A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape
Tóm tắt
Từ khóa
Tài liệu tham khảo
Amiaz, T. and Kiryati, N. 2005. Dense discontinuous optical flow via contour-based segmentation. In Proc. IEEE Int. Conf. Image Processing, Lausanne, vol 4, pp. 1264–1267.
Aubert, G., Barlaud M., Faugeras, O., and Jehan-Besson, S. 2003. Image segmentation using active contours: Calculus of variations or shape gradients? SIAM Journal of Applied Mathematics, 63(6):2128–2154.
Besag, J. 1986. On the statistical analysis of dirty pictures. J. Roy. Statist. Soc., Ser. B., 48(3):259–302.
Bigün, J. and Granlund G. 1987. Optimal orientation detection of linear symmetry. In Proceedings of the 1st International Conference on Computer Vision, London, England, IEEE Computer Society Press, pp. 433–438.
Bigün, J., Granlund, G.H., and Wiklund, J. 1991. Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Trans. on Patt. Anal. and Mach. Intell., 13(8):775–790.
Boykov, Y., Veksler O., and Zabih, R. 2001. Fast approximate energy minimization via graph cuts. IEEE Trans. on Patt. Anal. and Mach. Intell., 23(11):1222–1239.
Brox, T., Bruhn, A., Papenberg, N., and Weickert, J. 2004. High accuracy optical flow estimation based on a theory for warping. In T. Pajdla and V. Hlavac, (Eds.), European Conf. on Computer Vision, Vol. 3024 of LNCS, Prague Springer, pp. 25–36.
Brox, T., Bruhn, A., and Weickert, J. 2006. Variational motion segmentation with level sets. In A. Leonardis, H. Bischof and A. Pinz (Eds.), European Conference on Computer Vision (ECCV), Graz, Austria, Springer, LNCS, Vol. 3951, pp. 471–483.
Brox, T., Rousson, M., Deriche, R., and Weickert, J. 2003. Unsupervised segmentation incorporating colour, texture, and motion. In N. Petkov and M. A. Westenberg (Eds.), Computer Analysis of Images and Patterns, vol. 2756 of LNCS, Groningen, The Netherlands Springer, pp. 353–360.
Brox, T. and Weickert, J. 2004. Level set based image segmentation with multiple regions. In H. Bülthoff, M. Giese, and B. Schölkopf (Eds.), In Pattern Recognition, Springer LNCS 3175, C.-E. Rasmussen 26th DAGM, Tübingen, Germany, pp. 415–423.
Brox, T. and Weickert, J. 2004. A TV flow based local scale measure for texture discrimination. In T. Pajdla and V. Hlavac, (eds.), European Conf. on Computer Vision, Vol. 3022 of LNCS, Prague,Springer, pp. 578–590.
Caselles, V., Catté, F., Coll, T., and Dibos, F. 1993. A geometric model for active contours in image processing. Numer. Math., 66:1–31.
Caselles, V., Kimmel, R., and Sapiro, G. 1995. Geodesic active contours. In Proc. IEEE Intl. Conf. on Comp. Vis., Boston, USA, pp. 694–699.
Chan, T.F., and Vese, L.A. 2001. Active contours without edges. IEEE Trans. Image Processing, 10(2):266–277.
Charpiat, G., Faugeras, O., and Keriven, R. 2005. Approximations of shape metrics and application to shape warping and empirical shape statistics. Journal of Foundations of Computational Mathematics, 5(1):1–58.
Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K. S., Briggs, R.W., and Geiser, E. 2002. Using shape priors in geometric active contours in a variational framework. Int. J. of Computer Vision, 50(3):315–328.
De Cock, K. and De Moor, B. 2000. Subspace angles between linear stochastic models. In Int. Conf. on Decision and Control, vol 2, pp. 1561–1566.
Cohen, L.D. and Cohen, I. 1993. Finite-element methods for active contour models and balloons for 2-d and 3-d images. IEEE Trans. on Patt. Anal. and Mach. Intell., 15(11):1131–1147
Cremers, D. 2003. A variational framework for image segmentation combining motion estimation and shape regularization. In C. Dyer and P. Perona (Eds.), IEEE Conf. on Comp. Vis. and Patt. Recog., vol 1, pp. 53–58.
Cremers, D. 2006. Dynamical statistical shape priors for level set based tracking. IEEE Trans. on Patt. Anal. and Mach. Intell., 28(8):1262–1273.
Cremers, D., Kohlberger, T., and Schnörr, C. 2002. Nonlinear shape statistics in Mumford–Shah based segmentation. In A. Heyden et al. (Eds.), Europ. Conf. on Comp. Vis., vol. 2351 of LNCS, Copenhagen Springer, pp. 93–108.
Cremers, D., Osher, S.J., and Soatto, S. 2006. Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. Int. J. of Computer Vision, 69(3):335–351.
Cremers, D. and Schnörr, C. 2002. Motion Competition: Variational integration of motion segmentation and shape regularization. In L. van Gool (Ed.), Pattern Recognition, vol. 2449 of LNCS, Zürich, Springer, pp. 472–480.
Cremers, D. and Soatto, S. 2003. Variational space-time motion segmentation. In B. Triggs and A. Zisserman (Eds.), IEEE Int. Conf. on Computer Vision, Nice, vol. 2, pp. 886–892.
Cremers, D., and Soatto, S. 2005. Motion Competition: A variational framework for piecewise parametric motion segmentation. Int. J. of Computer Vision, 62(3):249–265.
Cremers, D., Sochen, N., and Schnörr, C. 2006. A multiphase dynamic labeling model for variational recognition-driven image segmentation. Int. J. of Computer Vision, 66(1):67–81.
Cremers, D., Tischhäuser, F., Weickert, J., and Schnörr, C. 2002. Diffusion Snakes: Introducing statistical shape knowledge into the Mumford–Shah functional. Int. J. of Computer Vision, 50(3):295–313.
Cremers, D. and Yuille, A.L. 2003. A generative model based approach to motion segmentation. In B. Michaelis and G. Krell (Eds.), Pattern Recognition, vol. 2781 of LNCS, Magdeburg, Springer, pp. 313–320.
Cross, G. and Jain, A. 1983. Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:25–39.
de Luis García, R., Deriche, R., Rousson, M., and Alberola-López, C. 2005. Tensor processing for texture and colour segmentation. Scandinavian Conference on Image Analysis 2005: 1117–1127.
Delingette, H., and Montagnat, J. 2000. New algorithms for controlling active contours shape and topology. In D. Vernon, (Ed.), Proc. of the Europ. Conf. on Comp. Vis., vol. 1843, of LNCS, Springer, pp. 381–395.
Dervieux, A. and Thomasset, F. 1979. A finite element method for the simulation of Raleigh-Taylor instability. Springer Lect. Notes in Math., 771:145–158.
Dervieux, A. and Thomasset, F. 1981. Multifluid incompressible flows by a finite element method. Lecture Notes in Physics, 11:158–163
Doretto, G., Chiuso, A., Wu, Y.N., and Soatto, S. 2003. Dynamic textures. Int. Journal of Computer Vision, 51(2):91–109.
Doretto, G., Cremers, D., Favaro, P., and Soatto, S. 2003. Dynamic texture segmentation. In B. Triggs and A. Zisserman (Eds.), IEEE Int. Conf. on Computer Vision, Nice, vol 2, pp. 1236–1242.
Duci, A., Yezzi, A., Mitter, S., and Soatto, S. 2003. Shape representation via harmonic embedding. In ICCV, pp. 656–662.
Förstner, M.A., and Gülch, E. 1987. A fast operator for detection and precise location of distinct points, corners and centers of circular features. In Proceedings of the Intercommission Workshop of the International Society for Photogrammetry and Remote Sensing, Interlaken, Switzerland.
Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Patt. Anal. and Mach. Intell., 6(6):721–741.
Grady, L. 2006. Random walks for image segmentation. IEEE Trans. on Patt. Anal. and Mach. Intell., to appear.
Granlund, G.H. and Knutsson, H. 1995. Signal Processing for Computer Vision, Kluwer Academic Publishers.
Grenander, U., Chow, Y., and Keenan, D.M. 1991. Hands: A Pattern Theoretic Study of Biological Shapes, Springer, New York.
Hassner, M. and Sklansky, J. 1980. The use of Markov random fields as models of texture. Computer Graphics and Image Processing, 12:357–370.
Heiler, M. and Schnoerr, C. 2003. Natural image statistics for natural image segmentation. In IEEE Int. Conf. on Computer Vision, pp. 1259–1266
Herbulot, A., Jehan-Besson, S., Barlaud, M., and Aubert, G. 2004. Shape gradient for multi-modal image segmentation using mutual information. In Int. Conf. on Image Processing.
Jehan-Besson, S., Barlaud, M., and Aubert, G. 2003. DREAM2S: Deformable regions driven by an eulerian accurate minimization method for image and video segmentation. Int. J. of Computer Vision, 53(1):45–70.
Jepson, A., and Black, M.J. 1993. Mixture models for optic flow computation. In Proc. IEEE Conf. on Comp. Vision Patt. Recog., pp. 760–761.
Kass, M., Witkin, A., and Terzopoulos, D. 1988. Snakes: Active contour models. Int. J. of Computer Vision, 1(4):321–331.
Keuchel, J., Schnörr, C., Schellewald, C., and Cremers, D. 2003. Binary partitioning, perceptual grouping, and restoration with semidefinite programming. IEEE Trans. on Patt. Anal. and Mach. Intell., 25(11):1364–1379.
Kichenassamy, S., Kumar, A., Olver, P.J., Tannenbaum, A., and Yezzi, A. J. 1995. Gradient flows and geometric active contour models. In IEEE Int. Conf. on Computer Vision, pp. 810–815.
Kim, J., Fisher, J.W., Yezzi, A., Cetin, M. and Willsky, A. 2002. Nonparametric methods for image segmentation using information theory and curve evolution. In Int. Conf. on Image Processing, vol. 3, pp. 797–800.
Lachaud, J.-O. and Montanvert, A. 1999. Deformable meshes with automated topology changes for coarse-to-fine three-dimensional surface extraction. Medical Image Analysis, 3(2):187–207.
Leclerc, Y.G. 1989. Constructing simple stable description for image partitioning. The International Journal of Computer Vision, 3(1):73–102
Leitner, F. and Cinquin, P. 1991. Complex topology 3d objects segmentation. In SPIE Conf. on Advances in Intelligent Robotics Systems, Boston, vol. 1609.
Lenglet, C., Rousson, M., and Deriche, R. 2004. Toward segmentation of 3D probability density fields by surface evolution: Application to diffusion MRI. INRIA Research Report.
Lenglet, C., Rousson, M., Deriche, R., and Faugeras, O. 2004. Statistics on multivariate normal distributions: A geometric approach and its application to diffusion tensor MRI. INRIA Research Report.
Leung, T. and Malik, J. 2001. Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. of Computer Vision, 43(1):29–44.
Leventon, M., Grimson, W., and Faugeras, O. 2000. Statistical shape influence in geodesic active contours. In Int. Conf. on Computer Vision and Pattern Recognition, Hilton Head Island, SC, vol. 1, pp. 316–323.
Malik, J., Belongie, S., Leung, T., and Shi, J. 2001. Contour and texture analysis for image segmentation. Int. J. of Computer Vision, 43(1):7–27.
Malladi, R., Sethian, J.A., and Vemuri, B.C. 1994a. Evolutionary fronts for topology-independent shape modeling and recovery. In Europ. Conf. on Computer Vision, vol. 1, pp. 3–13.
Malladi, R., Sethian, J.A., and Vemuri, B.C. 1994b. A topology independent shape modeling scheme. In SPIE Conf. on Geometric Methods in Comp. Vision II, vol. 2031, pp. 246–258.
Mallat, S. 1989. Multiresolution approximations and wavelet orthonormal bases of L 2(R). Trans. Amer. Math. Soc., 315:69–87.
Mansouri, A., Mitiche, A., and Feghali, R. 2002. Spatio-temporal motion segmentation via level set partial differential equations. In Proc. of the 5th IEEE Southewst Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe.
Martin, P., Refregier, P., Goudail, F., and Guerault, F. 2004. Influence of the noise model on level set active contour segmentation. IEEE Trans. on Patt. Anal. and Mach. Intell., 26(6):799–803.
McInerney, T. and Terzopoulos, D. 1995. Topologically adaptable snakes. In Proc. 5th Int. Conf. on Computer Vision, Los Alamitos, California, IEEE Comp. Soc. Press, pp. 840–845.
Mumford, D., and Shah, J. 1989. Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math., 42:577–685.
Nain, D., Yezzi, A., and Turk, G. 2003. Vessel segmentation using a shape driven flow. In Intl. Conf. on Medical Image Computing and Comp. Ass. Intervention (MICCAI), pp. 51–59.
Osher, S.J., and Sethian, J.A. 1988. Fronts propagation with curvature dependent speed: Algorithms based on Hamilton–Jacobi formulations. J. of Comp. Phys., 79:12–49.
Paragios, N. and Deriche, R. 2002. Geodesic active regions: a new paradigm to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation, 13(1/2):249–268.
Paragios, N. and Deriche, R. 2005. Geodesic active regions and level set methods for motion estimation and tracking. Computer Vision and Image Understanding, 97(3):259–282.
Pennec, X., Fillard, P., and Ayache, N. 2005. A Riemannian framework for tensor computing. International Journal of Computer Vision, 65(1).
Perkins, W.A. 1980. Area segmentation of images using edge points. IEEE Trans. on Patt. Anal. and Mach. Intell., 2(1):8–15.
Rao, A.R., and Schunck, B.G. 1991. Computing oriented texture fields. CVGIP: Graphical Models and Image Processing, 53:157–185.
Rochery, M., Jermyn, I., and Zerubia, J. 2006. Higher order active contours. Int. J. of Computer Vision, 69(1): 1573–1405.
Rousson, M. 2004. Cues Integrations and Front Evolutions in Image Segmentation. PhD thesis, Université de Nice-Sophia Antipolis.
Rousson, M., Brox, T., and Deriche, R. 2003. Active unsupervised texture segmentation on a diffusion based feature space. In Proc. IEEE Conf. on Comp. Vision Patt. Recog., Madison, WI, pp. 699–704.
Rousson, M. and Cremers, D. 2005. Efficient kernel density estimation of shape and intensity priors for level set segmentation. In Intl. Conf. on Medical Image Computing and Comp. Ass. Intervention (MICCAI), vol. 1, pp. 757–764.
Rousson, M. and Deriche, R. 2002. A variational framework for active and adaptative segmentation of vector valued images. RR 4515, INRIA.
Rousson, M. and Deriche, R. 2002. A variational framework for active and adaptive segmentation of vector valued images. In Proc. IEEE Workshop on Motion and Video Computing, Orlando, Florida, pp. 56–62.
Rousson, M., Lenglet, C., and Deriche, R. 2004. Level set and region based surface propagation for diffusion tensor MRI segmentation. In Computer Vision Approaches to Medical Image Analysis (CVAMIA) and Mathematical Methods in Biomedical Image Analysis (MMBIA) Workshop, Prague.
Rousson, M. and Paragios, N. 2002. Shape priors for level set representations. In A. Heyden et al. (Eds.), Europ. Conf. on Comp. Vis., Springer, vol. 2351 of LNCS, pp. 78–92.
Rousson, M., Paragios, N., and Deriche, R. 2004. Implicit active shape models for 3d segmentation in MRI imaging. In Intl. Conf. on Medical Image Computing and Comp. Ass. Intervention (MICCAI), Springer, vol. 2217 of LNCS, pp. 209–216.
Samson, C., Blanc-Feraud, L., Aubert, G., and Zerubia, J. 2000. A variational model for image classification and restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(5):460–472.
Schnoerr, C. 1992. Computation of discontinuous optical flow by domain decomposition and shape optimization. Int. J. of Computer Vision, 8(2):153–165.
Shi, J. and Malik, J. 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888–905.
Simoncelli, P., Freeman, W., Adelson, H., and Heeger, J. 1992. Shiftable multiscale transforms. IEEE trans. on Information Theory, 38:587–607.
Skovgaard, L.T. 1984. A Riemannian geometry of the multivariate normal model. Scand. J. Statistics, 11:211–223.
Sokolowski, J. and Zolesio, J.P. 1991. Introduction to shape optimization. Computational Mathematics. Springer Verlag.
Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, E., and Willsky, A. 2001. Model–based curve evolution technique for image segmentation. In Comp. Vision Patt. Recog., Kauai, Hawaii. pp. 463–468.
Tsai, A., Yezzi, A.J., and Willsky, A.S. 2001. Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. on Image Processing, 10(8):1169–1186.
Tsai, A., Yezzi, A. J., and Willsky, A. S. 2003. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. on Medical Imaging, 22(2):137–154.
Tschumperlé, D. and Deriche, R. 2001a. Diffusion tensor regularization with constraints preservation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii.
Tschumperlé, D. and Deriche, R. 2001b. Regularization of orthonormal vector sets using coupled PDE’s. In IEEE Workshop on Variational and Level Set Methods, Vancouver, Canada, pp. 3–10.
Unal, G., Krim, H., and Yezzi, A.Y. 2005. Information-theoretic active polygons for unsupervised texture segmentation. Int. J. of Computer Vision.
Vese, L.A., 2003. Multiphase object detection and image segmentation. In S. J. Osher and N. Paragios (Eds.), Geometric Level Set Methods in Imaging, Vision and Graphics, New York, Springer, pp. 175–194.
Vese, L.A., and Chan, T.F. 2002. A multiphase level set framework for image segmentation using the Mumford and Shah model. The International Journal of Computer Vision, 50(3):271–293.
Wang, J.Y.A., and Adelson, E.H. 1994. Representating moving images with layers. IEEE Trans. on Image Processing, 3(5):625–638.
Wang, Z. and Vemuri, B.C. 2004. An affine invariant tensor dissimilarity measure and its application to tensor-valued image segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC.
Weickert, J. and Brox, T. 2002. Diffusion and regularization of vector and matrix-valued images. In Contemporary Mathematics, vol. 313, pp. 251–268.
Yezzi, A., Tsai, A., and Willsky, A. 1999. A statistical approach to snakes for bimodal and trimodal imagery. In Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, vol. II, pp. 898–903.
Di Zenzo, S. 1986. A note on the gradient of a multi-image. Computer Vision, Graphics, and Image Processing, 33:116–125.
Zhao, H.-K., Chan, T., Merriman, B., and Osher, S. 1996. A variational level set approach to multiphase motion. J. of Comp. Phys., 127:179–195.
Zhu, S.C., Wu, Y., and Mumford, D. 1998. Filters, random fields and maximum entropy (frame). The International Journal of Computer Vision, 27(2):1–20.