{"id":"https://openalex.org/W7163894034","doi":"https://doi.org/10.48550/arxiv.2606.07333","title":"Varifold Moment Invariants for Sustainable and Explainable Contour Feature Extraction","display_name":"Varifold Moment Invariants for Sustainable and Explainable Contour Feature Extraction","publication_year":2026,"publication_date":"2026-06-05","ids":{"openalex":"https://openalex.org/W7163894034","doi":"https://doi.org/10.48550/arxiv.2606.07333"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.07333","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07333","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.07333","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138175429","display_name":"G. Longari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Longari, G.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042796864","display_name":"J.C. \u00c1lvarez Paiva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paiva, J. -C. Alvarez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138170601","display_name":"A. B. Tumpach","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tumpach, A. B.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.21940000355243683,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.21940000355243683,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.15039999783039093,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.10760000348091125,"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/invariant","display_name":"Invariant (physics)","score":0.6840000152587891},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5687999725341797},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5450000166893005},{"id":"https://openalex.org/keywords/tangent","display_name":"Tangent","score":0.5368000268936157},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.49390000104904175},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.46779999136924744},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.39649999141693115}],"concepts":[{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.6840000152587891},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6281999945640564},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5687999725341797},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5450000166893005},{"id":"https://openalex.org/C138187205","wikidata":"https://www.wikidata.org/wiki/Q131251","display_name":"Tangent","level":2,"score":0.5368000268936157},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.49390000104904175},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4706000089645386},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4359000027179718},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3682999908924103},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29829999804496765},{"id":"https://openalex.org/C112604564","wikidata":"https://www.wikidata.org/wiki/Q7489226","display_name":"Shape analysis (program analysis)","level":3,"score":0.29600000381469727},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.28200000524520874},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.26649999618530273},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2639999985694885}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.07333","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07333","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.07333","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07333","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"We":[0,129],"introduce":[1],"Varifold":[2],"Moments":[3],"Invariants":[4],"(VMI)":[5],"as":[6],"a":[7,75,137,154],"unifying":[8],"framework":[9],"for":[10],"many":[11],"previously":[12],"introduced":[13],"Moment":[14],"Invariants.":[15],"These":[16],"invariants":[17],"are":[18,26],"deeply":[19],"related":[20],"to":[21,48,54,69,73,117,124],"other":[22],"contour":[23],"features":[24,80],"that":[25],"invariant":[27,79],"under":[28],"translations":[29],"and":[30,63,85,149],"rotations,":[31],"like":[32],"Extended":[33],"Gaussian":[34],"Image,":[35],"Elliptic":[36],"Fourier":[37],"Descriptors":[38],"or":[39,102],"Shape":[40],"Distributions.":[41],"The":[42],"advantage":[43],"of":[44,58,66,78,120,140,143,157],"the":[45,56,59,64,96,114,118],"varifold":[46],"approach":[47,132],"moments":[49],"consists":[50],"in":[51,71],"being":[52],"able":[53],"combine":[55],"geometry":[57],"region,":[60],"its":[61],"boundary,":[62],"family":[65],"lines":[67],"tangent":[68],"it,":[70],"order":[72],"create":[74],"substantial":[76],"number":[77,139,156],"with":[81,95,153],"high":[82,151],"discriminating":[83],"power":[84],"clear":[86],"geometric":[87],"meaning.":[88],"By":[89],"coupling":[90],"our":[91,122,131],"VMI":[92],"feature":[93,98],"extraction":[94],"light":[97,127],"classifiers":[99],"Random":[100],"Forest":[101],"Multi-Layer-Perceptron,":[103],"we":[104],"outperform":[105],"state-of-the-art":[106],"approaches":[107],"based":[108],"on":[109,126,133,136],"contours,":[110],"while":[111],"decreasing":[112],"drastically":[113],"computational":[115],"cost":[116],"point":[119],"allowing":[121],"algorithm":[123],"run":[125],"devices.":[128],"tested":[130],"classification":[134],"tasks":[135],"large":[138],"widely-used":[141],"datasets":[142],"various":[144],"types":[145],"(leaves,":[146],"objects,":[147],"cells)":[148],"achieved":[150],"accuracy":[152],"low":[155],"geometrically":[158],"interpretable":[159],"features.":[160]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-09T00:00:00"}