{"id":"https://openalex.org/W3117603076","doi":"https://doi.org/10.23919/eusipco47968.2020.9287366","title":"A Deep Learning Method with CRF for Instance Segmentation of Metal-Organic Frameworks in Scanning Electron Microscopy Images","display_name":"A Deep Learning Method with CRF for Instance Segmentation of Metal-Organic Frameworks in Scanning Electron Microscopy Images","publication_year":2020,"publication_date":"2020-12-18","ids":{"openalex":"https://openalex.org/W3117603076","doi":"https://doi.org/10.23919/eusipco47968.2020.9287366","mag":"3117603076"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco47968.2020.9287366","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287366","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","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/A5040590113","display_name":"Ilyes Batatia","orcid":"https://orcid.org/0000-0001-6915-9851"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I142476485","display_name":"\u00c9cole Polytechnique","ror":"https://ror.org/05hy3tk52","country_code":"FR","type":"education","lineage":["https://openalex.org/I142476485","https://openalex.org/I4210145102"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Ilyes Batatia","raw_affiliation_strings":["ENS Paris-Saclay, University Paris Saclay, Cachan, France","Laboratoire PMC, Ecole Polytechnique-CNRS, IP Paris, Palaiseau, France"],"affiliations":[{"raw_affiliation_string":"ENS Paris-Saclay, University Paris Saclay, Cachan, France","institution_ids":["https://openalex.org/I277688954"]},{"raw_affiliation_string":"Laboratoire PMC, Ecole Polytechnique-CNRS, IP Paris, Palaiseau, France","institution_ids":["https://openalex.org/I142476485","https://openalex.org/I1294671590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5040590113"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I142476485","https://openalex.org/I277688954"],"apc_list":null,"apc_paid":null,"fwci":0.16,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.41971883,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"625","last_page":"629"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10096","display_name":"Metal-Organic Frameworks: Synthesis and Applications","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1604","display_name":"Inorganic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10399","display_name":"Hydrocarbon exploration and reservoir analysis","score":0.9847999811172485,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/conditional-random-field","display_name":"Conditional random field","score":0.901587188243866},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8258296251296997},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6912307739257812},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6656268835067749},{"id":"https://openalex.org/keywords/unary-operation","display_name":"Unary operation","score":0.6381416320800781},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6186360120773315},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5889814496040344},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5724657773971558},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5716939568519592},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5227513313293457},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.481229305267334},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46331027150154114},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4127747118473053},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4001000225543976},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1756986379623413}],"concepts":[{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.901587188243866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8258296251296997},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6912307739257812},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6656268835067749},{"id":"https://openalex.org/C78023250","wikidata":"https://www.wikidata.org/wiki/Q657596","display_name":"Unary operation","level":2,"score":0.6381416320800781},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6186360120773315},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5889814496040344},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5724657773971558},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5716939568519592},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5227513313293457},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.481229305267334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46331027150154114},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4127747118473053},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4001000225543976},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1756986379623413},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco47968.2020.9287366","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287366","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","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":22,"referenced_works":["https://openalex.org/W1597178913","https://openalex.org/W1923697677","https://openalex.org/W1964772475","https://openalex.org/W1974966064","https://openalex.org/W1977952172","https://openalex.org/W2054360246","https://openalex.org/W2088112538","https://openalex.org/W2094145140","https://openalex.org/W2116040950","https://openalex.org/W2141939342","https://openalex.org/W2161236525","https://openalex.org/W2216125271","https://openalex.org/W2412782625","https://openalex.org/W2587927892","https://openalex.org/W2784226479","https://openalex.org/W2804860796","https://openalex.org/W2919115771","https://openalex.org/W2952793010","https://openalex.org/W2954727898","https://openalex.org/W2963881378","https://openalex.org/W6640295612","https://openalex.org/W6688789216"],"related_works":["https://openalex.org/W2888918612","https://openalex.org/W2166285859","https://openalex.org/W3148597776","https://openalex.org/W1538206641","https://openalex.org/W2025370853","https://openalex.org/W3211656382","https://openalex.org/W2060412688","https://openalex.org/W1972183506","https://openalex.org/W2592997798","https://openalex.org/W2964954556"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"an":[3,130],"integrated":[4],"method":[5,112,139],"for":[6,144],"recognizing":[7],"special":[8],"crystals,":[9],"called":[10],"metal-organic":[11],"frameworks":[12],"(MOF),":[13],"in":[14,58],"scanning":[15],"electron":[16],"microscopy":[17],"images":[18,121],"(SEM).":[19],"The":[20,62,75,96,111,137],"proposed":[21,138],"approach":[22],"combines":[23],"two":[24],"deep":[25],"learning":[26],"networks":[27],"and":[28,71],"a":[29,103,117],"dense":[30,63],"conditional":[31],"random":[32],"field":[33,94],"(CRF)":[34],"to":[35,54,67,102,153],"perform":[36],"image":[37],"segmentation.":[38],"A":[39],"modified":[40],"Unet-like":[41],"convolutional":[42],"neural":[43],"network":[44],"(CNN),":[45],"incorporating":[46],"dilatation":[47],"techniques":[48],"using":[49,92],"atrous":[50],"convolution,":[51],"is":[52,65,81,90],"designed":[53],"segment":[55],"cluttered":[56],"objects":[57,124],"the":[59,79,84,87,142],"SEM":[60],"image.":[61],"CRF":[64,80],"tailored":[66],"enhance":[68],"object":[69],"boundaries":[70],"recover":[72],"small":[73],"objects.":[74],"unary":[76],"energy":[77,89],"of":[78,119,133,156],"obtained":[82],"from":[83,125],"CNN.":[85],"And":[86],"pairwise":[88],"estimated":[91],"mean":[93],"approximation.":[95],"resulting":[97],"segmented":[98],"regions":[99],"are":[100],"fed":[101],"fully":[104],"connected":[105],"CNN":[106],"that":[107,150],"performs":[108],"instance":[109],"recognition.":[110,136],"has":[113],"been":[114],"trained":[115],"on":[116],"dataset":[118],"500":[120],"with":[122],"3200":[123],"3":[126],"classes.":[127],"Testing":[128],"achieves":[129],"overall":[131],"accuracy":[132],"95.7%":[134],"MOF":[135,157],"opens":[140],"up":[141],"possibility":[143],"developing":[145],"automated":[146],"chemical":[147],"process":[148],"monitoring":[149],"allows":[151],"researchers":[152],"optimize":[154],"conditions":[155],"synthesis.":[158]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
