{"id":"https://openalex.org/W2419102386","doi":"https://doi.org/10.1109/acpr.2015.7486515","title":"A learned overcomplete sparseness and IGMRF based regularization framework for dense disparity estimation","display_name":"A learned overcomplete sparseness and IGMRF based regularization framework for dense disparity estimation","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2419102386","doi":"https://doi.org/10.1109/acpr.2015.7486515","mag":"2419102386"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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/A5003494180","display_name":"Sonam Nahar","orcid":"https://orcid.org/0000-0002-9308-2185"},"institutions":[{"id":"https://openalex.org/I33552525","display_name":"LNM Institute of Information Technology","ror":"https://ror.org/03jp7rg16","country_code":"IN","type":"education","lineage":["https://openalex.org/I33552525"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sonam Nahar","raw_affiliation_strings":["The LNMIIT, Jaipur, India"],"affiliations":[{"raw_affiliation_string":"The LNMIIT, Jaipur, India","institution_ids":["https://openalex.org/I33552525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101917213","display_name":"Manjunath V. Joshi","orcid":"https://orcid.org/0000-0002-1842-9118"},"institutions":[{"id":"https://openalex.org/I98389781","display_name":"Dhirubhai Ambani Institute of Information and Communication Technology","ror":"https://ror.org/02d5b7g69","country_code":"IN","type":"education","lineage":["https://openalex.org/I98389781"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manjunath V. Joshi","raw_affiliation_strings":["DA-IICT, Gandhinagar, India"],"affiliations":[{"raw_affiliation_string":"DA-IICT, Gandhinagar, India","institution_ids":["https://openalex.org/I98389781"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5003494180"],"corresponding_institution_ids":["https://openalex.org/I33552525"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22098087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"306","last_page":"310"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9993000030517578,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9993000030517578,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9926000237464905,"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":0.9923999905586243,"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/regularization","display_name":"Regularization (linguistics)","score":0.64907306432724},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5826258063316345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5619630813598633},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5261618494987488},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5112910270690918},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4959042966365814},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4417755603790283},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.4313731789588928},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.42526954412460327},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.17032581567764282},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1387031376361847},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.08235412836074829}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.64907306432724},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5826258063316345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5619630813598633},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5261618494987488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5112910270690918},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4959042966365814},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4417755603790283},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.4313731789588928},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.42526954412460327},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.17032581567764282},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1387031376361847},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.08235412836074829},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acpr.2015.7486515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8500000238418579,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1520110625","https://openalex.org/W1781337833","https://openalex.org/W1967027087","https://openalex.org/W2004500952","https://openalex.org/W2022651513","https://openalex.org/W2101309634","https://openalex.org/W2104974755","https://openalex.org/W2110013009","https://openalex.org/W2111559961","https://openalex.org/W2114122776","https://openalex.org/W2136905173","https://openalex.org/W2137017911","https://openalex.org/W2143516773","https://openalex.org/W2145104498","https://openalex.org/W2151646056","https://openalex.org/W2153663612","https://openalex.org/W2160547390","https://openalex.org/W6631120827"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W2085259108","https://openalex.org/W2100805585","https://openalex.org/W2162874930"],"abstract_inverted_index":{"In":[0,141],"this":[1],"work,":[2],"we":[3],"propose":[4],"to":[5,24,96],"use":[6],"an":[7,122],"Inhomogeneous":[8],"Gaussian":[9],"Markov":[10],"Random":[11],"Field":[12],"(IGMRF)":[13],"and":[14,152,172],"sparsity":[15,48],"based":[16],"priors":[17],"in":[18,22,57,121,173],"a":[19,112,136],"regularization":[20],"framework":[21,125],"order":[23],"estimate":[25],"the":[26,34,51,58,64,69,76,81,97,100,118,128,153,160,168,182,189,193,196],"dense":[27],"disparity":[28,59,78,129,169,176],"map.":[29,60,130],"The":[30,47,61,91,104],"IGMRF":[31,119,144],"prior":[32,49,113,120],"captures":[33,50],"spatial":[35],"variation":[36],"among":[37],"disparities":[38,65,98,108,163],"locally":[39],"as":[40,42,55,111,157,159],"well":[41,158],"it":[43],"preserves":[44],"sharp":[45],"discontinuities.":[46],"additional":[52],"structure":[53],"such":[54],"sparseness":[56,62,161],"of":[63,80,99,107,162,195],"are":[66,94,146,164],"represented":[67],"over":[68],"overcomplete":[70],"dictionary":[71,92,154],"which":[72,114],"is":[73,109,115,133,155,178],"learned":[74,156],"from":[75],"estimated":[77,134,179],"map":[79,132,170,177],"given":[82,101],"stereo":[83,102],"pair,":[84],"using":[85,135],"K-singular":[86],"value":[87],"decomposition":[88],"(K-SVD)":[89],"algorithm.":[90,140],"atoms":[93],"adaptive":[95],"pair.":[103],"sparse":[105],"representation":[106],"used":[110],"combined":[116],"with":[117],"energy":[123],"minimization":[124],"for":[126],"estimating":[127],"Disparity":[131],"two":[137],"phase,":[138],"iterative":[139],"phase":[142,174],"one,":[143],"parameters":[145,184],"computed":[147],"at":[148],"each":[149],"pixel":[150],"location":[151],"obtained":[165],"while":[166],"keeping":[167,181],"fixed,":[171],"two,":[175],"by":[180],"other":[183],"fixed.":[185],"Experimental":[186],"results":[187],"on":[188],"standard":[190],"dataset":[191],"demonstrate":[192],"effectiveness":[194],"proposed":[197],"approach.":[198]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
