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Multimodal Scene Reconstruction Using Genetic Algorithm-Based Optimization

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Optimization Techniques in Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

Many applications require the use of 3D graphics to create models of real environments. These models are usually built from range or depth images. In the scene modeling process, the use of additional 2D digital sensorial information leads to multimodal scene representation, where an image acquired by a 2D sensor is used as a texture map for a geometric model of a scene. In this chapter we present, as an example of optimization, a photo-realistic scene reconstruction procedure using laser range data and color photographs.

The reconstruction system involves the creation of triangle meshes from range images as a scene surface representation, but the main emphasis is made on the registration of laser range and photographic images. Major 3D data acquisition techniques are discussed in Appendix C, and a real range data is acquired by using a light amplitude detection and ranging (LADAR) range scanner.

The proposed multimodal image registration approach uses random distributions of pixels to measure the amount of dependence between two images and estimates the relative pose of one imaging system to the other. The similarity metric used in the proposed automatic registration algorithm is based on the χ 2 measure of dependence, which is presented as an alternative to the standard mutual information criterion. These two criteria belong to the class of information theoretic similarity measures, which quantify the dependence in terms of the information provided by one image about the other. For the maximization of the similarity measure, a robust optimization scheme is needed. To achieve both accurate and robust results, genetic algorithms are investigated in the heuristic manner.

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References

  1. M.A. Abidi, R.C. Gonzalez, Data Fusion in Robotics and Machine Intelligence (Academic, Boston, MA, 1992)

    MATH  Google Scholar 

  2. R. Basri, D. Weinshall, Distance metric between 3D models and 2D images for recognition and classification. IEEE Trans. Pattern Anal. Mach. Intell. 18(4), 465–470 (1996)

    Article  Google Scholar 

  3. R. Battiti, G. Tecchiolli, The reactive Tabu search. ORSA J. Comput. 6(2), 126–140 (1994)

    Article  MATH  Google Scholar 

  4. D. Beasly, D.S. Bull, R.R. Martin, An overview of genetic algorithms: part 1, fundamentals. Univ. Comput. 15(2), 58–69 (1993)

    Google Scholar 

  5. D. Beasly, D.S. Bull, R.R. Martin, An overview of genetic algorithms: part 2, research topics. Univ. Comput. 15(4), 170–181 (1993)

    Google Scholar 

  6. P.J. Besl, N.D. McKay, A method for registration of 3D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)

    Article  Google Scholar 

  7. R.R. Brooks, S.S. Iyenger, J.C. Chen, Automatic correlation and calibration of noisy sensor readings using elite genetic algorithms. Artif. Intell. 84, 339–354 (1996)

    Article  Google Scholar 

  8. A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, and G. Marchal, Automated multimodality medical image registration using information theory, in Proceeding of the 14th International Conference Information Processing in Medical Imaging; Computational Imaging and Vision 3 (1995), pp. 263–274

    Google Scholar 

  9. O. Faugeras, Three-Dimensional Computer Vision (The MIT Press, Cambridge, MA, 1993)

    Google Scholar 

  10. J. Feldmar, N. Ayache, F. Betting, 3D-2D registration of free-form curves and surfaces. Comput. Vis. Image Underst. 65(3), 403–424 (1997)

    Article  Google Scholar 

  11. F. Glover, M. Laguna, Robotics, Tabu Search (Kluwer’s Academic Publishers, Boston, 1997)

    Book  MATH  Google Scholar 

  12. R.M. Haralick, L.G. Shapiro, Computer and Robot Vision (Addison-Wesley Publishing Company, Michigan, 1993)

    Google Scholar 

  13. G. Haüsler, D. Ritter, Feature-based object recognition and localization in 3D space using a single video image. Comput. Vis. Image Underst. 73(1), 64–81 (1999)

    Article  MATH  Google Scholar 

  14. B.K.P. Horn, Robot Vision (The MIT Press, Cambridge, MA, 1986)

    Google Scholar 

  15. M. Irani and P. Anandan, Robust Multi-sensor Image Alignment, Technical Report, The Weizmann Institute of Science, 1998

    Google Scholar 

  16. Q. Ji, M.S. Costa, R.M. Haralick, and L.G. Shapiro, An Integrated Linear Technique for Pose Estimation from Different Geometric Features, Technical Report, University of Washington, Department of Electrical and Computer Engineering, 1998

    Google Scholar 

  17. A.E. Johnson and S.B. Kang, Registration and integration of textured 3D data, in Proceedings of the International Conference of Recent Advances in 3D Digital Imaging and Modeling (1998), pp. 331–338

    Google Scholar 

  18. K. Kanatani, N. Ohta, Optimal robot self-localization and reliability evaluation, in Proceedings of the European Conference Computer Vision, 1998 (Springer, Berlin, 1998), pp. 796–808

    Google Scholar 

  19. R.E. Kass, P.W. Vos, M.S.J. Tsao, Geometrical Foundations of Asymptotic Inference (John Wiley & Sons, New York, 1997)

    Book  MATH  Google Scholar 

  20. S. Lavallée, R. Szeliski, Recovering the position and orientation of free-form objects from image contours using 3D distance maps. IEEE Trans. Pattern Anal. Mach. Intell. 17(4), 378–390 (1995)

    Article  Google Scholar 

  21. G. Lohmann, Volumetric Image Analysis (John Wiley & Sons and B. G. Teubner Publishers, New York, 1998)

    MATH  Google Scholar 

  22. A.P. Mangan, Photo-Realistic Surface Reconstruction, Master’s Thesis, University of Tennessee, Knoxville, 1999

    Google Scholar 

  23. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, P. Suetens, Multi-modality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187–198 (1997)

    Article  Google Scholar 

  24. M. Maimone, L. Matthies, J. Osborn, E. Rollins, J. Teza, and S. Thayer, A Photo-Realistic Mapping System for Extreme Nuclear Environments: Chornobyl, IEEE/RSJ International Conference on Intelligent Robotic Systems (1998)

    Google Scholar 

  25. S. Maybank, Theory of Reconstruction from Image Motion (Springer, Berlin, 1993)

    Book  MATH  Google Scholar 

  26. D.P. McGarry, T.R. Jackson, M.B. Plantec, N.F. Kassell, and J. Hunter Downs, III, Registration of Functional Magnetic Resonance Imagery using Mutual Information, Technical Report, University of Virginia, Charlottesville, Neuro-surgical Visualization Laboratory, 1996

    Google Scholar 

  27. C. Nikou et al., Robust registration of dissimilar single and multi-modal images, in Proceedings of the European Conference Computer Vision, 1998 (Springer, Berlin, 1998), pp. 51–63

    Google Scholar 

  28. W.H. Press, B.P. Flannery, S.A. Teukolsky, W.T. Vetterling, Numerical Recipes in C, 2nd edn. (Cambridge University Press, New York, 1992)

    MATH  Google Scholar 

  29. A. Roche, G. Malandin, X. Pennec, and N. Ayache, Multi-modal Image Registration by Maximization of the Correlation Ratio, Research Report, Institut National de Recherche en informatique et en Automatique, No. 3378, 1998

    Google Scholar 

  30. D.F. Rogers, R.A. Earnshaw, State of the Art in Computer Graphics: Visualization and Modeling (Springer, Berlin, 1991)

    Book  MATH  Google Scholar 

  31. V. Sequira, E. Wolfart, J.G.M. Gonclaves, D. Hogg, Automated reconstruction of 3D models from real environments. ISPRS J. Photogramm. Remote Sens. 54, 1–22 (1999)

    Article  Google Scholar 

  32. C. Stoker, T. Blackmon, J. Hagen, B. Kanefsky, D. Rasmussen, K. Schwehr, M. Sims, E. Zbinden, MARSMAP: An Interactive Virtual Reality Model of the Pathfinder Landing Site (NASA Ames Research Center, Moffett Field, CA, 1998)

    Google Scholar 

  33. M.R. Stevens, J.R. Beveridge, Precise matching of 3D target models to multi-sensor data. IEEE Trans. Med. Imaging 6(1), 126–142 (1997)

    Google Scholar 

  34. R. Szeliski, Video mosaics for virtual environments, IEEE Comput. Graph. Appl., 22–30 (1996)

    Google Scholar 

  35. R.Y. Tsai, A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robot. Autom. 3(4), 323–344 (1987)

    Article  Google Scholar 

  36. M. Tuceryan, D.S. Greer, R.T. Whitaker, D.E. Breen, C. Crampton, E. Rose, K.H. Ahlers, Calibration requirements and procedures for a monitor-based augmented reality system. IEEE Trans. Vis. Comput. Graph. 1, 255–273 (1995)

    Article  Google Scholar 

  37. R.J.M. Vaessens, E.H.L. Aarts, J.K. Lenstra, A local search template. Comput. Oper. Res. 25(11), 969–979 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  38. I. Vajda, Theory of Statistical Inference and Information (Kluwer, Boston, MA, 1989)

    MATH  Google Scholar 

  39. P. Viola, W. Wells III, Alignment by maximization of mutual information. Proc. IEEE Int. Conf. Comput. Vis. 24(2), 137–154 (1997)

    Article  Google Scholar 

  40. R.P. Woods, J.C. Mazziotta, S.R. Cherry, MRI-PET registration with automated algorithm. J. Comput. Assist. Tomogr. 17(4), 536–546 (1993)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, Tennessee, USA

    Mongi A. Abidi

  2. Department of Human Factors, Controls, and Statistics, Idaho National Laboratory, Idaho Falls, Idaho, USA

    Andrei V. Gribok

  3. Image Processing and Intelligent Systems Laboratory, Chung-Ang University, Seoul, Korea

    Joonki Paik

Authors
  1. Mongi A. Abidi
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  3. Joonki Paik
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Abidi, M.A., Gribok, A.V., Paik, J. (2016). Multimodal Scene Reconstruction Using Genetic Algorithm-Based Optimization. In: Optimization Techniques in Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-46364-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-46364-3_12

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