{"id":"https://openalex.org/W4386764056","doi":"https://doi.org/10.1109/dac56929.2023.10247959","title":"On-Device Unsupervised Image Segmentation","display_name":"On-Device Unsupervised Image Segmentation","publication_year":2023,"publication_date":"2023-07-09","ids":{"openalex":"https://openalex.org/W4386764056","doi":"https://doi.org/10.1109/dac56929.2023.10247959"},"language":"en","primary_location":{"id":"doi:10.1109/dac56929.2023.10247959","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac56929.2023.10247959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 60th ACM/IEEE Design Automation Conference (DAC)","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/A5060451214","display_name":"Junhuan Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Junhuan Yang","raw_affiliation_strings":["George Mason University"],"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090438161","display_name":"Yi Sheng","orcid":"https://orcid.org/0000-0001-9087-1042"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Sheng","raw_affiliation_strings":["George Mason University"],"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101668021","display_name":"Yuzhou Zhang","orcid":"https://orcid.org/0000-0003-0893-6754"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Yuzhou Zhang","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019027088","display_name":"Weiwen Jiang","orcid":"https://orcid.org/0000-0002-9004-487X"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weiwen Jiang","raw_affiliation_strings":["George Mason University"],"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011903902","display_name":"Yang Lei","orcid":"https://orcid.org/0000-0002-3572-0345"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Yang","raw_affiliation_strings":["George Mason University"],"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5060451214"],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":2.4893,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.89852715,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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":0.998199999332428,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6791369318962097},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6355995535850525},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6233629584312439},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5756723284721375},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.48005470633506775},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.47643572092056274},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4469185173511505},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.44048595428466797},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3278038501739502}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6791369318962097},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6355995535850525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6233629584312439},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5756723284721375},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.48005470633506775},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.47643572092056274},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4469185173511505},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.44048595428466797},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3278038501739502}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac56929.2023.10247959","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac56929.2023.10247959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 60th ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.7099999785423279,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W349216298","https://openalex.org/W1745334888","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2019062120","https://openalex.org/W2497320330","https://openalex.org/W2734941459","https://openalex.org/W2770180314","https://openalex.org/W2962914239","https://openalex.org/W2963136578","https://openalex.org/W2980289969","https://openalex.org/W2981994674","https://openalex.org/W3019791076","https://openalex.org/W3042615550","https://openalex.org/W3099193570","https://openalex.org/W3105115497","https://openalex.org/W3132455321","https://openalex.org/W3178193590","https://openalex.org/W4214762187","https://openalex.org/W4283766928","https://openalex.org/W6745842894","https://openalex.org/W6798271659","https://openalex.org/W6838539104"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W1986655823","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W4205800335","https://openalex.org/W2371519352","https://openalex.org/W2386644571","https://openalex.org/W2372421320"],"abstract_inverted_index":{"Along":[0],"with":[1,69],"the":[2,52,108,162,171,176,193,205,260,263,278],"breakthrough":[3],"of":[4,22,31,54,116,204,275],"convolutional":[5],"neural":[6,221],"networks,":[7],"in":[8,17,89,111,237,259],"particular":[9],"encoder-decoder":[10],"and":[11,157],"U-Net,":[12],"learning-based":[13],"segmentation":[14,57,90,125,141,172,179,211,228,284],"has":[15,75],"emerged":[16],"many":[18],"research":[19],"works.":[20],"Most":[21],"them":[23],"are":[24,148],"based":[25],"on":[26,78,130,137,202,249,277],"supervised":[27,117],"learning,":[28],"requiring":[29],"plenty":[30],"annotated":[32,56,134],"data;":[33],"however,":[34,72],"to":[35,67,106,121,150,170,209,269,273],"support":[36],"segmentation,":[37,118],"a":[38,50,64,151,187,226,234,255,291],"label":[39],"for":[40,81,92,98,161,254],"each":[41],"pixel":[42],"is":[43,46,63,86,199],"required,":[44],"which":[45,95,126,191],"obviously":[47],"expensive.":[48],"As":[49],"result,":[51],"issue":[53,110],"lacking":[55],"data":[58,91],"commonly":[59],"exists.":[60],"Continuous":[61],"learning":[62],"promising":[65],"way":[66],"deal":[68],"this":[70,102],"issue;":[71],"it":[73,244],"still":[74],"high":[76,144],"demands":[77],"human":[79],"labor":[80],"annotation.":[82],"What\u2019s":[83,252],"more,":[84,253],"privacy":[85],"highly":[87],"required":[88],"real-world":[93],"applications,":[94],"further":[96,200],"calls":[97],"on-device":[99],"learning.":[100],"In":[101,183],"paper,":[103],"we":[104,119,160,185],"aim":[105],"resolve":[107],"above":[109],"an":[112],"alternative":[113],"way:":[114],"Instead":[115],"propose":[120],"develop":[122],"efficient":[123],"unsupervised":[124,178,223],"can":[127,142,218,232,282],"be":[128,267],"executed":[129],"edge":[131],"devices":[132],"without":[133],"data.":[135],"Based":[136],"our":[138],"observation":[139],"that":[140,216],"obtain":[143,210,283],"performance":[145],"when":[146],"pixels":[147],"mapped":[149],"high-dimension":[152,207],"space":[153],"using":[154],"their":[155],"position":[156],"color":[158],"information,":[159],"first":[163],"time":[164],"bring":[165],"brain-inspired":[166],"hyperdimensional":[167],"computing":[168],"(HDC)":[169],"task.":[173],"We":[174],"build":[175],"HDC-based":[177],"framework,":[180],"namely":[181],"\"SegHDC\".":[182],"SegHDC,":[184],"devise":[186],"novel":[188],"encoding":[189],"approach,":[190],"follows":[192],"Manhattan":[194],"distance.":[195],"A":[196],"clustering":[197],"algorithm":[198],"developed":[201],"top":[203],"encoded":[206],"vectors":[208],"results.":[212],"Experimental":[213],"results":[214,285],"show":[215],"SegHDC":[217,231,281],"significantly":[219],"surpass":[220],"network-based":[222],"segmentation.":[224],"On":[225],"standard":[227],"dataset,":[229,262],"DSB2018,":[230],"achieve":[233],"28.0%":[235],"improvement":[236],"Intersection":[238],"over":[239,246],"Union":[240],"(IoU)":[241],"score;":[242],"meanwhile,":[243],"achieves":[245],"300\u00d7":[247],"speedup":[248],"Raspberry":[250,270],"PI.":[251],"larger":[256],"size":[257],"image":[258],"BBBC005":[261],"existing":[264],"approach":[265],"cannot":[266],"accommodated":[268],"PI":[271],"due":[272],"out":[274],"memory;":[276],"other":[279],"hand,":[280],"within":[286],"3":[287],"minutes":[288],"while":[289],"achieving":[290],"0.9587":[292],"IoU":[293],"score.":[294]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
