{"id":"https://openalex.org/W4386590407","doi":"https://doi.org/10.1109/icip49359.2023.10222905","title":"A Differentiable Gaussian Prototype Layer for Explainable Fruit Segmentation","display_name":"A Differentiable Gaussian Prototype Layer for Explainable Fruit Segmentation","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386590407","doi":"https://doi.org/10.1109/icip49359.2023.10222905"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5015155790","display_name":"Michael Gerstenberger","orcid":"https://orcid.org/0009-0008-1793-5829"},"institutions":[{"id":"https://openalex.org/I2800274787","display_name":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute","ror":"https://ror.org/02tbr6331","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800274787","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Gerstenberger","raw_affiliation_strings":["Fraunhofer HHI,Berlin,Germany","Fraunhofer HHI, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer HHI,Berlin,Germany","institution_ids":["https://openalex.org/I2800274787"]},{"raw_affiliation_string":"Fraunhofer HHI, Berlin, Germany","institution_ids":["https://openalex.org/I2800274787"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047414483","display_name":"Steffen Maa\u00df","orcid":null},"institutions":[{"id":"https://openalex.org/I2800274787","display_name":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute","ror":"https://ror.org/02tbr6331","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800274787","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Steffen Maa\u00df","raw_affiliation_strings":["Fraunhofer HHI,Berlin,Germany","Fraunhofer HHI, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer HHI,Berlin,Germany","institution_ids":["https://openalex.org/I2800274787"]},{"raw_affiliation_string":"Fraunhofer HHI, Berlin, Germany","institution_ids":["https://openalex.org/I2800274787"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009614926","display_name":"Peter Eisert","orcid":"https://orcid.org/0000-0001-8378-4805"},"institutions":[{"id":"https://openalex.org/I2800274787","display_name":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute","ror":"https://ror.org/02tbr6331","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800274787","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Peter Eisert","raw_affiliation_strings":["Fraunhofer HHI,Berlin,Germany","Fraunhofer HHI, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer HHI,Berlin,Germany","institution_ids":["https://openalex.org/I2800274787"]},{"raw_affiliation_string":"Fraunhofer HHI, Berlin, Germany","institution_ids":["https://openalex.org/I2800274787"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020506776","display_name":"Sebastian Bosse","orcid":"https://orcid.org/0000-0002-8104-3904"},"institutions":[{"id":"https://openalex.org/I2800274787","display_name":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute","ror":"https://ror.org/02tbr6331","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800274787","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sebastian Bosse","raw_affiliation_strings":["Fraunhofer HHI,Berlin,Germany","Fraunhofer HHI, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer HHI,Berlin,Germany","institution_ids":["https://openalex.org/I2800274787"]},{"raw_affiliation_string":"Fraunhofer HHI, Berlin, Germany","institution_ids":["https://openalex.org/I2800274787"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800274787"],"apc_list":null,"apc_paid":null,"fwci":1.2116,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83444314,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2665","last_page":"2669"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9969000220298767,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.78731769323349},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.7758539915084839},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6282802224159241},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.6190049052238464},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.602148175239563},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5674566030502319},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5611231923103333},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5597937107086182},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5271888971328735},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.506074845790863},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4493636190891266},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35055011510849},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09939113259315491}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.78731769323349},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7758539915084839},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6282802224159241},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.6190049052238464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.602148175239563},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5674566030502319},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5611231923103333},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5597937107086182},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5271888971328735},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.506074845790863},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4493636190891266},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35055011510849},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09939113259315491},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","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},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip49359.2023.10222905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:publica.fraunhofer.de:publica/508830","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/508830","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2082060620","https://openalex.org/W2811104224","https://openalex.org/W2893918048","https://openalex.org/W2954471638","https://openalex.org/W2963150697","https://openalex.org/W2999206534","https://openalex.org/W3004977026","https://openalex.org/W3103795537","https://openalex.org/W3135579708","https://openalex.org/W3173446152","https://openalex.org/W3194195161","https://openalex.org/W3197368102","https://openalex.org/W3213509545","https://openalex.org/W3215430231","https://openalex.org/W3215928733","https://openalex.org/W4200064219","https://openalex.org/W4283639857","https://openalex.org/W4313194958","https://openalex.org/W4389010732","https://openalex.org/W6753001334","https://openalex.org/W6754568377","https://openalex.org/W6797162190"],"related_works":["https://openalex.org/W3082848404","https://openalex.org/W1979583797","https://openalex.org/W2016864125","https://openalex.org/W2372254676","https://openalex.org/W2080135837","https://openalex.org/W2793679056","https://openalex.org/W4283332091","https://openalex.org/W1975321310","https://openalex.org/W3145050838","https://openalex.org/W1578916557"],"abstract_inverted_index":{"We":[0,88,104],"introduce":[1],"a":[2,83,96,112,124,138,154,170],"Gaussian":[3,34,107],"Prototype":[4,108],"Layer":[5,109],"for":[6,16,69,100,198],"gradient-based":[7,64,171],"prototype":[8,60,157],"learning":[9,196],"and":[10,66,75,122,184],"demonstrate":[11],"two":[12],"novel":[13,97],"network":[14],"architectures":[15],"explainable":[17,101],"segmentation":[18],"one":[19],"of":[20,45,82,153,164,178,187],"which":[21,189],"relies":[22,62],"on":[23,30,63],"region":[24,133],"proposals.":[25,134],"Both":[26],"models":[27,201],"are":[28,50,116],"evaluated":[29],"agricultural":[31],"datasets.":[32],"While":[33],"Mixture":[35],"Models":[36],"(GMMs)":[37],"have":[38],"been":[39],"used":[40,94],"to":[41,77,143,192],"model":[42,113,125],"latent":[43,120],"distributions":[44],"neural":[46,102],"networks":[47],"before,":[48],"they":[49],"typically":[51],"fitted":[52],"using":[53],"the":[54,58,79,119,144,148,151,162,176,182],"EM":[55],"algorithm.":[56],"Instead,":[57],"proposed":[59],"layer":[61,173],"optimization":[65],"hence":[67],"allows":[68,76],"end-to-end":[70,179],"training.":[71],"This":[72],"facilitates":[73],"development":[74],"use":[78],"full":[80],"potential":[81],"trainable":[84],"deep":[85],"feature":[86],"extractor.":[87],"show":[89],"that":[90,159],"it":[91],"can":[92],"be":[93],"as":[95,132,141],"building":[98],"block":[99],"networks.":[103],"employ":[105],"our":[106],"in":[110,118,202],"(1)":[111],"where":[114],"prototypes":[115],"detected":[117],"grid":[121],"(2)":[123],"inspired":[126],"by":[127],"Fast-RCNN":[128],"with":[129,181],"SLIC":[130],"superpixels":[131],"The":[135],"earlier":[136],"achieves":[137],"similar":[139],"performance":[140],"compared":[142],"state-of-the":[145],"art":[146],"while":[147],"latter":[149],"has":[150],"benefit":[152],"more":[155],"precise":[156],"localization":[158],"comes":[160],"at":[161],"cost":[163],"slightly":[165],"lower":[166],"accuracies.":[167],"By":[168],"introducing":[169],"GMM":[172],"we":[174],"combine":[175],"benefits":[177],"training":[180],"simplicity":[183],"theoretical":[185],"foundation":[186],"GMMs":[188],"will":[190],"allow":[191],"adapt":[193],"existing":[194],"semi-supervised":[195],"strategies":[197],"prototypical":[199],"part":[200],"future.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}