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Tracking Nonrigid Motion and Structure from 2D Satellite Cloud Images without Correspondences
November 2001 (vol. 23 no. 11)
pp. 1330-1336

Abstract—Tracking both structure and motion of nonrigid objects from monocular images is an important problem in vision. In this paper, a hierarchical method which integrates local analysis (that recovers small details) and global analysis (that appropriately limits possible nonrigid behaviors) is developed to recover dense depth values and nonrigid motion from a sequence of 2D satellite cloud images without any prior knowledge of point correspondences. This problem is challenging not only due to the absence of correspondence information but also due to the lack of depth cues in the 2D cloud images (scaled orthographic projection). In our method, the cloud images are segmented into several small regions and local analysis is performed for each region. A recursive algorithm is proposed to integrate local analysis with appropriate global fluid model constraints, based on which a structure and motion analysis system, SMAS, is developed. We believe that this is the first reported system in estimating dense structure and nonrigid motion under scaled orthographic views using fluid model constraints. Experiments on cloud image sequences captured by meteorological satellites (GOES-8 and GOES-9) have been performed using our system, along with their validation and analyses. Both structure and 3D motion correspondences are estimated to subpixel accuracy. Our results are very encouraging and have many potential applications in earth and space sciences, especially in cloud models for weather prediction.

[1] J.K. Aggarwal and R.O. Duda, “Computer Analysis of Moving Polygonal Images,” IEEE Trans. Computers, vol. 24, no. 10, pp. 966-976, Oct. 1975.
[2] R. Chellappa, “Structure from Motion: Light at the End of Tunnel!” Proc. Int'l Conf. Image Processing, p. 26PS1, 1999.
[3] E.M. Emin and P. Perez, “Fluid Motion Recovery by Coupling Dense and Parametric Vector Fields,” Proc. IEEE CS Int'l Conf. Computer Vision, pp. 620-625, 1999.
[4] R.M. Ford and R.N. Strickland, “Representing and Visualizing Fluid-Flow Images and Velocimetry Data by Nonlinear Dynamical-Systems,” Graphical Models and Image Processing, vol. 57, no. 6, pp. 462-482, Nov. 1995.
[5] T. Fujita, D.L. Bradbury, C. Murino, and L. Mull, “A Study of Mesoscale Cloud Motions Computed from ATS-1 and Terrestrial Photographs from Satellite,” Mesometeorological Research Project Research Paper No. 71, Dept. of Geophysical Sciences, Univ. of Chicago, p. 25, 1968.
[6] A.F. Hasler, “Stereographic Observations from Satellites: An Important New Tool for the Atmospheric Sciences,” Bull. Am. Meteorological Soc., vol. 62, pp. 194-212, 1981.
[7] A.F. Hasler, “Stereoscopic Measurements.” Weather Satellites: Systems, Data and Environmental Applications, Section VII-3, P.K. Rao, S.J. Holms, R.K. Anderson, J. Winston, and P. Lehr, eds., pp. 231-239, Boston: Am. Meteorlogical Soc., 1990.
[8] A.F. Hasler and K.R. Morris, “Hurricane Structure and Wind Fields from Stereoscopic and Infrared Satellite Observations and Radar Data,” J. Climate Applied Meteorology, vol. 25, pp. 709-727, 1986.
[9] A.F. Hasler, K. Palaniappan, C. Kambhamettu, P. Black, E. Uhlhorn, and D. Chesters, “High-Resolution Wind Fields within the Inner Core and Eye of a Mature Tropical Cyclone from GOES 1-Min Images,” Bull. Am. Meteorological Soc., vol. 79, no. 11, pp. 2483-2496, Nov. 1998.
[10] D.P. Jorgensen, “Mesoscale and Convective-Scale Characteristics of Mature Hurricanes: Part I. General Observations by Research Aircraft,” J. Atmospheric Sciences, vol. 41, pp. 1268-1285, 1984.
[11] C. Kambhamettu, D.B. Goldgof, D. Terzopoulos, and T.S. Huang, “Nonrigid Motion Analysis,” Handbook of Pattern Recognition and Image Processing: Computer Vision, T. Young, ed., vol. II, pp. 405-430, San Diego, Calif.: Academic Press, 1994.
[12] C. Kambhamettu, K. Palaniappan, and A.F. Hasler, “Coupled, Multi-Resolution Stereo and Motion Analysis,” Proc. IEEE Int'l Symp. Computer Vision, pp. 43-48, Nov. 1995.
[13] C. Kambhamettu, K. Palaniappan, and A.F. Hasler, “Hierarchical Motion Decomposition for Cloud-Tracking,” Proc. AMS 17th Conf. Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, pp. 318-323, 2001.
[14] C. Kambhamettu, K. Palaniappan, and A.F. Hasler, “Automated Cloud-Drift Winds from GOES Images,” Proc. SPIE GOES-8 and Beyond, vol. 2812, 122-133, Aug. 1996.
[15] C.S. Kambhamettu, “Nonrigid Motion Analysis under Small Deformations,” PhD thesis, Dept. of Computer Science and Eng., Univ. of South Florida, Dec. 1994.
[16] J.A. Leese, C.S. Novak, and B.B. Clark, “An Automated Technique for Obtaining Cloud Motion from Geosynchronous Satellite Data Using Cross-Correlation,” J. Applied Meteorology, vol. 10, pp. 118-132, 1971.
[17] M. Maurizot, P. Bouthemy, and B. Delyon, “2D Fluid Motion Analysis from a Single Image,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 184-191, 1998.
[18] P. Minnis, P.W. Heck, and E.F. Harrison, “The 27-28 October 1986 Fire IFO Cirrus Case Study: Cloud Parameter Fields Derived from Satellite Data,” Monthly Weather Rev., vol. 118, pp. 2426-2447, 1990.
[19] K. Palaniappan, M. Faisal, C. Kambhamettu, and A.F. Hasler, “Implementation of an Automatic Semi-Fluid Motion Analysis Algorithm on a Massively Parallel Computer,” Proc. IEEE Int'l Parallel Processing Symp., pp. 864-872, 1996.
[20] K. Palaniappan, C. Kambhamettu, A.F. Hasler, and D.B. Goldgof, “Structure and Semi-Fluid Motion Analysis of Stereoscopic Satellite Images for Cloud Tracking,” Proc. Int'l Conf. Computer Vision, pp. 659-665, 1995.
[21] K. Palaniappan, J. Vass, and X. Zhuang, “Parallel Robust Relaxation Algorithm for Automatic Stereo Analysis,” Proc. SPIE Parallel and Distributed Methods for Image Processing II, pp. 958-962, 1998.
[22] A.E. Perry and M.S. Chong, “A Description of Eddying Motions and Flow Patterns Using Critical Point Concepts,” Ann. Rev. Fluid Mechanics, vol. 19, pp. 125-155, 1987.
[23] D.R. Phillips, E.A. Smith, and V.E. Suomi, “Comment on‘An Automated Technique for Obtaining Cloud Motion from Geosynchronous Satellite Data Using Cross-Correlation,’ J. Applied Meteorology, vol. 11, pp. 752-754, 1972.
[24] A.F. Hasler, R.A. Mack, and R.F. Adler, “Thunderstorm Cloud Top Observations Using Satellite Stereoscopy,” Monthly Weather Rev., vol. 111, pp. 1949-1964, 1983.
[25] E. Rodgers, R. Mack, and A.F. Hasler, “A Satellite Stereoscopic Technique to Estimate Tropical Cyclone Intensity,” Monthly Weather Rev., vol. 111, pp. 1599-1610, 1983.
[26] E.A. Smith and D.R. Phillips, “Automated Cloud Tracking Using Precisely Aligned Digital ATS Pictures,” IEEE Trans. Computers, vol. 21, pp. 715-729, 1972.
[27] C.S. Velden, “Winds Derived from Geostationary Satellite Moisture Channel Observations: Applications and Impact on Numerical Weather Prediction,” Meteorology and Atmospheric Physics, vol. 60, pp. 37-46, 1996.
[28] C.S. Velden, C.M. Hayden, S. Nieman, W.P. Menzel, S. Wanzong, and J. Goerss, “Upper-Tropospheric Winds Derived from Geostationary Satellite Water Vapor Observations,” Am. Meteorological Soc., vol. 78, pp. 173-195, 1997.
[29] C.S. Velden, T.L. Olander, and S. Wanzong, “The Impact of Multispectral GOES-8 Wind Information on Atlantic Tropical Cyclone Track Forecasts in 1995. Part 1: Dataset Methodology, Description and Case Analysis,” Monthly Weather Rev., 1998.
[30] J. Weng, N. Ahuja, and T.S. Huang, “3D Motion Estimation, Understanding and Prediction from Noisy Image Sequences,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, pp. 370-389, 1987.
[31] R.P. Wildes, M.J. Amabile, A. Lanzillotto, and T. Leu, “Recovering Estimates of Fluid Flow from Image Sequence Data,” Computer Vision and Image Understanding, vol. 80, pp. 246–266, 2000.
[32] G.S. Young and R. Chellappa, “3-D Motion Estimation Using a Sequence of Noisy Stereo Images: Models, Estimation, and Uniqueness Results,” Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 8, pp. 735–759, Aug. 1990.
[33] L. Zhou and C. Kambhamettu http://www.cis.udel.edu~vims, 2001.
[34] L. Zhou, C. Kambhamettu, and D.B. Goldgof, “Structure and Nonrigid Motion Analysis of Satellite Cloud Images,” Proc. Indian Conf. Computer Vision, Graphics, and Image Processing, pp. 285-291, Dec. 1998.
[35] L. Zhou, C. Kambhamettu, and D.B. Goldgof, “Extracting Nonrigid Motion and 3D Structure of Hurricanes from Satellite Image Sequences without Correspondences,” Computer Vision and Pattern Recognition, vol. ll, pp. 280-285, 1999.
[36] L. Zhou, “3D Nonrigid Motion Analysis from 2D Images,” PhD thesis, Dept. of Computer and Information Sciences, Univ. of Delaware, Feb. 2001.

Index Terms:
<p>Nonrigid objects, structure estimation, image motion estimation, fluid models.</p>
Citation:
Lin Zhou, Chandra Kambhamettu, Dmitry B. Goldgof, K. Palaniappan, A.F. Hasler, "Tracking Nonrigid Motion and Structure from 2D Satellite Cloud Images without Correspondences," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1330-1336, Nov. 2001, doi:10.1109/34.969121
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