{"id":"https://openalex.org/W2808944178","doi":"https://doi.org/10.1109/icait.2017.8388952","title":"An efficient object recognition based on Gabor transform and LBP variance","display_name":"An efficient object recognition based on Gabor transform and LBP variance","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2808944178","doi":"https://doi.org/10.1109/icait.2017.8388952","mag":"2808944178"},"language":"en","primary_location":{"id":"doi:10.1109/icait.2017.8388952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icait.2017.8388952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","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/A5102015026","display_name":"Yongxin Chang","orcid":"https://orcid.org/0000-0003-1831-5185"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yongxin Chang","raw_affiliation_strings":["Dept. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Dept. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, China","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101952644","display_name":"Shijie Feng","orcid":"https://orcid.org/0000-0001-8261-5276"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shijie Feng","raw_affiliation_strings":["Dept. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Dept. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, China","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026063666","display_name":"Jing Zhang","orcid":"https://orcid.org/0000-0001-8135-8124"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["Dept. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Dept. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102015026"],"corresponding_institution_ids":["https://openalex.org/I165745306"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24235153,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9008","issue":null,"first_page":"393","last_page":"398"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995999932289124,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995999932289124,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9987000226974487,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7782819271087646},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.7737181186676025},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7700768113136292},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.6162756681442261},{"id":"https://openalex.org/keywords/viewpoints","display_name":"Viewpoints","score":0.5942528247833252},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.586835503578186},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5028014183044434},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5017049312591553},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4734437167644501},{"id":"https://openalex.org/keywords/3d-single-object-recognition","display_name":"3D single-object recognition","score":0.46925899386405945},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.45943281054496765},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4112577438354492},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39695772528648376},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.37796658277511597},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11517810821533203}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7782819271087646},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.7737181186676025},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7700768113136292},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.6162756681442261},{"id":"https://openalex.org/C2776035091","wikidata":"https://www.wikidata.org/wiki/Q7928819","display_name":"Viewpoints","level":2,"score":0.5942528247833252},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.586835503578186},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5028014183044434},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5017049312591553},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4734437167644501},{"id":"https://openalex.org/C14551309","wikidata":"https://www.wikidata.org/wiki/Q4636325","display_name":"3D single-object recognition","level":4,"score":0.46925899386405945},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.45943281054496765},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4112577438354492},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39695772528648376},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.37796658277511597},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11517810821533203},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icait.2017.8388952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icait.2017.8388952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1985636418","https://openalex.org/W2022274350","https://openalex.org/W2029683515","https://openalex.org/W2055349880","https://openalex.org/W2060223645","https://openalex.org/W2108556791","https://openalex.org/W2108704430","https://openalex.org/W2120419212","https://openalex.org/W2123456673","https://openalex.org/W2131693260","https://openalex.org/W2161969291","https://openalex.org/W2163352848","https://openalex.org/W2168356304","https://openalex.org/W2397631926","https://openalex.org/W2535410496","https://openalex.org/W2548197316","https://openalex.org/W6678178411","https://openalex.org/W6712571603"],"related_works":["https://openalex.org/W4376620596","https://openalex.org/W3177249605","https://openalex.org/W2534152068","https://openalex.org/W3138508047","https://openalex.org/W1972515067","https://openalex.org/W1689909837","https://openalex.org/W4293054914","https://openalex.org/W4298525700","https://openalex.org/W2953362004","https://openalex.org/W2549121492"],"abstract_inverted_index":{"Recognizing":[0],"objects":[1,156],"from":[2,89],"arbitrary":[3],"aspects":[4],"is":[5,82],"always":[6],"a":[7,35,126],"highly":[8],"challenging":[9],"problem":[10,49,119],"in":[11,30,76],"applied":[12],"engineering":[13],"and":[14,53,66,95,120,143,159],"computer":[15],"vision":[16],"fields.":[17],"At":[18],"present,":[19],"most":[20],"existing":[21],"algorithms":[22],"mainly":[23],"focus":[24],"on":[25,135],"specific":[26],"viewpoint":[27,86],"detection.":[28],"Hence,":[29],"this":[31,129],"paper":[32],"we":[33],"propose":[34],"novel":[36,130],"recognizing":[37,61],"model,":[38],"which":[39],"combines":[40],"Gabor":[41],"transform":[42],"with":[43,107],"LBP":[44],"variance":[45],"to":[46,71,83,97,103],"handle":[47],"the":[48,57,69,73,77,90,111,117,122],"of":[50,59,93,100],"different":[51],"viewpoints":[52],"pose":[54],"changing.":[55],"Then,":[56],"images":[58],"inaccurate":[60],"are":[62],"evaluated":[63],"by":[64],"learning":[65],"fed":[67],"back":[68],"detector":[70],"avoid":[72],"same":[74],"mistakes":[75],"future.":[78],"The":[79,147],"principal":[80],"idea":[81],"extract":[84],"intrinsic":[85],"invariant":[87],"features":[88,102],"unseen":[91],"poses":[92],"object,":[94],"then":[96],"take":[98],"advantage":[99],"these":[101],"support":[104],"recognition.":[105],"Compared":[106],"other":[108],"recognition":[109,123,164],"models,":[110],"proposed":[112],"approach":[113,153],"can":[114,154],"efficiently":[115],"tackle":[116],"multi-view":[118],"promote":[121],"performance.":[124],"For":[125],"quantitative":[127],"evaluation,":[128],"algorithm":[131],"has":[132],"been":[133],"tested":[134],"two":[136],"benchmark":[137],"datasets":[138],"such":[139],"as":[140],"Caltech":[141],"101":[142],"PASCAL":[144],"VOC":[145],"2011datasets.":[146],"experimental":[148],"results":[149],"validate":[150],"that":[151],"our":[152],"recognize":[155],"more":[157],"precisely":[158],"outperforms":[160],"others":[161],"single":[162],"view":[163],"methods.":[165]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}