{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T06:10:22Z","timestamp":1763878222700,"version":"3.37.3"},"reference-count":21,"publisher":"World Scientific Pub Co Pte Ltd","issue":"10","funder":[{"name":"Henan science and technology project","award":["NO.172102410071"],"award-info":[{"award-number":["NO.172102410071"]}]},{"name":"Henan Natural Science Foundation","award":["NO.162300410095"],"award-info":[{"award-number":["NO.162300410095"]}]},{"name":"Henan science and technology project","award":["NO.172102210039"],"award-info":[{"award-number":["NO.172102210039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2018,10]]},"abstract":"<jats:p> A novel algorithm is presented based on non-subsampled contourlet transform (NSCT) and two dimension property histogram in order to realize the aerial small target detection of infrared imaging under complex background. First, this method transforms the infrared image from space domain to NSCT domain. In high frequency bandpass domain, this method describes the sub-band coefficients according to Gaussian scale mixture model based on Bayesian estimation and estimates the center coefficient with the local neighbor\u2019s in order to predict the high frequency background. On the other hand, this method predicts the low frequency background with self-adaption median filter in low frequency lowpass domain. Subsequently, the reversing NSCT is done and the complex background is estimated. By means of subtracting the estimated background image from the source image, the complex background is suppressed and the outstanding small target is acquired. Second, constructing the target\u2019s property set according to the priori knowledge, this method defines the corresponding two-dimensional property histogram which is applied into calculating the segmenting threshold on basis of the maximum entropy method. Subsequently, the infrared image whose complex background is suppressed will be segmented into binary image by the threshold. Finally, infrared small target is detected by the pipeline filter algorithm which makes use of the relativity of the target movement between frames. The experimental results prove the presented method\u2019s effectiveness which can detect the small target whose signal noise ratio (SNR) value is above 2 steadily. <\/jats:p>","DOI":"10.1142\/s0218001418500313","type":"journal-article","created":{"date-parts":[[2018,3,11]],"date-time":"2018-03-11T23:20:17Z","timestamp":1520810417000},"page":"1850031","source":"Crossref","is-referenced-by-count":8,"title":["Infrared Aerial Small Target Detection with NSCT and Two-Dimensional Property Histogram"],"prefix":"10.1142","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9743-7847","authenticated-orcid":false,"given":"Gang","family":"Liu","sequence":"first","affiliation":[{"name":"Information Engineering College, Henan University of Science and Technology, Luoyang 471023, P. R. China"}]},{"given":"Sen","family":"Liu","sequence":"additional","affiliation":[{"name":"Information Engineering College, Henan University of Science and Technology, Luoyang 471023, P. R. 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