{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T13:36:10Z","timestamp":1648992970433},"reference-count":12,"publisher":"Hindawi Limited","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61471124","61473090","PIRSESGA-2010-269124"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2016]]},"abstract":"In echo-cardiac clinical computer-aided diagnosis, an important step is to automatically classify echocardiography videos from different angles and different regions. We propose a kind of echocardiography video classification algorithm based on the dense trajectory and difference histograms of oriented gradients (DHOG). First, we use the dense grid method to describe feature characteristics in each frame of echocardiography sequence and then track these feature points by applying the dense optical flow. In order to overcome the influence of the rapid and irregular movement of echocardiography videos and get more robust tracking results, we also design a trajectory description algorithm which uses the derivative of the optical flow to obtain the motion trajectory information and associates the different characteristics (e.g., the trajectory shape, DHOG, HOF, and MBH) with embedded structural information of the spatiotemporal pyramid. To avoid \u201cdimension disaster,\u201d we apply Fisher\u2019s vector to reduce the dimension of feature description followed by the SVM linear classifier to improve the final classification result. The average accuracy of echocardiography video classification is 77.12% for all eight viewpoints and 100% for three primary viewpoints.<\/jats:p>","DOI":"10.1155\/2016\/9610192","type":"journal-article","created":{"date-parts":[[2016,2,29]],"date-time":"2016-02-29T21:02:21Z","timestamp":1456779741000},"page":"1-7","source":"Crossref","is-referenced-by-count":2,"title":["Dense Trajectories and DHOG for Classification of Viewpoints from Echocardiogram Videos"],"prefix":"10.1155","volume":"2016","author":[{"given":"Liqin","family":"Huang","sequence":"first","affiliation":[{"name":"School of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Xiangyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China"}]}],"member":"98","reference":[{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-012-9543-x"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cardfail.2014.02.006"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2011.2161673"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2014.2313132"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-36678-9_7"},{"issue":"4","key":"9","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1080\/AC.70.4.3096898","volume":"70","year":"2015","journal-title":"Acta Cardiologica"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.4250\/jcu.2015.23.2.72"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-012-0594-8"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15552-9_52"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1109\/lsp.2014.2358230"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2013.144"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2015.2418770"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2016\/9610192.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2016\/9610192.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2016\/9610192.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T03:46:17Z","timestamp":1567655177000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/cmmm\/2016\/9610192\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":12,"alternative-id":["9610192","9610192"],"URL":"https:\/\/doi.org\/10.1155\/2016\/9610192","relation":{},"ISSN":["1748-670X","1748-6718"],"issn-type":[{"value":"1748-670X","type":"print"},{"value":"1748-6718","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]}}}
