文件列表:

toolbox_fast_marching (0, 2008-02-14)
toolbox_fast_marching\.DS_Store (21508, 2008-02-14)
toolbox_fast_marching\batch_landmarks_error.m (192, 2005-11-16)
toolbox_fast_marching\batch_propagation_mesh.m (284, 2007-11-05)
toolbox_fast_marching\batch_shape_meshing.m (87, 2007-11-06)
toolbox_fast_marching\callback_active_contour.m (462, 2007-09-30)
toolbox_fast_marching\compile_mex.m (1749, 2008-02-10)
toolbox_fast_marching\compute_alpha_map.m (908, 2007-10-09)
toolbox_fast_marching\compute_bending_invariant.m (1730, 2007-11-03)
toolbox_fast_marching\compute_distance_landmark.m (3554, 2005-11-17)
toolbox_fast_marching\compute_eccentricity_transform.m (3222, 2008-01-28)
toolbox_fast_marching\compute_edge_energy.m (2180, 2007-11-08)
toolbox_fast_marching\compute_geodesic.m (7148, 2008-01-28)
toolbox_fast_marching\compute_geodesic_mesh.m (4159, 2007-12-04)
toolbox_fast_marching\compute_heuristic_landmark.m (502, 2005-06-06)
toolbox_fast_marching\compute_heuristic_multiresolution.m (1457, 2008-01-28)
toolbox_fast_marching\compute_levelset_shape.m (3005, 2007-10-05)
toolbox_fast_marching\compute_saddle_points.m (1563, 2008-02-14)
toolbox_fast_marching\compute_shape_boundary.m (1982, 2008-01-28)
toolbox_fast_marching\compute_voronoi_triangulation.m (2806, 2008-01-28)
toolbox_fast_marching\compute_voronoi_triangulation_mesh.m (842, 2008-01-28)
toolbox_fast_marching\convert_distance_color.m (576, 2007-03-10)
toolbox_fast_marching\copying (17992, 2006-06-28)
toolbox_fast_marching\copyright (808, 2007-04-25)
toolbox_fast_marching\data (0, 2007-09-27)
toolbox_fast_marching\data\.DS_Store (6148, 2007-05-21)
toolbox_fast_marching\data\apple.gif (480, 2006-07-06)
toolbox_fast_marching\data\brain.png (33282, 2007-09-27)
toolbox_fast_marching\data\camel.gif (619, 2006-07-06)
toolbox_fast_marching\data\cavern.png (21131, 2004-10-22)
toolbox_fast_marching\data\chan-vese.jpg (6702, 2004-04-20)
toolbox_fast_marching\data\chicken.gif (555, 2006-07-06)
toolbox_fast_marching\data\giraffe.gif (595, 2006-07-06)
toolbox_fast_marching\data\image01.tiff (28540, 2006-03-09)
toolbox_fast_marching\data\mm.png (104858, 2006-11-20)
toolbox_fast_marching\data\mountain.png (3384, 2001-02-23)
toolbox_fast_marching\data\road2.png (350277, 2005-05-08)
toolbox_fast_marching\data\room.png (631, 2005-02-04)
toolbox_fast_marching\data\room1.png (456, 2004-11-08)
toolbox_fast_marching\data\stephanodiscusniagarae.jpg (32495, 2005-02-04)
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WARNING: you have to add the files under toolbox/ in your Matlab path. The Fast Marching algorithm, introduced by Sethian (1996) is a numerical algorithm that is able to catch the viscosity solution of the Eikonal equation |grad(D)|=P. The level set {x \ F(x)=t} can be seen as a front advancing with speed P(x). The resulting function D is a distance function, and if the speed P is constant, it can be seen as the distance function to a set of starting points. The Fast Marching is very similar to the Dijkstra algorithm that finds shortest paths on graphs. Using a gradient descent of the distance function D, one is able to extract a good approximation of the shortest path (geodesic) in various settings (euclidean for P constant, and a weighted riemanian manifold with P varying). The main reference about the Fast Marching algorithm is the book Level Set Methods and Fast Marching Methods Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science J.A. Sethian, Cambridge University Press, 1999 Cambridge Monograph on Applied and Computational Mathematics A good review of the Fast Marching in 3D together with some applications can be found in Fast extraction of minimal paths in 3D images and application to virtual endoscopy. T.Deschamps and L.D. Cohen. September 2000. To appear in Medical Image Analysis. Basic features: * The functions 'perform_fast_marching_2d' and 'perform_fast_marching_3d' compute the distance function from a set of starting points. * To extract the geodesics between these starting points and an ending point, you can use 'extract_path_2d' and 'extract_path_3d'. * There are also test applications 'test_fast_marching_2d' and 'test_fast_marching_3d' so that you can easily understand the use of the functions. Additional features: * Circular path extraction : a callback function can be passed to the C++ function 'perform_front_propagation_2d' to prevent the front to passing though some area. The function 'perform_circular_fast_marching_2d' implement a simple trick, by prenventing the front to pass through an horizontal half line, which in turn enable the extraction of circular paths. The function 'test_circular_fast_marching_2d' shows an application of this to cell segmentation. For more information you can read the original paper: Appleton, B., Talbot, H., Globally Optimal Geodesic Active Contours, Journal of Mathematical Imaging and Vision, to appear. * Constraint path planing : the script 'test_path_planing' shows the application of 3D fast marching to the extraction of optimal path for a tiny rectangle. The function 'generate_constrained_map' generate the 3D speed function for a given 2D map (the 2D speed function that should be used if the object to move was a point). For more information see: http://math.berkeley.edu/~sethian/Movies/Movierobotics.html and read Kimmel, R., and Sethian, J.A., Fast Marching Methods for Robotic Navigation with Constraints Center for Pure and Applied Mathematics Report, Univ. of California, Berkeley, May 1996 The main computation are done in a mex file so it is very fast (using a Fibonacci heap structure). To compile the mex files, simply run 'compile_mex'. This should be ok if you have set some C++ compiler (use 'mex -setup'). Precompiled version (.dll) for Windows are provided. Copyright (c) 2004 Gabriel Peyré

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