{"id":"https://openalex.org/W3198783440","doi":"https://doi.org/10.1109/rcar52367.2021.9517482","title":"Gun model recognition using geometric features of contour image","display_name":"Gun model recognition using geometric features of contour image","publication_year":2021,"publication_date":"2021-07-15","ids":{"openalex":"https://openalex.org/W3198783440","doi":"https://doi.org/10.1109/rcar52367.2021.9517482","mag":"3198783440"},"language":"en","primary_location":{"id":"doi:10.1109/rcar52367.2021.9517482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar52367.2021.9517482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111073425","display_name":"Zhisheng Zhou","orcid":"https://orcid.org/0000-0002-0298-7406"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhisheng Zhou","raw_affiliation_strings":["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101813433","display_name":"Jun Han","orcid":"https://orcid.org/0000-0002-7287-858X"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Han","raw_affiliation_strings":["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100360564","display_name":"Jiaxin Chen","orcid":"https://orcid.org/0009-0003-3813-2068"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaxin Chen","raw_affiliation_strings":["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086350933","display_name":"Yuming Dong","orcid":"https://orcid.org/0000-0002-9279-8245"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuming Dong","raw_affiliation_strings":["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5111073425"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210145761"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16550849,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"1154","last_page":"1157"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9663000106811523,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9663000106811523,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9490000009536743,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.9330000281333923,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7794528007507324},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6356848478317261},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6312940120697021},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5287727117538452},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5010766983032227},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.46904119849205017},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06329646706581116}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7794528007507324},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6356848478317261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6312940120697021},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5287727117538452},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5010766983032227},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.46904119849205017},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06329646706581116},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rcar52367.2021.9517482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar52367.2021.9517482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1586885301","https://openalex.org/W2045010718","https://openalex.org/W2118232540","https://openalex.org/W2159498975","https://openalex.org/W2213892522","https://openalex.org/W2486880883","https://openalex.org/W2540964246","https://openalex.org/W2766324185","https://openalex.org/W2966858883","https://openalex.org/W2974320575","https://openalex.org/W3109200427","https://openalex.org/W4249787253","https://openalex.org/W6728992001"],"related_works":["https://openalex.org/W1891287906","https://openalex.org/W2036807459","https://openalex.org/W2775347418","https://openalex.org/W1969923398","https://openalex.org/W2772917594","https://openalex.org/W2166024367","https://openalex.org/W2755342338","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2058170566"],"abstract_inverted_index":{"Global":[0],"boarder":[1],"customs":[2],"seize":[3],"large":[4,12],"numbers":[5],"of":[6,14,34,106,129,168,181],"illegally":[7],"smuggled":[8],"guns":[9,127,182],"annually,":[10],"including":[11,89],"kinds":[13],"lethal":[15],"aerodynamic":[16],"guns.":[17],"To":[18],"classify":[19,157],"the":[20,25,32,55,79,101,107,113,119,136,152,160,175],"guns'":[21],"types":[22],"and":[23,29,77,94,139,158,202],"recognize":[24,159],"models":[26,132,180],"is":[27,47,110,154],"beneficial":[28],"essential":[30],"for":[31,112],"investigation":[33],"smuggling":[35,207],"cases.":[36],"Therefore,":[37],"an":[38],"automatic":[39,198],"gun":[40,199],"model":[41,61,115,162,200],"recognition":[42,116,201],"system":[43],"with":[44,163],"high":[45,166],"efficiency":[46],"very":[48],"important.":[49],"In":[50],"this":[51,191],"work,":[52],"we":[53],"investigate":[54],"possibility":[56],"that":[57,128,151,174],"identifying":[58],"a":[59,71,146,164],"gun's":[60,114,161],"by":[62,186],"its":[63],"contour":[64,72,80,102,176],"image.":[65],"The":[66],"procedure":[67],"mainly":[68],"involves":[69],"acquiring":[70],"image":[73,187],"using":[74],"back-illuminated":[75],"imaging":[76],"classifying":[78],"region":[81],"based":[82,117],"on":[83,118,197],"geometric":[84,87],"features.":[85],"Four":[86],"features":[88,109],"area,":[90],"circumference,":[91],"maximum":[92],"distance":[93],"Hu":[95],"moment":[96],"in-variants":[97],"are":[98,133,142],"extracted":[99],"from":[100],"region.":[103],"A":[104],"combination":[105],"above":[108],"adopted":[111],"Normalized":[120],"Manhattan":[121],"Distance":[122],"rule.":[123],"79":[124],"water":[125],"bomb":[126],"20":[130],"different":[131,179],"used":[134],"in":[135,206],"verifying":[137],"experiment":[138],"950":[140],"images":[141],"taken":[143],"to":[144,156],"form":[145],"dataset.":[147],"Experimental":[148],"results":[149],"indicate":[150],"method":[153],"able":[155],"remarkably":[165],"accuracy":[167],"larger":[169],"than":[170],"99%,":[171],"which":[172],"suggests":[173],"differences":[177],"between":[178],"can":[183],"be":[184],"detected":[185],"classification.":[188],"We":[189],"expect":[190],"work":[192],"will":[193],"promote":[194],"further":[195],"studies":[196],"find":[203],"potential":[204],"applications":[205],"arms":[208],"investigations.":[209]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
