{"id":"https://openalex.org/W2513919867","doi":"https://doi.org/10.1109/icip.2016.7533009","title":"A sparse sample collection and representation method using re-weighting and dynamically updating OMP for fish tracking","display_name":"A sparse sample collection and representation method using re-weighting and dynamically updating OMP for fish tracking","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2513919867","doi":"https://doi.org/10.1109/icip.2016.7533009","mag":"2513919867"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7533009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7533009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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/A5103446152","display_name":"Yi-Hao Hsiao","orcid":null},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yi-Hao Hsiao","raw_affiliation_strings":["Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038569083","display_name":"Chien-Chang Chen","orcid":"https://orcid.org/0000-0001-6974-2422"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chaur-Chin Chen","raw_affiliation_strings":["Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4803,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.7470047,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3494","last_page":"3497"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9785000085830688,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9771999716758728,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.7197375297546387},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7014898061752319},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6220493316650391},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5703495740890503},{"id":"https://openalex.org/keywords/fish-actinopterygii","display_name":"Fish <Actinopterygii>","score":0.5615285038948059},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5278366208076477},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5063196420669556},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.49571382999420166},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4311600923538208},{"id":"https://openalex.org/keywords/fishery","display_name":"Fishery","score":0.10682207345962524},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.06850501894950867},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.05466967821121216},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.049450814723968506},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.048681050539016724}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7197375297546387},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7014898061752319},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6220493316650391},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5703495740890503},{"id":"https://openalex.org/C2909208804","wikidata":"https://www.wikidata.org/wiki/Q127282","display_name":"Fish <Actinopterygii>","level":2,"score":0.5615285038948059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5278366208076477},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5063196420669556},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.49571382999420166},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4311600923538208},{"id":"https://openalex.org/C505870484","wikidata":"https://www.wikidata.org/wiki/Q180538","display_name":"Fishery","level":1,"score":0.10682207345962524},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.06850501894950867},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.05466967821121216},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.049450814723968506},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.048681050539016724},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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:10.1109/icip.2016.7533009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7533009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","score":0.8799999952316284,"id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2069057506","https://openalex.org/W2095978736","https://openalex.org/W2102625004","https://openalex.org/W2119667497","https://openalex.org/W2127271355","https://openalex.org/W2129812935","https://openalex.org/W2165037244","https://openalex.org/W2167613050","https://openalex.org/W2289917018","https://openalex.org/W2536750559","https://openalex.org/W6684332838"],"related_works":["https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2049003611","https://openalex.org/W2127804977","https://openalex.org/W2108418243","https://openalex.org/W164103134","https://openalex.org/W2787352659","https://openalex.org/W1970611213","https://openalex.org/W4206560911","https://openalex.org/W4372260270"],"abstract_inverted_index":{"Tracking":[0],"fish":[1,16,50,245],"in":[2],"their":[3],"natural":[4],"environment":[5],"is":[6,18,32,179],"an":[7],"important":[8],"aspect":[9],"of":[10,29,55,69,120,128,168],"marine":[11],"ecosystem":[12],"research.":[13],"However,":[14],"real-world":[15,195],"tracking":[17,102,185,238],"challenging":[19],"due":[20],"to":[21,33,93],"unconstrained":[22],"environments":[23],"and":[24,39,58,72,98,123,138,159,208,222,243],"complex":[25],"scenarios.":[26],"The":[27,52,63,84,126,146,172],"purpose":[28],"this":[30],"study":[31],"develop":[34],"a":[35,95,116],"sparse":[36,59,107,117,169],"sample":[37,56,60,64,108,114,174],"collection":[38,57,65],"representation":[40,61,109],"method":[41,82,144,191,213,234],"(SSCR)":[42],"based":[43],"on":[44],"the":[45,77,101,106,129,156,166,176,182,189,201,204,211,215],"compressive":[46,223],"sensing":[47],"concept":[48,89],"for":[49,90],"tracking.":[51,246],"SSCR":[53,190],"consists":[54],"procedures.":[62],"procedure":[66,110],"obtains":[67],"sets":[68],"positive,":[70],"negative,":[71],"predictive":[73,113,130,173],"samples":[74,131],"by":[75],"using":[76,134,192],"proposed":[78,136,233],"speed-up":[79],"background":[80,96],"modeling":[81],"(SuBM).":[83],"SuBM":[85,202],"adopts":[86],"nonparametric":[87],"histogram":[88],"each":[91,112],"pixel":[92],"build":[94],"model,":[97],"efficiently":[99,164,236],"accelerates":[100,244],"speed.":[103],"In":[104],"addition,":[105],"represents":[111],"as":[115,181],"linear":[118],"combination":[119],"all":[121],"positive":[122],"negative":[124,162],"samples.":[125],"weights":[127],"are":[132],"computed":[133],"our":[135,232],"re-weighting":[137,155],"dynamically":[139,160],"updating":[140,161],"orthogonal":[141,216],"matching":[142,217,225],"pursuit":[143,218,226],"(RwDuOMP).":[145],"RwDuOMP,":[147],"which":[148],"includes":[149],"three":[150],"concepts":[151],"(picking":[152],"extra":[153],"samples,":[154,158],"picked":[157],"samples),":[163],"improves":[165],"performance":[167],"signal":[170],"reconstruction.":[171],"with":[175,203,214],"maximum":[177],"weight":[178],"regarded":[180],"target":[183],"object":[184],"result.":[186],"We":[187],"evaluate":[188],"several":[193],"complicated":[194],"underwater":[196],"sequences.":[197],"Furthermore,":[198],"we":[199],"compare":[200,210],"Gaussian":[205],"Mixture":[206],"Model,":[207],"also":[209],"RwDuOMP":[212],"(OMP),":[219],"regularized":[220],"OMP,":[221],"sampling":[224],"methods.":[227],"Experimental":[228],"results":[229,239],"indicate":[230],"that":[231],"achieves":[235],"higher":[237],"than":[240],"other":[241],"methods,":[242]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
