{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T21:11:28Z","timestamp":1766869888785,"version":"3.41.2"},"reference-count":27,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>A method based on the contour coefficient and image processing technology is proposed to better identify the visual elements. This article takes the contour of the image as the recognition feature, summarizes the methods of target contour feature extraction, contour shape representation, and similarity representation, and studies the processing methods of contour edge preserving and denoising, contour feature simplification and description methods, and contour matching methods. This problem can usually be solved by filling out a form. Generally, a simple iterative equation is given to express the direct relationship between the current table and the calculated table values. The dynamic programming algorithm of inner distance shape context, multi-scale convexity convexity, and triangle area representation finds the best sequence correspondence.<\/jats:p>","DOI":"10.1515\/pjbr-2022-0120","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T06:50:44Z","timestamp":1690872644000},"source":"Crossref","is-referenced-by-count":2,"title":["Visual element recognition based on profile coefficient and image processing technology"],"prefix":"10.1515","volume":"14","author":[{"given":"Wei","family":"Luo","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Chongqing Three Gorges Vocational College , Wanzhou, Chongqing , 404000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"2025073006061534172_j_pjbr-2022-0120_ref_001","doi-asserted-by":"crossref","unstructured":"W. Chen, B. Chen, X. Peng, J. Liu, and H. Liu, \u201cTensor RNN with Bayesian nonparametric mixture for radar HRRP modeling and target recognition,\u201d IEEE Trans. Signal. Process, vol. 69, pp. 1995\u20132009, 2021.","DOI":"10.1109\/TSP.2021.3065847"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_002","doi-asserted-by":"crossref","unstructured":"C. Mao, L. Huang, Y. Xiao, F. He, and Y. Liu, \u201cTarget recognition of sar image based on CN-GAN and CNN in complex environment,\u201d IEEE Access, vol. 9, pp. 39608\u201339617, 2021.","DOI":"10.1109\/ACCESS.2021.3064362"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_003","doi-asserted-by":"crossref","unstructured":"H. Wang, \u201cMulti-sensor fusion module for perceptual target recognition for intelligent machine learning visual feature extraction,\u201d IEEE Sens. J., vol. 22, pp. 17431\u201317438, 2021.","DOI":"10.1109\/JSEN.2021.3061207"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_004","doi-asserted-by":"crossref","unstructured":"G. Xiong, Y. Xi, D. Chen, and W. Yu, \u201cDual-polarization SAR ship target recognition based on mini hourglass region extraction and dual-channel efficient fusion network,\u201d IEEE Access, vol. 9, pp. 29078\u201329089, 2021.","DOI":"10.1109\/ACCESS.2021.3058188"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_005","unstructured":"Z. Li, Q. Zhang, T. Long, and B. Zhao, \u201cShip target detection and recognition method on sea surface based on multi-level hybrid network,\u201d J. Beijing Inst. Technol., vol. 30, no. zk, pp. 1\u201310, 2021."},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_006","doi-asserted-by":"crossref","unstructured":"W. Lu, Y. Zhang, C. Yin, C. Lin, and X. Zhang, \u201cA deformation robust ISAR image satellite target recognition method based on PT-CCNN,\u201d IEEE Access, vol. 9, pp. 23432\u201323453, 2021.","DOI":"10.1109\/ACCESS.2021.3056671"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_007","doi-asserted-by":"crossref","unstructured":"X. X. Du, Y. Mu, Z. W. Ye, and Y. J. Zhu, \u201cA passive target recognition method based on led lighting for industrial internet of things,\u201d IEEE Photonics J., vol. 13, pp. 1\u20138, 2021.","DOI":"10.1109\/JPHOT.2021.3098672"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_008","doi-asserted-by":"crossref","unstructured":"Y. Li, L. Du, and J. Chen, \u201cOnline factor analysis model with Kullback-Leibler constraint for satellite target recognition,\u201d IEEE Sens. J., vol. 21, pp. 5322\u20135330, 2021.","DOI":"10.1109\/JSEN.2021.3072404"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_009","doi-asserted-by":"crossref","unstructured":"Y. Liu, X. Ma, X. Li, and C. Zhang, \u201cTwo-stage image smoothing based on edge-patch histogram equalisation and patch decomposition,\u201d IET Image Process. vol. 14, no. 6, pp. 1132\u20131140, 2020.","DOI":"10.1049\/iet-ipr.2019.0484"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_010","unstructured":"Z. A. Liu, Y. K. Hou, J. Xu, X. T. Zhen, and M. M. Cheng, \u201cNon-local image smoothing with objective evaluation,\u201d IEEE Trans. Multimed., vol. 99, p. 1, 2020."},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_011","doi-asserted-by":"crossref","unstructured":"L. Chen and G. Fu, \u201cStructure-preserving image smoothing with semantic cues,\u201d Vis. Comput., vol. 36, no. 10\u201312, pp. 1\u201311, 2020.","DOI":"10.1007\/s00371-020-01950-1"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_012","doi-asserted-by":"crossref","unstructured":"A. Xh, A. Lf, B. Hr, C. Xc, and C. Zl, \u201cRetinal optical coherence tomography image classification with label smoothing generative adversarial network - sciencedirect,\u201d Neurocomputing, vol. 405, pp. 37\u201347, 2020.","DOI":"10.1016\/j.neucom.2020.04.044"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_013","doi-asserted-by":"crossref","unstructured":"K. T. Ahmed, H. Afzal, M. R. Mufti, A. Mehmood, and G. S. Choi, \u201cDeep image sensing & retrieval using suppression, scale spacing & division, interpolation and spatial color coordinates with bag of words for large and complex datasets,\u201d IEEE Access, vol. 8, pp. 90351\u201390379, 2020.","DOI":"10.1109\/ACCESS.2020.2993721"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_014","doi-asserted-by":"crossref","unstructured":"D. Chen, Q. Fan, J. Liao, A. Aviles-Rivero, and G. Hua, \u201cControllable image processing via adaptive filterbank pyramid,\u201d IEEE Trans. Image Process, vol. 29, pp. 8043\u20138054, 2020.","DOI":"10.1109\/TIP.2020.3009844"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_015","doi-asserted-by":"crossref","unstructured":"X. Liu, \u201cResearch on intelligent visual image feature region acquisition algorithm in internet of things framework - sciencedirect,\u201d Comput. Commun., vol. 151, pp. 299\u2013305, 2020.","DOI":"10.1016\/j.comcom.2020.01.008"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_016","doi-asserted-by":"crossref","unstructured":"H. Song, J. Yun, H. Li, M. Zheng, and A. Fang, \u201cAn efficient and effective model based on mean positive examples for social image annotation,\u201d IEEE Access, vol. 8, pp. 210695\u2013210708, 2020.","DOI":"10.1109\/ACCESS.2020.3039625"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_017","doi-asserted-by":"crossref","unstructured":"E. Silva, J. Costa, and J. Schleicher, \u201cImage-guided raytracing and its applications,\u201d Geophysics, vol. 86, no. 3, pp. 1\u201344, 2021.","DOI":"10.1190\/geo2020-0642.1"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_018","doi-asserted-by":"crossref","unstructured":"I. E. Tabrizi, A. Kefal, J. Zanjani, and M. Yildiz, \u201cDamage growth and failure detection in hybrid fiber composites using experimental in-situ optical strain measurements and smoothing element analysis,\u201d Int. J. Damage Mech., vol. 31, no. 4, pp. 479\u2013507, 2022.","DOI":"10.1177\/10567895211045121"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_019","doi-asserted-by":"crossref","unstructured":"D. Qiu, L. Zheng, J. Zhu, and D. Huang, \u201cMultiple improved residual networks for medical image super-resolution,\u201d Future Gener. Comput. Syst., vol. 116, pp. 200\u2013208, 2021.","DOI":"10.1016\/j.future.2020.11.001"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_020","doi-asserted-by":"crossref","unstructured":"F. Espinosa, T. Deroin, V. Malcot, W. Wang, and F. Jabbour, \u201cHistorical note on the taxonomy of the genus Delphinium L. (Ranunculaceae) with an amended description of its floral morphology,\u201d Adansonia, vol. 43, no. 2, pp. 9\u201318, 2021.","DOI":"10.5252\/adansonia2021v43a"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_021","doi-asserted-by":"crossref","unstructured":"A. Vigneshwaran, C. E. Wetzel, D. M. Williams, and B. Karthick, \u201cA re-description of Fragilaria fonticola hustedt and its varieties, with three new combinations and one new species from India,\u201d Phytotaxa, vol. 453, no. 3, pp. 179\u2013198, 2020.","DOI":"10.11646\/phytotaxa.453.3.2"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_022","doi-asserted-by":"crossref","unstructured":"B. Vijver and L. Ector, \u201cAnalysis of the type material of Synedra perminuta (Bacillariophyceae) with the description of two new fragilaria species from Sweden,\u201d Phytotaxa, vol. 468, no. 1, pp. 89\u2013100, 2020.","DOI":"10.11646\/phytotaxa.468.1.5"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_023","doi-asserted-by":"crossref","unstructured":"Y. Zhang, X. Kou, Z. Song, Y. Fan, M. Usman, and V. Jagota, \u201cResearch on logistics management layout optimization and real-time application based on nonlinear programming,\u201d Nonlinear Eng., vol. 10, no. 1, pp. 526\u2013534, 2021.","DOI":"10.1515\/nleng-2021-0043"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_024","doi-asserted-by":"crossref","unstructured":"G. Veselov, A. Tselykh, A. Sharma, and R. Huang, \u201cSpecial issue on applications of artificial intelligence in evolution of smart cities and societies,\u201d Informatica (Slovenia), vol. 45, no. 5, p. 603, 2021.","DOI":"10.31449\/inf.v45i5.3600"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_025","doi-asserted-by":"crossref","unstructured":"X. Liu and Z. Ahmadi, \u201cH 2O and H 2S adsorption by assistance of a heterogeneous carbon-boron-nitrogen nanocage: Computational study,\u201d Main. Group. Chem., vol. 21, pp. 185\u2013193, 1 Jan. 2022.","DOI":"10.3233\/MGC-210113"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_026","doi-asserted-by":"crossref","unstructured":"J. Jayakumar, B. Nagaraj, S. Chacko, and P. Ajay, \u201cConceptual implementation of artificial intelligent based e-mobility controller in smart city environment,\u201d Wirel. Commun. Mob. Comput., vol. 2021, p. 5325116, 2021.","DOI":"10.1155\/2021\/5325116"},{"key":"2025073006061534172_j_pjbr-2022-0120_ref_027","doi-asserted-by":"crossref","unstructured":"X. Ren, C. Li, X. Ma, F. Chen, H. Wang, and A. Sharma, et al. Design of multi-information fusion based intelligent electrical fire detection system for green buildings. Sustainability, vol. 13, no. 6, p. 3405, 2021.","DOI":"10.3390\/su13063405"}],"container-title":["Paladyn, Journal of Behavioral Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/pjbr-2022-0120\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/pjbr-2022-0120\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T06:09:34Z","timestamp":1753855774000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/pjbr-2022-0120\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,1]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,7,29]]},"published-print":{"date-parts":[[2023,7,29]]}},"alternative-id":["10.1515\/pjbr-2022-0120"],"URL":"https:\/\/doi.org\/10.1515\/pjbr-2022-0120","relation":{},"ISSN":["2081-4836"],"issn-type":[{"type":"electronic","value":"2081-4836"}],"subject":[],"published":{"date-parts":[[2023,1,1]]},"article-number":"20220120"}}