{"id":"https://openalex.org/W2910276342","doi":"https://doi.org/10.1109/aiccsa.2018.8612854","title":"Real-Time GPU Based Video Segmentation with Depth Information","display_name":"Real-Time GPU Based Video Segmentation with Depth Information","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2910276342","doi":"https://doi.org/10.1109/aiccsa.2018.8612854","mag":"2910276342"},"language":"en","primary_location":{"id":"doi:10.1109/aiccsa.2018.8612854","is_oa":false,"landing_page_url":"http://doi.org/10.1109/aiccsa.2018.8612854","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)","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/A5004750612","display_name":"Nilangshu Bidyanta","orcid":null},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nilangshu Bidyanta","raw_affiliation_strings":["Department of Electrical & Computer Engineering, University of Arizona, Tucson, AZ"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, University of Arizona, Tucson, AZ","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080974820","display_name":"Ali Akoglu","orcid":"https://orcid.org/0000-0001-7982-8991"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Akoglu","raw_affiliation_strings":["Department of Electrical & Computer Engineering, University of Arizona, Tucson, AZ"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, University of Arizona, Tucson, AZ","institution_ids":["https://openalex.org/I138006243"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5004750612"],"corresponding_institution_ids":["https://openalex.org/I138006243"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16937575,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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/T10531","display_name":"Advanced Vision and Imaging","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/T11019","display_name":"Image Enhancement Techniques","score":0.9997000098228455,"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":0.9995999932289124,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7800804376602173},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6138511300086975},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5886138677597046},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5413920283317566},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.5144567489624023},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4907875955104828}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7800804376602173},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6138511300086975},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5886138677597046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5413920283317566},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.5144567489624023},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4907875955104828}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aiccsa.2018.8612854","is_oa":false,"landing_page_url":"http://doi.org/10.1109/aiccsa.2018.8612854","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)","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":19,"referenced_works":["https://openalex.org/W157162179","https://openalex.org/W1508404128","https://openalex.org/W1964108521","https://openalex.org/W2017313218","https://openalex.org/W2030346542","https://openalex.org/W2056760934","https://openalex.org/W2065985268","https://openalex.org/W2067191022","https://openalex.org/W2086374833","https://openalex.org/W2099244020","https://openalex.org/W2116046277","https://openalex.org/W2121947440","https://openalex.org/W2124211486","https://openalex.org/W2124386111","https://openalex.org/W2129340571","https://openalex.org/W2155598147","https://openalex.org/W2161337641","https://openalex.org/W2545985378","https://openalex.org/W6677945368"],"related_works":["https://openalex.org/W2755342338","https://openalex.org/W2058170566","https://openalex.org/W2036807459","https://openalex.org/W2775347418","https://openalex.org/W1969923398","https://openalex.org/W2772917594","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407"],"abstract_inverted_index":{"In":[0,75],"the":[1,12,58,67,72,83,88,105,115,124,134,153,164,182,199,208,211,214,221],"context":[2],"of":[3,60,90,117,126,136,188,197,210],"video":[4,36,231],"segmentation":[5,64,69,84],"with":[6,133,184],"depth":[7,42,61,150,177],"sensor,":[8],"prior":[9,73],"work":[10,40],"maps":[11],"Metropolis":[13,128,217],"algorithm,":[14],"a":[15,45,52,100],"simulated":[16],"annealing":[17],"based":[18],"key":[19],"routine":[20],"during":[21,63],"segmentation,":[22,212],"onto":[23],"an":[24],"Nvidia":[25],"Graphics":[26],"Processing":[27],"Unit":[28],"(GPU)":[29],"and":[30,65,86,96,113,161,191,213],"achieves":[31],"real-time":[32],"performance":[33],"for":[34,103,229],"320\u00d7256":[35,230],"sequences.":[37,232],"However":[38],"that":[39,56,163],"utilizes":[41],"information":[43,62,151],"in":[44,195,216],"very":[46],"limited":[47],"manner.":[48],"This":[49,119],"paper":[50],"presents":[51],"new":[53],"GPU-based":[54],"method":[55],"expands":[57],"use":[59],"shows":[66],"improved":[68],"quality":[70,209],"over":[71,131],"work.":[74],"particular,":[76],"we":[77,148,180],"discuss":[78],"various":[79],"ways":[80],"to":[81,122,143,226],"restructure":[82],"flow,":[85],"evaluate":[87,139],"impact":[89],"several":[91],"design":[92,141],"choices":[93,142],"on":[94],"throughput":[95],"quality.":[97],"We":[98,138],"introduce":[99],"scaling":[101],"factor":[102],"amplifying":[104],"interaction":[106,165],"strength":[107],"between":[108,158,167,201],"two":[109,140,159],"spatially":[110],"neighboring":[111,168],"pixels":[112,169],"increasing":[114],"clarity":[116],"borderlines.":[118],"allows":[120],"us":[121],"reduce":[123],"number":[125],"required":[127],"iterations":[129,218],"by":[130,175],"50%":[132],"drawback":[135],"over-segmentation.":[137],"overcome":[144],"this":[145],"problem.":[146],"First,":[147],"incorporate":[149],"into":[152],"perceived":[154],"color":[155],"difference":[156,200],"calculations":[157],"pixels,":[160],"show":[162,192],"strengths":[166],"can":[170],"be":[171],"more":[172],"accurately":[173],"modeled":[174],"incorporating":[176],"information.":[178],"Second,":[179],"pre-process":[181],"frames":[183],"Bilateral":[185],"filter":[186],"instead":[187],"Gaussian":[189],"filter,":[190],"its":[193],"effectiveness":[194],"terms":[196],"reducing":[198],"similar":[202],"colors.":[203],"Both":[204],"approaches":[205],"help":[206],"improve":[207,220],"reduction":[215],"helps":[219],"throughout":[222],"from":[223],"29":[224],"fps":[225,228],"34":[227]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}