{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T01:48:50Z","timestamp":1648950530219},"reference-count":25,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p> The normalized intensity factor based on statistical first-order moment of gray-scale image is defined in this paper. The intensity factor can be used to distinguish the brightness level of a gray-scale image and to determine a threshold value for image segmentation. According to the intensity factor and the characteristic of human body in the gray-scale infrared image, a new algorithm of calculating the intensity-level threshold is designed which can be used for segmenting human body area in an infrared image. In the algorithm, based on the concept of intensity factor, a histogram of low brightness gray-scale image (LGIRI) is divided into three parts: a low-intensity region (0.25[Formula: see text][Formula: see text]), a medium-intensity region (0.25\u20130.75[Formula: see text][Formula: see text]), and a high-intensity region (0.75\u20131[Formula: see text][Formula: see text]), and then the intensity [Formula: see text] which satisfies the [Formula: see text] is selected as an intensity-level value [Formula: see text], and the intensity [Formula: see text] which satisfies [Formula: see text] is selected as an intensity-level value [Formula: see text], at last [Formula: see text] is the pixel classification threshold (the intensity-level threshold). It is noted that there is no preprocessing for image noise filtering and\/or processing, and all images come from OTCBVS. Compared with the method of selecting trough points of the histogram as the intensity-level threshold, this algorithm avoids the problem of nonexistence of evident trough point at the high-intensity level of a histogram. Also, the experimental results show that the segmenting results of LGIRI processed by the algorithm are better than those of Otsu method. <\/jats:p>","DOI":"10.1142\/s0218001421560012","type":"journal-article","created":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T14:07:31Z","timestamp":1595858851000},"page":"2156001","source":"Crossref","is-referenced-by-count":0,"title":["Intensity Factor Method for Segmenting Human Body Region in Gray-scale Infrared Image"],"prefix":"10.1142","volume":"35","author":[{"given":"Jia","family":"Liu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University Beijing, P. R. China"},{"name":"Smart City College, Beijing Union University, Beijing, P\u00a0.R\u00a0.China"}]},{"given":"Miyi","family":"Duan","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University Beijing, P. R. China"}]},{"given":"Hongqi","family":"Gao","sequence":"additional","affiliation":[{"name":"The PLA Shenyang Joint Service Support Center, Beijing, P\u00a0.R\u00a0.China"}]}],"member":"219","published-online":{"date-parts":[[2020,7,27]]},"reference":[{"issue":"1","key":"S0218001421560012BIB001","first-page":"21","volume":"15","author":"Cielniak G.","year":"2004","journal-title":"J. Intell. Fuzzy Syst."},{"key":"S0218001421560012BIB002","first-page":"1096","volume-title":"IEEE ICMA","author":"Enzeng D.","year":"2019"},{"key":"S0218001421560012BIB003","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1109\/TPAMI.2009.122","volume":"32","author":"Ger\u00f3nimo D.","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"S0218001421560012BIB004","first-page":"759","volume-title":"Proc. IEEE Intelligent Vehicles Symp.","author":"Grisleri P."},{"key":"S0218001421560012BIB005","first-page":"1","volume-title":"Annual IEEE India Conf. (INDICON)","author":"Irshad","year":"2015"},{"key":"S0218001421560012BIB006","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.measurement.2014.07.010","volume":"57","author":"Jadin M. S.","year":"2014","journal-title":"Measurement"},{"key":"S0218001421560012BIB007","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","volume":"45","author":"Jiayi M.","year":"2019","journal-title":"Inf. Fusion"},{"key":"S0218001421560012BIB008","first-page":"1101","volume-title":"IEEE Adv. Inf. Technol. Electron. Automat. Control","author":"Jiayuan M.","year":"2015"},{"issue":"9","key":"S0218001421560012BIB009","doi-asserted-by":"crossref","first-page":"2826","DOI":"10.1109\/TITS.2017.2761901","volume":"19","author":"Li L.","year":"2018","journal-title":"IEEE Trans. Intell. Transportation Syst."},{"key":"S0218001421560012BIB010","first-page":"839","volume-title":"IEEE ICIP","author":"Lu L.","year":"2016"},{"key":"S0218001421560012BIB011","first-page":"1171","volume-title":"Fifth IMCCC Proc.","author":"Luo Z.","year":"2015"},{"issue":"6","key":"S0218001421560012BIB012","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1109\/TVT.2004.834878","volume":"53","author":"Massimo B.","year":"2004","journal-title":"IEEE Trans. Vehicular Technol."},{"issue":"1","key":"S0218001421560012BIB013","first-page":"62","volume-title":"IEEE Trans. SMC","volume":"9","author":"Nobuyuki O.","year":"1979"},{"key":"S0218001421560012BIB015","first-page":"3763","volume-title":"IEEE IGARSS Proc.","author":"Penglin W.","year":"2017"},{"key":"S0218001421560012BIB016","volume-title":"Int. Conf. Computer Vision and Image Analysis Applications","author":"Rachid S.","year":"2015"},{"key":"S0218001421560012BIB017","first-page":"1962","volume-title":"14th Int. Conf. Information Fusion Proc.","author":"Schnelle S. R."},{"key":"S0218001421560012BIB018","first-page":"5421","volume-title":"Int. Conf. Multimedia Technol","author":"Shunyong Z.","year":"2011"},{"key":"S0218001421560012BIB019","first-page":"1","volume-title":"IEEE 21st INMIC Proc.","author":"UrRehman S.","year":"2018"},{"key":"S0218001421560012BIB020","first-page":"430","volume-title":"14th IEEE ICSP Proc.","author":"Voronin V.","year":"2018"},{"key":"S0218001421560012BIB021","first-page":"2313","volume-title":"17th IEEE ICIP Proc.","author":"Weihong W.","year":"2010"},{"key":"S0218001421560012BIB022","first-page":"5644","volume-title":"Chin. Control Decision Conf. (CCDC)","author":"Ya W.","year":"2018"},{"issue":"5","key":"S0218001421560012BIB023","first-page":"1679","volume":"53","author":"Yajun F.","year":"2004","journal-title":"IEEE Tran. Vehieular Technol."},{"issue":"3","key":"S0218001421560012BIB024","first-page":"237","volume":"7","author":"Yu X.","year":"2014","journal-title":"Int. J. Signal Process. Image Process. Pattern Recog."},{"key":"S0218001421560012BIB025","first-page":"306","volume-title":"IEEE ICISMS Proc.","author":"Zaid O.","year":"2014"},{"key":"S0218001421560012BIB026","first-page":"1","volume-title":"10th ICMIC Proc.","author":"Zheng-Guang X.","year":"2018"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001421560012","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T11:17:36Z","timestamp":1611919056000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001421560012"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,27]]},"references-count":25,"journal-issue":{"issue":"01","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1142\/S0218001421560012"],"URL":"https:\/\/doi.org\/10.1142\/s0218001421560012","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,27]]}}}