Prof. Mireille Boutin
 

Recognition and Symmetry Detection for Discrete Objects

Invariant-based methods for object recognition and symmetry detection.



Symmetry Detection in Images using the Pascal Triangle



  1. •M. Boutin, S. Huang, “The Pascal Triangle of a Discrete Image: Definition, Properties and Application to Shape Analysis,” Symmetry, Integrability and Geometry: Methods and Applications (SIGMA) (Special issue on “Symmetries of Differential Equations: Frames, Invariants and Applications,”) Vol. 9, 031 (25 pages), 2013.

  2. •A. Parra Pozo, B. Zhao, A. Haddad, M. Boutin, E.J. Delp, “Hazardous material sign detection and recognition,” IEEE International Conference on Image Procession (ICIP), Melbourne, Australia, September 15–19, 2013


  1. •A. W. Haddad, S. Huang, M. Boutin, E. J. Delp, “Detection of Symmetric Shapes on a Mobile Device with Application to Automatic Sign Interpretation,” IS&T/SPIE Joint Symposium, Multimedia on Mobile Devices conference, San Francisco, CA, January 2012.


Object Recognition using Invariant Statistics



  1. •M. Boutin and G. Kemper, “On Reconstructing n-point Configurations from the Distribution of Distances of Areas,” Advances in Applied Mathematics, Vol. 32, pp. 709-735, 2004.


  1. •M. Boutin and G. Kemper, “On Reconstructing Configurations of Points in P2 from a Joint Distribution of Invariants,” Journal of Applicable Algebra In Engineering, Communication and Computing, Vol. 15, No. 6, pp. 361-391, 2005.


  1. •C.-H. Park, M. J. T. Smith, M. Boutin, and J.-J. Lee, “Fingerprint Matching Using the Distribution of the Pairwise Distances Between Minutiae,” Audio and Video-based Biometric Person Authentication (AVBPA) 2005, Lecture Notes in Computer Science, Vol. 3546, pp. 693-701, 2005.


  1. •M. Boutin, K. Lee, M. Comer, “Lossless Shape Representation Using Invariant Statistics: the Case of Point-sets,” Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, October 29–November 1, 2006.


  1. •M. Boutin and M. Comer, “Faithful Shape Representation for 2D Gaussian Mixtures,” IEEE International Conference on Image Processing (ICIP), San Antonio, TX, September 16–19, 2007.


  1. •M. Boutin and G. Kemper, “Which Point Configurations are Reconstructible from the Distribution of their Pairwise Distances?,” International Journal of Computational Geometry and Applications, Vol. 17, No. 1, pp. 31-43, 2007.


  1. •M. Boutin and G. Kemper, “Lossless Representation of Graphs using Distributions,” arXiv manuscript #arXiv:0710.1870, 2007.


  1. •H. Santos-Villalobos and M. Boutin, “An Empirical Method for Comparing the Shape of Two Gaussian Mixtures,” IEEE International Conference on Image Procession (ICIP), Hong Kong, China, September 26–29, 2010.

  2. •H. Santos Villalobos and M. Boutin, “A Method for Recognizing the Shape of a Gaussian Mixture from a Sparse Sample Set,” IS&T/SPIE Joint Symposium, Computational Imaging VIII, San Jose, CA, January 2010.


  1. •H. Santos-Villalobos, M. Boutin, “A Computationally Efficient Method to Compare the Shape of Planar Gaussian Mixtures From Point Samples,” Journal of Electronic Imaging, Vol. 21, No. 2, 023023, June 22, 2012.



Object Recognition using Invariant Signatures



  1. •M. Boutin, “Numerically Invariant Signature Curves, ”International Journal of Computer Vision, Vol. 40, No.3, pp. 235-248, 2000.


  1. •M.Boutin,“On Orbit Dimensions Under a Simultaneous Lie Group Action on n Copies of a Manifold,”Journal of Lie Theory, Vol. 12, pp. 191-203, 2002.


  1. •M. Boutin, “Joint Invariant Signatures for Curve Recognition,” Inverse Problems, Image Analysis and Medical Imaging, M.Z. Nashed and O. Schetzer, editors, Contemporary Mathematics, Vol. 313, American Mathematical Society, 2002, pp. 37-52.

  2. •M. Boutin, “Polygon Recognition and Symmetry Detection,” Foundations of Computational Mathematics, Vol. 3, pp. 227-271, 2003.