{"id":"https://openalex.org/W2902532691","doi":"https://doi.org/10.1109/icpr.2018.8545649","title":"Feature Extraction and Grain Segmentation of Sandstone Images Based on Convolutional Neural Networks","display_name":"Feature Extraction and Grain Segmentation of Sandstone Images Based on Convolutional Neural Networks","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2902532691","doi":"https://doi.org/10.1109/icpr.2018.8545649","mag":"2902532691"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5101710538","display_name":"Feng Jiang","orcid":"https://orcid.org/0000-0001-5362-3234"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Feng Jiang","raw_affiliation_strings":["College of Mobile Internet, NUST, Taizhou, China"],"affiliations":[{"raw_affiliation_string":"College of Mobile Internet, NUST, Taizhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061025205","display_name":"Qing Gu","orcid":"https://orcid.org/0000-0002-1112-790X"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Gu","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048868703","display_name":"Huizhen Hao","orcid":"https://orcid.org/0000-0002-4910-763X"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huizhen Hao","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100368070","display_name":"Na Li","orcid":"https://orcid.org/0000-0002-1103-6864"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Na Li","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101710538"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5016,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.65562021,"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":"2636","last_page":"2641"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12282","display_name":"Mineral Processing and Grinding","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9884999990463257,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.71080082654953},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6922272443771362},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6781560778617859},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6657597422599792},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5114427804946899},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5082271099090576},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.50446617603302},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4995858669281006},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4950789511203766},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.47500550746917725},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.42122000455856323},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4137135446071625}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.71080082654953},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6922272443771362},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6781560778617859},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6657597422599792},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5114427804946899},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5082271099090576},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.50446617603302},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4995858669281006},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4950789511203766},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.47500550746917725},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.42122000455856323},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4137135446071625},{"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.550000011920929,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W25833819","https://openalex.org/W1508404128","https://openalex.org/W1590014818","https://openalex.org/W1686810756","https://openalex.org/W1797441772","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1990368529","https://openalex.org/W1995357300","https://openalex.org/W2063385051","https://openalex.org/W2070613525","https://openalex.org/W2080039100","https://openalex.org/W2097117768","https://openalex.org/W2103933679","https://openalex.org/W2109255472","https://openalex.org/W2118246710","https://openalex.org/W2119531662","https://openalex.org/W2126237481","https://openalex.org/W2163605009","https://openalex.org/W2317851288","https://openalex.org/W2597243853","https://openalex.org/W2618530766","https://openalex.org/W2793345048","https://openalex.org/W2793954666","https://openalex.org/W2804865177","https://openalex.org/W2962835968","https://openalex.org/W2963108253","https://openalex.org/W2963762683","https://openalex.org/W6630399218","https://openalex.org/W6637373629","https://openalex.org/W6639824700","https://openalex.org/W6684191040","https://openalex.org/W6699915822","https://openalex.org/W6713634518"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W3135697610","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319","https://openalex.org/W2922442631","https://openalex.org/W4390494008","https://openalex.org/W2053596378","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Grain":[0],"segmentation":[1,65,191],"of":[2,32,40,46,79,106,119,172],"sandstone":[3,23,26,68,107,142,173],"images":[4,69,81,105,143],"is":[5,15,100,111,130,164,185],"to":[6,37,114,132,176],"segment":[7],"the":[8,16,38,41,44,47,71,74,77,80,93,116,120,123,126,134,149,153,161,169,177,189],"mineral":[9,20,33,108,138,170],"grains":[10,171],"into":[11,90,137],"separate":[12],"regions,":[13],"which":[14,35,110,148],"first":[17,75],"step":[18],"for":[19,52,63,167],"identification":[21],"and":[22,43,87],"classification.":[24],"A":[25],"image":[27],"contains":[28],"a":[29,60],"large":[30],"number":[31],"grains,":[34,109],"due":[36],"complexity":[39],"micro-structures":[42],"variability":[45],"optical":[48],"properties,":[49],"becomes":[50],"difficult":[51],"automatic":[53],"segmentation.":[54],"In":[55,73,92,122],"this":[56],"paper,":[57],"we":[58],"propose":[59],"three-stage":[61,183],"method":[62,184],"grain":[64],"taking":[66],"multi-angle":[67],"as":[70],"input.":[72],"stage,":[76,95,125],"pixels":[78],"are":[82],"clustered":[83],"by":[84,160],"both":[85],"color":[86],"spatial":[88],"properties":[89],"superpixels.":[91,121],"second":[94],"convolutional":[96,117,157],"neural":[97],"network":[98],"(CNN)":[99],"trained":[101],"based":[102,146],"on":[103,147],"replicated":[104],"then":[112],"used":[113,131],"extract":[115],"features":[118,158],"third":[124],"fuzzy":[127],"clustering":[128],"algorithm":[129],"merge":[133],"over-segmented":[135],"superpixels":[136],"grains.":[139],"We":[140],"collect":[141],"from":[144],"Tibet,":[145],"experimental":[150],"results":[151],"demonstrate":[152],"following:":[154],"(1)":[155],"The":[156,181],"extracted":[159],"designed":[162],"CNN":[163],"more":[165,186],"suitable":[166],"characterizing":[168],"images,":[174],"comparing":[175],"handcrafted":[178],"features.":[179],"(2)":[180],"proposed":[182],"effective":[187],"than":[188],"state-of-the-art":[190],"methods.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
