This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Tensor Voting for Image Correction by Global and Local Intensity Alignment
January 2005 (vol. 27 no. 1)
pp. 36-50
ASCII Text
x 
 
Jiaya Jia, Chi-Keung Tang, "Tensor Voting for Image Correction by Global and Local Intensity Alignment," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 36-50, January, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.20,
author = {Jiaya Jia and Chi-Keung Tang},
title = {Tensor Voting for Image Correction by Global and Local Intensity Alignment},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {1},
issn = {0162-8828},
year = {2005},
pages = {36-50},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.20},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Tensor Voting for Image Correction by Global and Local Intensity Alignment
IS - 1
SN - 0162-8828
SP36
EP50
EPD - 36-50
A1 - Jiaya Jia,
A1 - Chi-Keung Tang,
PY - 2005
KW - Image correction and recovery
KW - color transfer
KW - replacement functions
KW - applications.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Jiaya Jia, IEEE Computer Society
Chi-Keung Tang, IEEE Computer Society
This paper presents a voting method to perform image correction by global and local intensity alignment. The key to our modeless approach is the estimation of global and local replacement functions by reducing the complex estimation problem to the robust 2D tensor voting in the corresponding voting spaces. No complicated model for replacement function (curve) is assumed. Subject to the monotonic constraint only, we vote for an optimal replacement function by propagating the curve smoothness constraint using a dense tensor field. Our method effectively infers missing curve segments and rejects image outliers. Applications using our tensor voting approach are proposed and described. The first application consists of image mosaicking of static scenes, where the voted replacement functions are used in our iterative registration algorithm for computing the best warping matrix. In the presence of occlusion, our replacement function can be employed to construct a visually acceptable mosaic by detecting occlusion which has large and piecewise constant color. Furthermore, by the simultaneous consideration of color matches and spatial constraints in the voting space, we perform image intensity compensation and high contrast image correction using our voting framework, when only two defective input images are given.

[1] M. Ben-Ezra and S.K. Nayar, “Motion Deblurring Using Hybrid Imaging,” Computer Vision and Pattern Recognition, 2003.
[2] P.J. Burt and E.H. Adelson, “A Multiresolution Spline with Applications to Image Mosaics,” ACM Trans. Graphics, vol. 2, no. 4, pp. 217-236, Oct. 1983.
[3] R. Chellappa and A.K. Jain, Markov Random Fields: Theory and Applications. Academic Press, 1993.
[4] D. Comanicu and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
[5] J. Davis, “Mosaics of Scenes with Moving Objects,” Computer Vision and Pattern Recognition, 1998.
[6] P. Debevec and J. Malik, “Recovering High Dynamic Range Radiance Maps from Photographs,” Proc. SIGGRAPH, pp. 369-378, 1997.
[7] A. Edirisinghe, G.E. Chapman, and J.P. Louis, “Radiometric Corrections for Multispectral Airborne Video Imagery,” Photogrammetric Eng. & Remote Sensing, vol. 67, no. 8, pp. 915-924, Aug. 2001.
[8] P. Shirley, E. Reinhard, M. Stark, and J. Ferwerda, “ Photographic Tone Reproduction for Digital Images,” Proc. SIGGRAPH, pp. 267-276, 2002.
[9] R. Fabian and D. Malah, “Robust Identification of Motion and Out-of-Focus Blur Parameters from Blurred and Noisy Images,” CVGIP: Graphical Models and Image Processing, 1991.
[10] M.D. Grossberg and S.K. Nayar, “What Can Be Known about the Radiometric Response Function from Images?” Proc. European Conf. Computer Vision, May 2002.
[11] D. Hasler and S. Susstrunk, “Colour Handling in Panoramic Photography,” Proc. SPIE, Jan. 2001.
[12] G.E. Healey and R. Kondepudy, “Radiometric CCD Camera Calibration and Noise Estimation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 3, pp. 267-276, Mar. 1994.
[13] J. Jia, J. Sun, C.-K. Tang, and H.-Y. Shum, “Bayesian Correction of Image Luminance with Spatial consideration,” Proc. European Conf. Computer Vision, 2004.
[14] J. Jia and C.-K. Tang, “Luminance Voting: Image Registration with Global and Local Luminance Alignment,” Proc. Int'l Conf. Computer Vision, 2003.
[15] D. Kundur and D. Hatzinakos, “A Novel Blind Deconvolution Scheme for Image Restoration Using Recursive Filtering,” IEEE Trans. Signal Processing, vol. 46, no. 2, pp. 375-390, Feb. 1998.
[16] S. Mann and R. Mann, “Quantigraphic Imaging: Estimating the Camera Response and Exposures from Differently Exposed Images,” Computer Vision and Pattern Recognition, Dec. 2001.
[17] S. Mann and R. Piccard, “Being “Undigital” with Digital Cameras: Extending the Dynamic Range by Combining Differently Exposed Pictures,” Proc. IST 48th Ann. Conf., pp. 422-428, May 1995.
[18] G. Medioni, M.S. Lee, and C.K. Tang, A Computational Framework for Segmentation and Grouping. Elsevier Science B.V., 2000.
[19] G. Messina, A. Castorina, S. Battiato, and A. Bosco, “Image Quality Improvement by Adaptive Exposure Correction Techniques,” Proc. IEEE Int'l Conf. Multimedia and Expo, 2003.
[20] T. Mitsunaga and S. Nayar, “Radiometric Self Calibration,” Computer Vision and Pattern Recognition, pp. 374-380, 1999.
[21] M. Gooch, B. Reinhard, E. Ashikhmin, and P. Shirley, “Color Transfer between Images,” IEEE Computer Graphics and Applications, pp. 34-40, 2001.
[22] H. Sawhney and S. Ayer, “Compact Representations of Videos through Dominant and Multiple Motion Estimation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 8, pp. 814-830, Aug. 1996.
[23] Y.Y. Schechner and S.K. Nayar, “Generalized Mosaicing: Wide Field of View Multispectral Imaging,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 10, pp. 1334-1348, Oct. 2002.
[24] W. J. Smith, Modern Optical Engineering, third ed. McGraw-Hill, 2000.
[25] R. Szeliski, “Video Mosaics for Virtual Environments,” IEEE Computer Graphics and Applications, pp. 22-30, Mar. 1996.
[26] R. Szeliski and H.-Y. Shum, “Creating Full View Panoramic Image Mosaics and Environment Maps,” Proc. SIGGRAPH, pp. 251-258, 1997.
[27] R. Szeliski and H.-Y. Shum, “Construction of Panoramic Image Mosaics with Global and Local Alignment,” Int'l J. Computer Vision, vol. 36, no. 2, pp. 101-130, 2000.
[28] Y. Tsin, V. Ramesh, and T. Kanade, “Statistical Calibration of CCD Imaging Process,” Proc. Int'l Conf. Computer Vision, July 2001.
[29] M. Uyttendaele, A. Eden, and R. Szeliski, “Eliminating Ghosting and Exposure Artifacts in Image Mosaics,” Computer Vision and Pattern Recognition, 2001.
[30] A. Lantzman, Y. Yitzhaky, I. Mor, and N.S. Kopeika, “Direct Method for Restoration of Motion-Blurred Images,” J. Optical Soc. Am., A, vol. 15, no. 6, pp. 1512-1519, June 1998.
[31] Y. Levy, Y. Yitzhaky, G. Boshusha, and N.S. Kopeika, “Restoration of an Image Degraded by Vibrations Using only a Single Frame,” Optical Eng., 2002.
[32] Z. Zhu, E.M. Riseman, and A.R. Hanson, “Parallel-Perspective Stereo Mosaics,” Proc. Int'l Conf. Computer Vision, July 2001.

Index Terms:
Image correction and recovery, color transfer, replacement functions, applications.
Citation:
Jiaya Jia, Chi-Keung Tang, "Tensor Voting for Image Correction by Global and Local Intensity Alignment," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 36-50, Jan. 2005, doi:10.1109/TPAMI.2005.20
Usage of this product signifies your acceptance of the Terms of Use.