{"id":"https://openalex.org/W4206160306","doi":"https://doi.org/10.1109/icct52962.2021.9658031","title":"Recognition of Video Time Watermark Based on Water-Wave Connected Domain Segmentation Algorithm","display_name":"Recognition of Video Time Watermark Based on Water-Wave Connected Domain Segmentation Algorithm","publication_year":2021,"publication_date":"2021-10-13","ids":{"openalex":"https://openalex.org/W4206160306","doi":"https://doi.org/10.1109/icct52962.2021.9658031"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct52962.2021.9658031","pdf_url":null,"source":{"id":"https://openalex.org/S4363607878","display_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5015354151","display_name":"Pengfei Meng","orcid":null},"institutions":[],"countries":["CN"],"is_corresponding":false,"raw_author_name":"PengFei Meng","raw_affiliation_strings":["Mogo Auto Intelligence and Telematics Information Technology Co., Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Mogo Auto Intelligence and Telematics Information Technology Co., Ltd, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091391011","display_name":"Shuang-cheng Jia","orcid":"https://orcid.org/0000-0003-2984-973X"},"institutions":[],"countries":["CN"],"is_corresponding":false,"raw_author_name":"ShuangCheng Jia","raw_affiliation_strings":["Mogo Auto Intelligence and Telematics Information Technology Co., Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Mogo Auto Intelligence and Telematics Information Technology Co., Ltd, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100340635","display_name":"Qian Li","orcid":"https://orcid.org/0000-0002-9530-4925"},"institutions":[],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Li","raw_affiliation_strings":["Mogo Auto Intelligence and Telematics Information Technology Co., Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Mogo Auto Intelligence and Telematics Information Technology Co., Ltd, Beijing, China","institution_ids":[]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":58},"biblio":{"volume":"45","issue":null,"first_page":"1123","last_page":"1127"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwriting Recognition and Text Detection","score":0.9998,"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/T10601","display_name":"Handwriting Recognition and Text Detection","score":0.9998,"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/T10388","display_name":"Digital Image Watermarking Techniques","score":0.9983,"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/T12357","display_name":"Digital Image Forgery Detection and Identification","score":0.9969,"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/watermarking","display_name":"Watermarking","score":0.633314},{"id":"https://openalex.org/keywords/scene-text-recognition","display_name":"Scene Text Recognition","score":0.593673},{"id":"https://openalex.org/keywords/text-detection","display_name":"Text Detection","score":0.566301},{"id":"https://openalex.org/keywords/handwriting-recognition","display_name":"Handwriting Recognition","score":0.555778},{"id":"https://openalex.org/keywords/resampling-detection","display_name":"Resampling Detection","score":0.543064},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.44818947}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.79821074},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.74697787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5981474},{"id":"https://openalex.org/C164112704","wikidata":"https://www.wikidata.org/wiki/Q7974348","display_name":"Watermark","level":3,"score":0.58893114},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.57175213},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.55582047},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49462768},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47950417},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46580702},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4609258},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.44818947},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42775083},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23908344},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15938729},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct52962.2021.9658031","pdf_url":null,"source":{"id":"https://openalex.org/S4363607878","display_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/6","score":0.81,"display_name":"Clean water and sanitation"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":9,"referenced_works":["https://openalex.org/W2046611156","https://openalex.org/W2080282572","https://openalex.org/W2093804096","https://openalex.org/W2128998325","https://openalex.org/W2138049212","https://openalex.org/W2142159465","https://openalex.org/W2194187530","https://openalex.org/W2971252057","https://openalex.org/W3048313648"],"related_works":["https://openalex.org/W4205800335","https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W2901890255","https://openalex.org/W2386644571","https://openalex.org/W2372421320","https://openalex.org/W2371519352","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W2055202857"],"abstract_inverted_index":{"Character":[0],"segmentation":[1,18,87,94,111,131,149],"has":[2,237],"always":[3],"been":[4],"a":[5,46,62,136,204],"difficult":[6],"task":[7],"in":[8,114,152,183,234],"the":[9,24,31,55,71,83,89,105,118,127,144,189,195,199,211,215,221,230],"field":[10],"of":[11,33,107,126,207,214],"text":[12],"recognition.":[13],"The":[14,110,179],"common":[15],"connected":[16,85,92,129,147],"domain":[17,86,93,130,148],"algorithm":[19,95,132,150],"not":[20,29],"only":[21,59],"repeatedly":[22],"traverses":[23],"pixels,":[25],"but":[26],"also":[27,103],"does":[28],"solve":[30],"problem":[32],"character":[34,140],"adhesion":[35],"segmentation.":[36,141],"In":[37],"this":[38,115,153,184,235],"work,":[39],"targeted":[40],"at":[41],"these":[42],"problems,":[43],"we":[44],"proposed":[45,113,151,182,216,233],"time":[47,56,74],"watermark":[48,57,75],"recognition":[49,58,180,192,212,223,231],"method,":[50],"which":[51],"can":[52,102],"quickly":[53],"realize":[54],"by":[60],"labeling":[61],"few":[63,243],"sample":[64],"pictures.":[65],"It":[66],"is":[67,186,225],"very":[68,242],"suitable":[69],"for":[70,139],"scene":[72],"where":[73],"needs":[76],"to":[77],"be":[78],"recognized":[79],"quickly.":[80],"Compared":[81,227],"with":[82,188,228],"traditional":[84,128],"algorithm,":[88],"water":[90,145],"wave":[91,146],"segmented":[96],"characters":[97],"more":[98],"accurately,":[99],"and":[100,121,134,162,175,198,220],"it":[101],"handle":[104],"situation":[106],"light":[108],"adhesion.":[109],"method":[112,181,193,217,232],"paper":[116,185],"avoids":[117],"repeated":[119],"stacking":[120],"popping":[122],"or":[123],"recursive":[124],"operations":[125],"mostly,":[133],"provides":[135],"new":[137],"idea":[138],"Based":[142],"on":[143,241],"article,":[154],"four":[155],"different":[156],"image":[157],"processing":[158],"algorithms":[159],"are":[160,209],"compared":[161,187],"studied:":[163],"template":[164],"matching":[165],"[1],":[166],"Hamming":[167],"distance":[168,172],"calculation":[169,173],"[2],":[170],"Euclidean":[171],"[3],":[174],"pixel":[176],"comparison":[177],"calculation.":[178],"CRNN":[190,222],"[4]":[191],"under":[194],"same":[196],"conditions,":[197],"results":[200,240],"show":[201],"that":[202],"when":[203],"small":[205],"number":[206],"samples":[208],"used,":[210],"rate":[213,224],"reaches":[218],"92.3%,":[219],"0.":[226],"CRNN,":[229],"article":[236],"achieved":[238],"good":[239],"samples.":[244]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4206160306","counts_by_year":[],"updated_date":"2024-09-26T18:27:39.306234","created_date":"2022-01-25"}