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基于標(biāo)號(hào)場(chǎng)的光流法二維運(yùn)動(dòng)估計(jì)

楊勇,  王橋,  吳樂南

楊勇, 王橋, 吳樂南. 基于標(biāo)號(hào)場(chǎng)的光流法二維運(yùn)動(dòng)估計(jì)[J]. 電子與信息學(xué)報(bào), 2001, 23(12): 1321-1325.
引用本文: 楊勇, 王橋, 吳樂南. 基于標(biāo)號(hào)場(chǎng)的光流法二維運(yùn)動(dòng)估計(jì)[J]. 電子與信息學(xué)報(bào), 2001, 23(12): 1321-1325.
Yang Yong, Wang Qiao, Wu Lenan . TWO-DIMENSION MOTION ESTIMATION BASED ON LABELING FIELD AND OPTICAL FLOW FIELD[J]. Journal of Electronics & Information Technology, 2001, 23(12): 1321-1325.
Citation: Yang Yong, Wang Qiao, Wu Lenan . TWO-DIMENSION MOTION ESTIMATION BASED ON LABELING FIELD AND OPTICAL FLOW FIELD[J]. Journal of Electronics & Information Technology, 2001, 23(12): 1321-1325.

基于標(biāo)號(hào)場(chǎng)的光流法二維運(yùn)動(dòng)估計(jì)

TWO-DIMENSION MOTION ESTIMATION BASED ON LABELING FIELD AND OPTICAL FLOW FIELD

  • 摘要: 該文提出了一種改進(jìn)的二維運(yùn)動(dòng)估計(jì)光流算法。在光流基本約束和全局平滑性約束的基礎(chǔ)上,引入二維運(yùn)動(dòng)標(biāo)號(hào)場(chǎng),對(duì)Horn-Schunck算法進(jìn)行了改進(jìn),不僅加快了算法的收斂速度,而且提高了運(yùn)動(dòng)估計(jì)的準(zhǔn)確性,減輕了遮擋邊界問題的影響。實(shí)驗(yàn)結(jié)果證明了該算法的有效性。
    Abstract: In this paper, an improved algorithm combining optical flow field with labeling field is presented for estimating the two-dimension motion. Based on the basic constraint and the global smooth constraint, a labeling field is introduced, by which Horn-Schunck algorithm is modified. This method not only fastens the convergence of the algorithm, but also improves the accuracy and weakens the effect of the occluding boundary on the motion estimation. The experimental results show that it is a suitable method.
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  • 高文,陳熙霖,計(jì)算機(jī)視覺-算法與系統(tǒng)原理,北京,清華大學(xué)出版社與廣西科學(xué)技術(shù)出版社,1999,119 208[2]吳立德,計(jì)算機(jī)視覺,上海,復(fù)旦大學(xué)出版社,1993,83-100.[3]B.K.P. Horn, B. G. Schunck, Determining optical flow, Artificial Intelligence, 1981, 17(1-3),185-203.[4]D. Terzopoulos, Regularization of inverse visual problems involving discontinuities, IEEE Trans.on PAMI, 1986, PAMI-8(4), 413-424.[5]H.H. Nagel, On the estimation of optical flow: Relation between different approaches and some new results, Artificial Intelligence, 1987, 33(3), 299-324.[6]H.H. Nagel, W. Enkelmann, An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences, IEEE Trans. on PAMI, 1986, PAMI-8(5), 565-593.[7]S.V. Fogel, Estimation of velocity vector fields from time-varying image sequences, CVGIP:Image Understanding, 1991, 53(3), 253-287.[8]A. Murat Tekalp, Digital Video Processing, Englewood Cliffs, NJ, Prentice Hall PTR, 1998,200-201.[9]A. Rosenfeld. C. K. Avinash, Digital Picture Processing, New York, NY, Academic Press, 1976,263-276.
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  • 收稿日期:  2000-06-27
  • 修回日期:  2000-10-25
  • 刊出日期:  2001-12-19

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