XED.
<div><p>Harnessing the power of artificial intelligence(AI) approaches to innovatively generating the vector graphics of fine-grained patterns has become an important task in image edge extraction, particularly on the domain of intangible cultural heritage (ICH) images where they are typ...
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2025
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| _version_ | 1852019401061040128 |
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| author | Anqi Chen (1950547) |
| author2 | Yicui Peng (21526573) Meng Li (79487) Hao Chen (5190) Chang Liu (35901) Jinrong Hu (5800001) Xiang Wen (6563624) Guo Huang (2388922) |
| author2_role | author author author author author author author |
| author_facet | Anqi Chen (1950547) Yicui Peng (21526573) Meng Li (79487) Hao Chen (5190) Chang Liu (35901) Jinrong Hu (5800001) Xiang Wen (6563624) Guo Huang (2388922) |
| author_role | author |
| dc.creator.none.fl_str_mv | Anqi Chen (1950547) Yicui Peng (21526573) Meng Li (79487) Hao Chen (5190) Chang Liu (35901) Jinrong Hu (5800001) Xiang Wen (6563624) Guo Huang (2388922) |
| dc.date.none.fl_str_mv | 2025-06-11T17:44:28Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0318930.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/XED_/29296480 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> harnessing reliable practical reference proposed method provides pattern recognition techniques machine learning algorithms intangible cultural heritage experimental results show convolutional neural networks reduce image noise xception algorithm based two different types generate vector graphics dimensional vectorial images grained pattern based qiang embroidery patterns image edge extraction vector graphics qiang embroidery image processing image information edge extraction edge detection shape characteristics processing methods local mean innovatively generating important task higher autonomy grained patterns example due concept contained complex edges clearly identified artistic reinterpretation artificial intelligence accurately extract |
| dc.title.none.fl_str_mv | XED. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Harnessing the power of artificial intelligence(AI) approaches to innovatively generating the vector graphics of fine-grained patterns has become an important task in image edge extraction, particularly on the domain of intangible cultural heritage (ICH) images where they are typically fine-grained and having the complex edges. With higher autonomy, the machine learning algorithms are able to accurately extract the image information, understand and convey the concept contained in it. In this paper, we take Qiang embroidery patterns as an example due to containing fine-grained patterns, which is more suitable for the study of image processing and pattern recognition techniques. We firstly adopt appropriate pre-processing methods, improved adaptive median filtering(IAMF) and non-local mean for the two different types of Qiang embroidery patterns to reduce image noise. Then, the Xception algorithm based on convolutional neural networks(CNNs) is used for edge detection and extraction to generate vector graphics of the patterns. Experimental results show that Qiang embroidery patterns, after denoising and edge extraction, can be clearly identified the shape characteristics of the patterns. Based on this approach, the images can be converted into vector graphics for the digital preservation and further artistic reinterpretation. The use of the Xception algorithm effectively solves the problem of extraction of Qiang embroidery in two-dimensional vectorial images. In addition, our proposed method provides a reliable practical reference for the preservation of other related ICH images.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_54dffd0a7a845cbf60ccf7ed734cc2bb |
| identifier_str_mv | 10.1371/journal.pone.0318930.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29296480 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | XED.Anqi Chen (1950547)Yicui Peng (21526573)Meng Li (79487)Hao Chen (5190)Chang Liu (35901)Jinrong Hu (5800001)Xiang Wen (6563624)Guo Huang (2388922)Science PolicyPlant BiologyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> harnessingreliable practical referenceproposed method providespattern recognition techniquesmachine learning algorithmsintangible cultural heritageexperimental results showconvolutional neural networksreduce image noisexception algorithm basedtwo different typesgenerate vector graphicsdimensional vectorial imagesgrained pattern basedqiang embroidery patternsimage edge extractionvector graphicsqiang embroideryimage processingimage informationedge extractionedge detectionshape characteristicsprocessing methodslocal meaninnovatively generatingimportant taskhigher autonomygrained patternsexample dueconcept containedcomplex edgesclearly identifiedartistic reinterpretationartificial intelligenceaccurately extract<div><p>Harnessing the power of artificial intelligence(AI) approaches to innovatively generating the vector graphics of fine-grained patterns has become an important task in image edge extraction, particularly on the domain of intangible cultural heritage (ICH) images where they are typically fine-grained and having the complex edges. With higher autonomy, the machine learning algorithms are able to accurately extract the image information, understand and convey the concept contained in it. In this paper, we take Qiang embroidery patterns as an example due to containing fine-grained patterns, which is more suitable for the study of image processing and pattern recognition techniques. We firstly adopt appropriate pre-processing methods, improved adaptive median filtering(IAMF) and non-local mean for the two different types of Qiang embroidery patterns to reduce image noise. Then, the Xception algorithm based on convolutional neural networks(CNNs) is used for edge detection and extraction to generate vector graphics of the patterns. Experimental results show that Qiang embroidery patterns, after denoising and edge extraction, can be clearly identified the shape characteristics of the patterns. Based on this approach, the images can be converted into vector graphics for the digital preservation and further artistic reinterpretation. The use of the Xception algorithm effectively solves the problem of extraction of Qiang embroidery in two-dimensional vectorial images. In addition, our proposed method provides a reliable practical reference for the preservation of other related ICH images.</p></div>2025-06-11T17:44:28ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0318930.s001https://figshare.com/articles/dataset/XED_/29296480CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292964802025-06-11T17:44:28Z |
| spellingShingle | XED. Anqi Chen (1950547) Science Policy Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> harnessing reliable practical reference proposed method provides pattern recognition techniques machine learning algorithms intangible cultural heritage experimental results show convolutional neural networks reduce image noise xception algorithm based two different types generate vector graphics dimensional vectorial images grained pattern based qiang embroidery patterns image edge extraction vector graphics qiang embroidery image processing image information edge extraction edge detection shape characteristics processing methods local mean innovatively generating important task higher autonomy grained patterns example due concept contained complex edges clearly identified artistic reinterpretation artificial intelligence accurately extract |
| status_str | publishedVersion |
| title | XED. |
| title_full | XED. |
| title_fullStr | XED. |
| title_full_unstemmed | XED. |
| title_short | XED. |
| title_sort | XED. |
| topic | Science Policy Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> harnessing reliable practical reference proposed method provides pattern recognition techniques machine learning algorithms intangible cultural heritage experimental results show convolutional neural networks reduce image noise xception algorithm based two different types generate vector graphics dimensional vectorial images grained pattern based qiang embroidery patterns image edge extraction vector graphics qiang embroidery image processing image information edge extraction edge detection shape characteristics processing methods local mean innovatively generating important task higher autonomy grained patterns example due concept contained complex edges clearly identified artistic reinterpretation artificial intelligence accurately extract |