{"id":"https://openalex.org/W4214556651","doi":"https://doi.org/10.1145/3488933.3488950","title":"Threshold Segmentation Based on Fuzzy Kaniadakis Entropy for Criminal Investigation Images","display_name":"Threshold Segmentation Based on Fuzzy Kaniadakis Entropy for Criminal Investigation Images","publication_year":2021,"publication_date":"2021-09-24","ids":{"openalex":"https://openalex.org/W4214556651","doi":"https://doi.org/10.1145/3488933.3488950"},"language":"en","primary_location":{"id":"doi:10.1145/3488933.3488950","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488933.3488950","pdf_url":null,"source":{"id":"https://openalex.org/S4363608564","display_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","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/A5114310126","display_name":"Rong Lan","orcid":"https://orcid.org/0000-0001-6665-2667"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lan Rong","raw_affiliation_strings":["School of Communications and Information Engineering,Xi'an University of Posts and Telecommunications, Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering,Xi'an University of Posts and Telecommunications, Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002173783","display_name":"Gao Xiaoge","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gao Xiaoge","raw_affiliation_strings":["School of Communications and Information Engineering,Xi'an University of Posts and Telecommunications, Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering,Xi'an University of Posts and Telecommunications, Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5114310126"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0653,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.40423387,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"102","last_page":"108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9977999925613403,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9976999759674072,"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/image-segmentation","display_name":"Image segmentation","score":0.7456104159355164},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.7242616415023804},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7091295123100281},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.6362983584403992},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.6109563112258911},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6022733449935913},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5678322315216064},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5601109862327576},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.516925036907196},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5000050067901611},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46626409888267517},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19548967480659485}],"concepts":[{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.7456104159355164},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.7242616415023804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7091295123100281},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.6362983584403992},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.6109563112258911},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6022733449935913},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5678322315216064},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5601109862327576},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.516925036907196},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5000050067901611},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46626409888267517},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19548967480659485},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3488933.3488950","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488933.3488950","pdf_url":null,"source":{"id":"https://openalex.org/S4363608564","display_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1078054840","display_name":null,"funder_award_id":"62071379,62071378","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W51653785","https://openalex.org/W986100872","https://openalex.org/W1880598239","https://openalex.org/W1971557661","https://openalex.org/W1997039624","https://openalex.org/W2047097851","https://openalex.org/W2054831422","https://openalex.org/W2067943751","https://openalex.org/W2068233430","https://openalex.org/W2083970667","https://openalex.org/W2107376108","https://openalex.org/W2133003941","https://openalex.org/W2133059825","https://openalex.org/W2133665775","https://openalex.org/W2141358266","https://openalex.org/W2141957843","https://openalex.org/W2611945846","https://openalex.org/W2766124895","https://openalex.org/W2789289199","https://openalex.org/W2907875344","https://openalex.org/W2974747703","https://openalex.org/W3018391297","https://openalex.org/W3099680030","https://openalex.org/W3112835708","https://openalex.org/W4244191689"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2185902295","https://openalex.org/W2945274617","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W2371519352","https://openalex.org/W4205800335","https://openalex.org/W2386644571","https://openalex.org/W2372421320","https://openalex.org/W2901890255"],"abstract_inverted_index":{"Criminal":[0],"investigation":[1,17,25,54],"image":[2,18,41,55,66,73],"segmentation":[3,22,43,94,119],"is":[4,27,111],"considered":[5],"one":[6],"of":[7,15,115],"the":[8,13,21,58,84,92,108],"most":[9],"important":[10],"tasks":[11],"in":[12,113],"field":[14],"criminal":[16,24,53],"processing.":[19],"However,":[20],"for":[23,52,65],"images":[26],"a":[28],"challenging":[29],"task":[30],"due":[31],"to":[32,91],"natural":[33],"or":[34],"human":[35],"factors.":[36],"In":[37],"this":[38],"paper,":[39],"an":[40],"threshold":[42],"algorithm":[44,110],"based":[45,78],"on":[46,79],"fuzzy":[47,74,86],"Kaniadakis":[48,87],"entropy":[49,88],"was":[50,63,89],"proposed":[51,109],"segmentation.":[56],"Firstly,":[57],"weighted":[59],"least":[60],"squares":[61],"filter":[62],"used":[64],"pre-processing.":[67],"Then,":[68],"by":[69],"using":[70],"membership":[71],"functions,":[72],"sets":[75],"were":[76],"constructed":[77],"restricted":[80],"dissimilarity":[81],"function.":[82],"Finally,":[83],"maximum":[85],"corresponded":[90],"optimal":[93],"threshold.":[95],"The":[96],"experimental":[97],"results":[98],"demonstrate":[99],"that":[100],"compared":[101],"with":[102],"several":[103],"existing":[104],"entropy-based":[105],"thresholding":[106],"algorithms,":[107],"effective":[112],"terms":[114],"visual":[116],"effects":[117],"and":[118],"quality":[120],"measures.":[121]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
