{"id":"https://openalex.org/W7133339468","doi":"https://doi.org/10.48550/arxiv.2603.00127","title":"Segmenting Low-Contrast XCTs of Concretes: An Unsupervised Approach","display_name":"Segmenting Low-Contrast XCTs of Concretes: An Unsupervised Approach","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7133339468","doi":"https://doi.org/10.48550/arxiv.2603.00127"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00127","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00127","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.00127","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127901001","display_name":"Kaustav Das","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Das, Kaustav","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040104207","display_name":"G. Rauchs","orcid":"https://orcid.org/0000-0002-0159-9946"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rauchs, Gaston","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128002847","display_name":"Jan Sykora","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sykora, Jan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127914774","display_name":"Anna Kucerova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kucerova, Anna","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5127901001"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.7642999887466431,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.7642999887466431,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10036","display_name":"Advanced Neural Network Applications","score":0.059700001031160355,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.0406000018119812,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6948999762535095},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6633999943733215},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5914999842643738},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5741000175476074},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.545199990272522},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5360999703407288},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4571000039577484},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.427700012922287}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7822999954223633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7566999793052673},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6948999762535095},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6633999943733215},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5914999842643738},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5741000175476074},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.545199990272522},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5360999703407288},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.427700012922287},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40709999203681946},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33009999990463257},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3197999894618988},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.298799991607666},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00127","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00127","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.00127","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00127","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,129],"work":[1],"tests":[2],"a":[3,9,28,59,91,126,135],"self-annotation-based":[4],"unsupervised":[5,155],"methodology":[6,157],"for":[7,15,31,62,79,166],"training":[8,73,156],"convolutional":[10],"neural":[11],"network":[12],"(CNN)":[13],"model":[14,132],"semantic":[16,63],"segmentation":[17,64],"of":[18,24,40,125,144,153],"X-ray":[19,37],"computed":[20],"tomography":[21],"(XCT)":[22],"scans":[23],"concretes.":[25],"Concrete":[26],"poses":[27],"unique":[29],"challenge":[30],"XCT":[32,160],"imaging":[33],"due":[34],"to":[35,85,101,113,133],"similar":[36,104,146],"attenuation":[38],"coefficients":[39],"aggregates":[41],"and":[42,110,141,162],"mortar,":[43],"resulting":[44],"in":[45,51,65,107,117,138],"low-contrast":[46],"between":[47],"the":[48,52,114,118,122,131,139,151,154],"two":[49],"phases":[50],"ensuing":[53],"images.":[54],"While":[55],"CNN-based":[56,127],"models":[57],"are":[58,83],"proven":[60],"technique":[61,93],"such":[66],"challenging":[67],"cases,":[68],"they":[69],"typically":[70],"require":[71],"labeled":[72],"data,":[74],"which":[75,97],"is":[76,94],"often":[77],"unavailable":[78],"new":[80],"datasets":[81,161],"or":[82],"costly":[84],"obtain.":[86],"To":[87],"counter":[88],"that":[89],"limitation,":[90],"self-annotation":[92],"used":[95],"here":[96],"leverages":[98],"superpixel":[99],"algorithms":[100],"identify":[102],"perceptually":[103],"local":[105],"regions":[106],"an":[108],"image":[109,119],"relates":[111],"them":[112],"global":[115],"context":[116],"by":[120],"utilizing":[121],"receptive":[123],"field":[124],"model.":[128],"enables":[130,142],"learn":[134],"global-local":[136],"relationship":[137],"images":[140],"identification":[143],"semantically":[145],"structures.":[147],"We":[148],"therefore":[149],"present":[150],"performance":[152],"on":[158],"our":[159],"discuss":[163],"potential":[164],"avenues":[165],"further":[167],"improvements.":[168]},"counts_by_year":[],"updated_date":"2026-03-04T07:09:34.246503","created_date":"2026-03-04T00:00:00"}