Abstract
Invariant moment is a highly concentrated and distortion invariant image features. The goal of multi-moving object characterization and classification methods is to find a suitable description of different kinds of moving objects in the scene and match the similarity between unknown moving objects with invariant moment. This paper presents evolution and development of invariant moments family history, and designs a classification model to classify multi-moving objects. Experimental results show that this method can effectively improve the recognition rate of the moving object.
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References
Amu G, Hasi S, Yang X et al (2004) Image analysis by Pseudo-Jacobi(p = 4, q = 3)-Fourier moments[J]. Appl Opt 43(10):2093–2101
Chen G, Bui TD (1999) Invariant fourier wavelet descriptor for patern recognition[J]. Pattern Recogn 32:1083–1088
Chen BJ, Shu HZ, Zhang H et al (2012) Quaternion Zernike moments and their invariants for color image analysis and object recognition[J]. Signal Process 92:308–318
Chen X, Yang J, Liang J, Ye Q (2012) Recursive robust least squares support vector regression based on maximum correntropy criterion[J]. Neurocomputing 97:63–73
Chen X, Yang J, Liang J, Ye Q (2012) Smooth twin support vector regression [J]. Neural Comput & Applic 21(3):505–513
Chen X, Yang J, Liang J (2011) A flexible support vector machine for regression [J]. Neural Comput & Applic 21(8):105–112
Cheng HD, Desai R (1998) Scene classification by fuzzy local moments[J]. Int J Pattern Recognit Artif Intell 7:921–938
Fan L-n, Dong L-j, Xu X-h (2005) Research on image pattern recognition based on line moment feature [C]. Harbin, Proc 2005 Chin Contrl Decision Conf: 579–582
Flusser J (2000) On the independence of rotation moment invariants[J]. Pattern Recogn 33:1405–1410
Flusser J, Kautsky J, Šroubek F (2009) Implicit moment invariants[J]. Int J Comput Vis 10:1007–1009
Flusser J, Suk T (1993) Pattern recognition by affine moment invariants[J]. Pattern Recogn 26:167–174
Gu HZ, Lee SY (2013) A view-invariant and anti-reflection algorithm for car body extraction and color classification[J]. Multimed Tools Appl 65(3):387–418
Gupta L, Srinath MD (1987) Contour sequence moments for the classification of closed planar shapes[J]. Pattern Recogn 20(3):267–272
Hahn WE, Lewkowitz S, Lacombe DC, et al. (2015) Deep learning human actions from video via sparse filtering and locally competitive algorithms[J]. Multimed Tools Appl:1-14
Ke J, Zhan Y-z, Chen X-j (2009) Pseudo invariant line moment to detect the target region of moving vessels[D]. 2009 Int Conf Intell Comput, Ulsan, Korea, 2009.9.16-9.19
Lenz R, Meer P (1994) Point configuration invariants under simultaneous projective and permutation transformations[J]. Pattern Recogn 27:1523–1532
Li Z-m (2005) Moments and its applications in geometric shape description [D]. Doctor of Engineering, Beijing, Institute of Computing Technology, Chinese Academy of Sciences
Li X-y, Lu C-h, J-m L (2007) Fusion of outline moment and the fourier descriptors for the recognition of pressed characters [J]. J Optoelectron Laser 18(10):1244–1247,1259
Liao SX, Chiang A, Lu Q, Pawlak M (2002) Chinese character recognition via Gegenbauer moments[C]. Proc 16th Int Conf Pattern Recogn ICPR’02 (Québec City, Canada) 3:11–15
M.K H (1962) Visual pattern recognition by moment invariants[J]. IRE Trans Inf Theory 8:179–187
Mukundan R, Ong SH, Lee PA (2001) Image analysis by Tchebichef moments[J]. IEEE Trans Image Process 10:1357–1364
Mukundan R, Ramakrishnan KR (1998) Moment functions in image analysis [M].Singapore:World Scientific
Ping Z, Ren H, Zou J, Sheng Y (2007) Generic orthogonal moments:Jacobi-Fourier moments for invariant image description[J]. Pattern Recogn 40:1245–1254
Ping ZL, Wu RG, Sheng YL (2002) Describing image with Chebyshev-FourierMoments[J]. J Optic Soc Am(A) 19(9):1748–1754
Pizlo Z, Rosenfeld A (1992) Recognition of planar shapes from perspective images using contour-based invariants[J]. CVGIP: Imag Understanding 3(56):330–350
Qjidaa H (2006) Image reconstruction by Laguerre moments[C]. Proc Second Int Sym Commun, Contrl Sign Process ISCCSP’06
Reddi SS (1981) Radial and angular moment invariants for image identification[J]. IEEE Trans Pattern Anal Mach Intell 3(2):240–242
Sheng YL, Arsenault HH (1987) Noisy-image normalization using low-order radial moments of circular-harmonic function[J]. J Optic Soc Am 4(7):1176–1184
Sheng YL, Shen LX (1994) Orthogonal Fourier-Mellin moments for invariant pattern recognition[J]. J Optic Soc Am(A) 11(6):1748–1757
Sheng Y, Wang C (2014) Stability in p-th moment for uncertain differential equation [J]. J Intell Fuzzy Syst 26(3):1263–1271
Suk T, Flusser J (2004) Projective moment invariants[J]. IEEE Trans Pattern Anal Mach Intell 26:1364–1367
Suk T, Flusser J (2011) Affine moment invariants generated by graph method[J]. Pattern Recogn 44:2047–2056
Teh CH, Chin RT (1988) On image analysis by the method of moments[J]. IEEE Trans Pattern Anal Mach Intell 10:496–513
Tsai CW, Liao MY, Yang CS et al (2013) Classification algorithms for interactive multimedia services: a review[J]. Multimed Tools Appl 67(1):137–165
Wu Y, Shen J (2005) Properties of orthogonal Gaussian-Hermite moments and their applications[J]. EURASIP J Appl Sign Process 4:588–599
Wu X, Wang D, Hui S (2004) An algorithm and implementaion for obtaining minimum exterior rectangle of image region [J]. Comput Eng 30(12):124–126
Yap P-T, Paramesran R (2004) Jacobi moments as image features[C]. Proc Region 10 Conf TENCON’04: 594–597
Yap PT, Paramesran R, Ong SH (2003) Image analysis by Krawtchouk moments[J]. IEEE Trans Imag Process 12:1367–1377
Yu J-h, Jun-wei LV, X-m B (2011) A new method for ship image target recognition based on combined invariant moments [J]. INFRARED 9(32):23–28
Zeng W-m, Wu Q-x, Jiang C-s (2009) Recognition method of aerial targets based on combined invariant moments [J]. Electron Optics Contrl 7(16):21–24,44
Zhu H, Shu H, Zhou J, Luo L, Coatrieux JL (2007) Image analysis by discrete orthogonal dual Hahn moments[J]. Pattern Recogn Lett 28:1688–1704
Acknowledgments
This research has partially been supported by theproject funded of the Department of Transportation Informatization under Grant No. 2013-364-836-900, National Natural Science Foundation of China under Grant No. 71573107, 71471077, 41374129, 41474095, 60673190, 61502206, 61502208 and 61203244, Science Foundation of Jiangsu Province under Grant No. BK20150522 and BK20150523, College Natural Science Research of Jiangsu Province under Grant No. 14KJB520008, Senior Technical Personnel of Scientific Research Fund of Jiangsu University under Grant No. 13JDG126, Nature Science Foundation of Jiangsu Province under Grant No.SBK2015040772, Research Innovation Program for College Graduates of Jiangsu Province under Grant No. KYLX15_1078.
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Chen, Xj., Ke, J., Zhan, Yz. et al. Improved combined invariant moment for moving targets classification. Multimed Tools Appl 76, 19959–19982 (2017). https://doi.org/10.1007/s11042-016-4014-x
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DOI: https://doi.org/10.1007/s11042-016-4014-x