{"id":"https://openalex.org/W4292949594","doi":"https://doi.org/10.1109/icip46576.2022.9897734","title":"Fully Trainable Gaussian Derivative Convolutional Layer","display_name":"Fully Trainable Gaussian Derivative Convolutional Layer","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4292949594","doi":"https://doi.org/10.1109/icip46576.2022.9897734"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897734","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/A5049185819","display_name":"Valentin Penaud--Polge","orcid":null},"institutions":[{"id":"https://openalex.org/I174015415","display_name":"Morpho (United States)","ror":"https://ror.org/05q5em355","country_code":"US","type":"company","lineage":["https://openalex.org/I174015415"]},{"id":"https://openalex.org/I2746051580","display_name":"Universit\u00e9 Paris Sciences et Lettres","ror":"https://ror.org/013cjyk83","country_code":"FR","type":"education","lineage":["https://openalex.org/I2746051580"]},{"id":"https://openalex.org/I70768539","display_name":"\u00c9cole Nationale Sup\u00e9rieure des Mines de Paris","ror":"https://ror.org/04y8cs423","country_code":"FR","type":"education","lineage":["https://openalex.org/I190752583","https://openalex.org/I2746051580","https://openalex.org/I70768539"]}],"countries":["FR","US"],"is_corresponding":true,"raw_author_name":"Valentin Penaud--Polge","raw_affiliation_strings":["PSL University Center for Mathematical Morphology,Mines Paris","Mines Paris, PSL University Center for Mathematical Morphology"],"affiliations":[{"raw_affiliation_string":"PSL University Center for Mathematical Morphology,Mines Paris","institution_ids":["https://openalex.org/I70768539","https://openalex.org/I2746051580"]},{"raw_affiliation_string":"Mines Paris, PSL University Center for Mathematical Morphology","institution_ids":["https://openalex.org/I70768539","https://openalex.org/I2746051580","https://openalex.org/I174015415"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081107954","display_name":"Santiago Velasco-Forero","orcid":"https://orcid.org/0000-0002-2438-1747"},"institutions":[{"id":"https://openalex.org/I174015415","display_name":"Morpho (United States)","ror":"https://ror.org/05q5em355","country_code":"US","type":"company","lineage":["https://openalex.org/I174015415"]},{"id":"https://openalex.org/I2746051580","display_name":"Universit\u00e9 Paris Sciences et Lettres","ror":"https://ror.org/013cjyk83","country_code":"FR","type":"education","lineage":["https://openalex.org/I2746051580"]},{"id":"https://openalex.org/I70768539","display_name":"\u00c9cole Nationale Sup\u00e9rieure des Mines de Paris","ror":"https://ror.org/04y8cs423","country_code":"FR","type":"education","lineage":["https://openalex.org/I190752583","https://openalex.org/I2746051580","https://openalex.org/I70768539"]}],"countries":["FR","US"],"is_corresponding":false,"raw_author_name":"Santiago Velasco-Forero","raw_affiliation_strings":["PSL University Center for Mathematical Morphology,Mines Paris","Mines Paris, PSL University Center for Mathematical Morphology"],"affiliations":[{"raw_affiliation_string":"PSL University Center for Mathematical Morphology,Mines Paris","institution_ids":["https://openalex.org/I70768539","https://openalex.org/I2746051580"]},{"raw_affiliation_string":"Mines Paris, PSL University Center for Mathematical Morphology","institution_ids":["https://openalex.org/I70768539","https://openalex.org/I2746051580","https://openalex.org/I174015415"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112874367","display_name":"Jesus Angulo","orcid":null},"institutions":[{"id":"https://openalex.org/I174015415","display_name":"Morpho (United States)","ror":"https://ror.org/05q5em355","country_code":"US","type":"company","lineage":["https://openalex.org/I174015415"]},{"id":"https://openalex.org/I2746051580","display_name":"Universit\u00e9 Paris Sciences et Lettres","ror":"https://ror.org/013cjyk83","country_code":"FR","type":"education","lineage":["https://openalex.org/I2746051580"]},{"id":"https://openalex.org/I70768539","display_name":"\u00c9cole Nationale Sup\u00e9rieure des Mines de Paris","ror":"https://ror.org/04y8cs423","country_code":"FR","type":"education","lineage":["https://openalex.org/I190752583","https://openalex.org/I2746051580","https://openalex.org/I70768539"]}],"countries":["FR","US"],"is_corresponding":false,"raw_author_name":"Jesus Angulo","raw_affiliation_strings":["PSL University Center for Mathematical Morphology,Mines Paris","Mines Paris, PSL University Center for Mathematical Morphology"],"affiliations":[{"raw_affiliation_string":"PSL University Center for Mathematical Morphology,Mines Paris","institution_ids":["https://openalex.org/I70768539","https://openalex.org/I2746051580"]},{"raw_affiliation_string":"Mines Paris, PSL University Center for Mathematical Morphology","institution_ids":["https://openalex.org/I70768539","https://openalex.org/I2746051580","https://openalex.org/I174015415"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5049185819"],"corresponding_institution_ids":["https://openalex.org/I174015415","https://openalex.org/I2746051580","https://openalex.org/I70768539"],"apc_list":null,"apc_paid":null,"fwci":0.7162,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.7938693,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2421","last_page":"2425"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9990000128746033,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9988999962806702,"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/kernel","display_name":"Kernel (algebra)","score":0.7252669930458069},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.7202708721160889},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6469032168388367},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6467447876930237},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.6193035840988159},{"id":"https://openalex.org/keywords/derivative","display_name":"Derivative (finance)","score":0.5483234524726868},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5187280774116516},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49172618985176086},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46504485607147217},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.4399510622024536},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4339964687824249},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.42592933773994446},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3813903331756592},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27920180559158325},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2471831738948822},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.09763345122337341},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.077874094247818},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.0673595666885376}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7252669930458069},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.7202708721160889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6469032168388367},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6467447876930237},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.6193035840988159},{"id":"https://openalex.org/C111771559","wikidata":"https://www.wikidata.org/wiki/Q66295","display_name":"Derivative (finance)","level":2,"score":0.5483234524726868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5187280774116516},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49172618985176086},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46504485607147217},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.4399510622024536},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4339964687824249},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.42592933773994446},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3813903331756592},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27920180559158325},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2471831738948822},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.09763345122337341},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.077874094247818},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0673595666885376},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"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/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897734","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1984757472","https://openalex.org/W2010548775","https://openalex.org/W2022735534","https://openalex.org/W2035444637","https://openalex.org/W2105326322","https://openalex.org/W2108598243","https://openalex.org/W2123340620","https://openalex.org/W2141419171","https://openalex.org/W2161004254","https://openalex.org/W2549047603","https://openalex.org/W2693096534","https://openalex.org/W2750384547","https://openalex.org/W2962737122","https://openalex.org/W2963107874","https://openalex.org/W2990407775","https://openalex.org/W2997296861","https://openalex.org/W3014382468","https://openalex.org/W3035421056","https://openalex.org/W3101188641","https://openalex.org/W3108410618","https://openalex.org/W3109158212","https://openalex.org/W3203623872","https://openalex.org/W4288364614","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6743688258","https://openalex.org/W6761414791","https://openalex.org/W6770705036"],"related_works":["https://openalex.org/W2964954556","https://openalex.org/W3034421924","https://openalex.org/W4386858688","https://openalex.org/W2982536526","https://openalex.org/W4380302312","https://openalex.org/W3008689640","https://openalex.org/W4385338604","https://openalex.org/W3081626085","https://openalex.org/W2375370983","https://openalex.org/W2369557298"],"abstract_inverted_index":{"The":[0,76],"Gaussian":[1,38,60],"kernel":[2],"and":[3,58,90,107],"its":[4],"derivatives":[5],"have":[6],"already":[7],"been":[8],"employed":[9],"for":[10,104,109],"Convolutional":[11],"Neural":[12],"Networks":[13],"in":[14,67,97],"several":[15,31],"previous":[16,68,88],"works.":[17],"Most":[18],"of":[19,33],"these":[20],"papers":[21],"proposed":[22,81],"to":[23,87],"compute":[24],"filters":[25],"by":[26],"linearly":[27],"combining":[28],"one":[29],"or":[30,35,41],"bases":[32],"fixed":[34],"slightly":[36],"trainable":[37],"kernels":[39,62],"with":[40],"without":[42],"their":[43,73],"derivatives.":[44],"In":[45],"this":[46],"article,":[47],"we":[48],"propose":[49],"a":[50],"high-level":[51],"configurable":[52],"layer":[53,82],"based":[54],"on":[55],"anisotropic,":[56],"oriented":[57],"shifted":[59],"derivative":[61],"which":[63],"generalize":[64],"notions":[65],"encountered":[66],"related":[69],"works":[70,89],"while":[71],"keeping":[72],"main":[74],"advantage.":[75],"results":[77],"show":[78],"that":[79,91],"the":[80],"has":[83],"competitive":[84],"performance":[85],"compared":[86],"it":[92],"can":[93],"be":[94],"successfully":[95],"included":[96],"common":[98],"deep":[99],"architectures":[100],"such":[101],"as":[102],"VGG16":[103],"image":[105,110],"classification":[106],"U-net":[108],"segmentation.":[111]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}
