samriya J K, Yadav A S, Kumar M. To improve scalability with Boolean matrix using efficient gossip failure detection and consensus algorithm for PeerSim simulator in IoT environmentInt. J Inf. Technol. 2022; 14(5): 2297-2307. https://doi.org/10.1007/s41870-022-00989-8