{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T01:23:23Z","timestamp":1746667403387,"version":"3.37.3"},"reference-count":11,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","funder":[{"DOI":"10.13039\/501100001809","name":"National natural Science Foundation of China","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2018,6]]},"abstract":"<jats:p> The traditional Gaussian mixture background model failed to build a reasonable background in complex scenarios, so this paper proposes an improved self-adaptive Gaussian mixture background modeling which integrates the difference method and adaptive threshold segmentation to improve the traditional one. In the proposed model, the difference method is applied to achieve the segmentation of changing area and background area, and different weight update policies are used for different areas. Background area updates background model with a fixed update rate. The changing region is divided into moving target area and background show area with the fusion of adaptive threshold; the background show area has a large update rate, allowing the previously obscured parts to recover rapidly; the moving target area won\u2019t build new Gaussian components for the Gaussian mixture model. Experiments show that the video sequence algorithm with uncertainties of building background model has good adaptability. It helps to improve computing speed to a large extent and it can also respond to the change of the actual scene quickly. <\/jats:p>","DOI":"10.1142\/s0218001418590103","type":"journal-article","created":{"date-parts":[[2017,11,28]],"date-time":"2017-11-28T03:27:58Z","timestamp":1511839678000},"page":"1859010","source":"Crossref","is-referenced-by-count":9,"title":["Research on Cloud Computing Modeling Based on Fusion Difference Method and Self-Adaptive Threshold Segmentation"],"prefix":"10.1142","volume":"32","author":[{"given":"Hong","family":"Sun","sequence":"first","affiliation":[{"name":"University of Shanghai for Science and Technology, Shanghai 200093, P. R. China"},{"name":"Shanghai Key Lab of Modern Optical System and Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai 200093, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shi-Ping","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Shanghai for Science and Technology, Shanghai 200093, P. R. China"},{"name":"Shanghai Key Lab of Modern Optical System and Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai 200093, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Ping","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Shanghai for Science and Technology, Shanghai 200093, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2018,2,21]]},"reference":[{"key":"S0218001418590103BIB001","first-page":"3269","volume":"30","author":"Cui T.","year":"2010","journal-title":"J. Comput. Appl."},{"key":"S0218001418590103BIB002","first-page":"4738","volume":"28","author":"Gang L.","year":"2011","journal-title":"Appl. Res. Comput."},{"key":"S0218001418590103BIB003","first-page":"95","volume":"27","author":"He X.","year":"2010","journal-title":"Appl. Res. Comput."},{"issue":"6","key":"S0218001418590103BIB004","first-page":"14","volume":"42","author":"Hong S.","year":"2015","journal-title":"Opto-Electron. Eng."},{"issue":"5","key":"S0218001418590103BIB005","first-page":"1104","volume":"36","author":"Hong S.","year":"2015","journal-title":"J. Chin. Comput. Syst."},{"key":"S0218001418590103BIB006","volume-title":"Appl. Res. Comput.","volume":"34","author":"Huang D.","year":"2017"},{"key":"S0218001418590103BIB007","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-005-3960-y"},{"key":"S0218001418590103BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/34.868677"},{"key":"S0218001418590103BIB009","first-page":"685","volume-title":"Micro Comput. Syst.","volume":"34","author":"Wang X.","year":"2013"},{"key":"S0218001418590103BIB010","first-page":"729","volume":"13","author":"Xin L.","year":"2008","journal-title":"J. Image Graph."},{"key":"S0218001418590103BIB012","first-page":"1","volume":"37","author":"Zhang X.","year":"2010","journal-title":"Opto-Electron. Eng."}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001418590103","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T17:04:52Z","timestamp":1565111092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001418590103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,21]]},"references-count":11,"journal-issue":{"issue":"06","published-online":{"date-parts":[[2018,2,21]]},"published-print":{"date-parts":[[2018,6]]}},"alternative-id":["10.1142\/S0218001418590103"],"URL":"https:\/\/doi.org\/10.1142\/s0218001418590103","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"type":"print","value":"0218-0014"},{"type":"electronic","value":"1793-6381"}],"subject":[],"published":{"date-parts":[[2018,2,21]]}}}