Title of Invention  METHOD AND SYSTEM FOR DETERMINING A LATTICE OF POINTS SUITABLE FOR A SIMULATION 

Abstract  The present invention relates to a system and method for generating a lattice of points that respects features in a digital image. The method comprises a process for initializing the lattice, and a process for optimizing the configuration of that lattice with respect to the image features. The optimization process operates by adjusting points of the lattice to extremize a composite function of the spatial coordinates of the lattice points. The lattice points are interpreted as atoms. Each lattice point contributes a potential function to an atomic potential field. The image represents another potential field. The composite function is a weighted sum of the atomic and image potential fields evaluated at the lattice points. 
Full Text  CROSSREFERENCE TO RELATED APPLICATIONS Not applicable. BACKGROUND Field of the invention This invention relates to the analysis of digital images specifically to the construction of computational meshes from such images. Description of the prior art Digital images are often analyzed to obtain meshes for further computation. For example, seismic images are analyzed to obtain geologic meshes used to simulate oil flowing in subsurface reservoirs. Similarly, medical images of the human brain are analyzed to obtain meshes used to simulate blood flow in arteries. A somewhat different exaxnple is image morphing, in which meshes derived from one image may be used to encode efficiently differences in subsequent images. In all of these apperceptions, an image is analyzed to construct a mesh. Images and meshing today Specifically, such applications often consist of the following sequence of steps: (1) Process an image to enhance features of interest. (2) Find curves or surfaces in the image that bound regions of interest. (3) Fill the space defined by those regions with a computational mesh. (4) Simulate some process on the spacefilling mesh. For an application in medical imaging, see, Cebral, J.R., and Lohner, R., From Medical Images to CFD Meshes, Proceedings of the 8th International Meshing Roundtable, p. 321331, Sandia, 1999. For an application in seismic imaging, see, Garrett, S., Griesbaxih, S., Johnson, D., Jones, D., Lo, M., Orr, W., and Sword, C, Earth Model Synthesis, First Break, v. 15, no. 1, p. 1320, 1997. For various reasons, each step in this sequence is today often taken independently, with little regaid for the requirements of subsequent steps. For example, it is common in step (2) to produce a bounding curve or surface with more detail than can be represented in the spacefilling mesh used in step (4). Seismic reflections representing geologic interfaces axe routinely mapped with higher resolution than can practically be used in meshes for petroleum reservoir simulation. This leads to the "scale up" or "upscaling" problem cited by Garrett et al. (1997). This discrepancy in resolution is often accompanied by a discrepancy in data struc¬ture. For example, a twodimensional (2D) curve represented by a simple Unked list of line segments in step (2) may become a relatively complex mesh of triangles in step (3). Such discrepancies today disrupt the analysis sequence, and may yield inconsistencies between the image processed in step (1) and the mesh used in step (4) that are difiicult to quemtify. The disruptions are costly. In the example of seismic image analysis, one iteration of this sequence today may require months of work. This high cost makes it difficult to perform multiple iterations in an attempt to estimate uncertainties in analysis results. One reason for discrepancies in resolution and data structure lies in the implemen¬tation of the analysis sequence. Historically, the four steps listed above may have been performed by separate computer programs, developed independently, and used by differ¬ent specialists. Today, a single person or small group may perform all four steps in this sequence, and there exists demand for the steps to be more integrated . Another reason for discrepancies is a lack of methodology. The simulation step (4) often requires the numerical solution of partial differential equations (PDEs). In the past, these solutions were feasible for only structured meshes, in which mesh elements can be indexed as simple arrays. The conversion of an unstructured mesh produced in step (3) to a structured mesh yields additional inconsistencies between the processed image and the mesh used in simulation. However, recent advances in numerical methods for solving PDEs on unstructured meshes sviggest that this conversion and resultuig inconsistencies can be avoided. Meshing with lattices The accuracy of most computations performed on meshes depends on the regularity of mesh elements. Simulations performed on highly regular triangulaor meshes, those with nearly equilateral triangles, are more accurate than those performed on irregular meshes with long thin triangles. This is one reason that the Delaunay triangulation is popular. Given a set of points representing the locations of nodes for a 2D mesh, the Delaunay triangulation of those points, when compared with all other possible trianguiations, )aelds triangles most nearly equilateral. However, Delaunay triangulation alone does not guarantee a regular mesh. For that, one must choose carefully the locations of the mesh nodes. (For a survey of 2D and 3D Delaunay triangulation applied to the problem of mesh¬ing, see Bern, M., and Eppstein, D., Mesh Generation and Optimal Triangulation, in Computing m Euchdean Geometry, Du, D.Z. and Hwang., P.K. eds.. World Scientific, 1995.) Outside the domain of image analysis, the problem of choosing optimal mesh node locations has been well studied. One solution to this problem is based on the observa tion that the triangulation of a set of points defined by a simple crystal lattice yields a highly regular mesh. (See Mello, U. T., and Cavalcanti, P. R., A Point Creation Strategy for Mesh Generation Using Crystal Lattices as Templates, Proceedings of the 9th Inter national Meshing Rotmdtable, p. 253261, Sandia, 2000.) However, a uniform lattice of points can seldom be aligned exactly with the boundaries of objects to be meshed. Therefore, some solutions begin with an approximately uniform lattice, and then use numerical models of physical forces between atoms or bubbles to automatically move mesh nodes (atoms or bubbles) to more optimal locations. (See Shimada, K., Physically Based Mesh Generation: Automated Triangulation of Surfaces and Volumes via Bubble Packing, Ph. D. thesis, Massachusetts Institute of Technology, 1993, and Shimada, K., and Gossard, D. C, Bubble Mesh: Automated TriangulEU" Meshing of NonManifold Geometry by Sphere Packing, in Proceedings of the Third Symposium on Solid Modeling and Applications, p. 409419,1995. U. S. patent 6,124,857, to Itoh, T., Inoue, K., Yamada, A., and Shimada, K., Meshing Method and Apparatus, 2000, extends this work to quadrilateral meshing. See also Bossen, F. J., and Heckbert, P. S., A Pliant Method for Anisotropic Mesh Generation, Proceedings of the 5th International Meshing Roundtable, p. 6374, Sandia, 1996.) In all of these examples, latticebased meshing begins with a geometric model that defines precisely the internal and external boundaries of objects to be meshed. Thus, there exists a need for a meshing system and methodology capable of addressing problems where such boundaries may not be known apriori. Reference is made to US 5859645 and EP 0974936, which is cited during the examination process, these documents do not teach repeatedly adjusting location of the lattice of points on the said region to extremise total potential energy, wherein said total potential energy comprises a combination of a atomic potential energy and a image potential energy. U. S. patents cited in this specification 4,908,781, to Levinthal, C, and Fine, R., Computing Device for Calculating Energy and Pairwise Central Forces of Peuticle Interactions, 1990. 5,596,511, to Toyoda, S., Ikeda, H,Hashimoto, E., and Miyakawa, N., Computing method and Apparatus for a ManyBody Problem, 1997. 6,124,857, to Itoh, T., Inoue, K., Yamada, A., and Shimada, K., Meshing Method and Apparatus, 2000. other references cited in this specification Bentley, J. L., and Friedman, J. H., Data Structures for Range Searching, Computing Surveys, vol. 11, no. 4, 1979. Bern, M., and Eppstein, D., Mesh Generation and Optimal Tciangulation, in Computing in Euclidean Geometry, Du, D.Z. and Hwang., F.K. eds., World Scientific, 1995. Bossen, F.J., and Heckbert, P.S., A Pliant Method for Anisotropic Mesh Generation, Proceedings of the 5th International Meshing Roundtable, p. 6374, Sandia, 1996. Byrd, R. H., Nocedal, J., and Schnabel, R. B., Representations of QuasiNewton Matrices and Their Use in Limited Memory Methods, Technical Report NAM03, Northwestern University, Department of Electrical Engineering and Computer Science, 1996. Cebral, J.R., and Lohner, R., From Medical Images to CFD Meshes, Proceedings of the 8th International Meshing Roundtable, p. 321331, Sandia, 1999. Garrett, S., Griesbach, S., Johnson, D., Jones, D., Lo, M., Orr, W., and Sword, C, Earth Model Synthesis, First Break, v. 15, no. 1, p. 1320, 1997. Mello, U.T., and Cavalcanti, P.R., A Point Creation Strategy for Mesh Generation Using Crystal Lattices as Templates, Proceedings of the 9th International Meshing Roundtable, p. 253261, Sandia, 2000. Shimada, K., PhysicallyBased Mesh Generation: Automated IVianguIation of Surfaces and Volumes via Bubble Packing, Ph.D. thesis, Massachusetts Institute of Technology, 1993. Shimada, K., and Gossard, D.C., Bubble Mesh: Automated Triangular Meshing of NonManifold Geometry by Sphere Packing: Proceedings of the Third Symposium on SoUd Modeling and Applications, ACM Press, p. 409419, 1995. SUMMARY OF THE I2SrVENTION In accordance with various embodiments of this invention, a method for generating a lattice of points which respects features in a digital image comprises a process for initial¬izing a lattice, and a process for optimizing the alignment of that lattice with respect to the image features. The optimization process operates by adjusting points of the lattice to extremize a composite function of the spatial coordinates of the lattice points. The composite function may be a weighted combination of a first function of pairwise dis¬tances between the points, and a second function derived from the image (e.g., computed from sampled values of the image near the lattice points). A mesh may be generated from the optimized lattice. The mesh may be used in any of a varieiy of appUcations. Pbr exajnple, the mesh may be used to simiilate a process associated with the image. As another example, the mesh may be used to compress the image or subsequent images in an image sequence (e.g. a video stream). In some embodiments, the optimized lattice may be used to encode information as¬sociated with the image with or without the generation of an intermediate mesh. Any of various types of associated information are contemplated. Objects and advantages One object of at least some embodiments of this invention is a method that enables one to replace the image analysis sequence described above (in the description of the prior art) with the more efficient sequence: (1) Process an image to enhance features of interest. (2) Fill space vnth a computational mesh aligned with image features. (3) Simulate some process on the spacefilling mesh. Instead of finding boundaries of regions within images and then meshing those regions, one simply constructs a mesh that is aligned with the boundaries. Additional objects of some embodiments of this invention are the use of this method to construct: • highly regular meshes, suitable for further computations, such as the solution of partial differentitd equations; and • graded meshes, in which the density of mesh nodes vaxies with image complexity or a userspecified function; and • 3D meshes, where 3D images are available. These and other objects and advantages of various embodiments of the invention will become appaxent from consideration of the following description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1: the flow of data among the primary components in the method of this inven¬tion. Figure 2: the pairwise (a) force and (b) potential functions of normalized distance between any two atoms. Figure 3: the contributions, for nominal distances (a) 4 and (b) 8, of one atom to a sampled atomic potential field. Figure 4: a hexagonal 2D lattice of atoms that has been triangulated. Figure 5: a facecenteredcubic 3D lattice of atoms. Figure 6: a seismic image that has been processed to enhance faults, discontinuities in subsurface geology. Figure 7: a nominal distance fimction, which represents the desired variable spacing between atoms in the lattice. Figure 8: an initial pseudoregular distribution of atoms in a lattice; each atom is maiked by a circle with diameter proportional to the nominal distance function evaluated at the atom location. Figure 9: a mesh corresponding to a pseudoregular initial lattice for the seismic image. Pigiure 10: a mesh corresponding to a pseudorandom initial lattice for the seismic image. Figure 11: a mesh corresponding to the optimized lattice for the seismic image. Figure 12: edges in the mesh that are most aligned with features in the seismic image. Figure 13: a Voronoi mesh corresponding to an optimized lattice in which atoms are are repelled by features in the seismic image. Figure 14; a magnetic resonance image (MRI) of a human head. Figure 15: an image of a human head, processed to enhance edge features. Figure 16: a mesh aligned with features in the image of a human head. Figure 17: edges in the mesh that axe most aligned with features in the image of a human head. Figure 18: a magnetic resonance image (MRI) of a hmnan torso. Figure 19: an image of a human torso, processed to enhance edge features. Figure 20: a mesh aligned with features in the image of a human torso. Figure 21: edges in the mesh that are most aligned with features in the image of a human torso. Figure 22: components of a computational device used to implement the method of this invention. Figmre 23: an embodiment of the method of this invention. While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the drawings and detfiiled description thereto are not intended to limit the invention to the paxticular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A method for generating a lattice of points which respects features in a digital image is described herein. The method comprises initializing and then optimizing the lattice of points. In one set of embodiments, the points of the optimized lattice tend to be aligned on (coincident with) features in the image. In a second set of embodiments, the points of the optimized lattice tend to be aligned alongside, but not on, features in the image. In other embodiments, the optimized lattice points may tend to be aligned on some features and not aligned with other features in the same image. Hereafter, we refer to points in the lattice as atoms. We describe the process of lattice initialization in terms of the atomic structures of crystals. Likewise, we describe lattice optimization in terms of minimizing the potential energy of an atomic lattice. These physical models make our description easier to understand, but an atom in the context of this invention is merely a point. Figure 1 illustrates in more detail the flow of data among the primary components of this method. A data input component 100 produces an image that has been processed elsewhere to highlight features of interest. Here, the image is either obtained directly as a result of that processing or loaded from a computer memory. A lattice, initializer 200 creates an initial lattice of atoms that spans the image. A lattice optimizer 300 moves the atoms so that the lattice is aligned with features in the unage. The data output component 400 consumes the optimized lattice of atoms, either passing it to some other process or storing it in a computer memory. The lattice optimizer 300 comprises a generic function minimizer 310 and a potential energy computer 320. The generic minimizer 310 iteratively searches for a minimum of a function of many variables. During its search, the minimizer requires repeated evaluations of the function and its partial derivative with respect to each variable. Here, the variables are the spatial coordinates of atoms in a lattice. Given atom coordinates and an image, the potential energy computer 320 evaluates the lattice potential energy and its partial derivative with respect to each atom coordinate. Computing the potential energy An atom in a twodimensionaJ (2D) space has x and y coordinates, and an atom in a threedimensional (3D) space has x, y, and z coordinates. The vector x denotes the x and y (or a;, y and z) spatial coordinates of a point in 2D (or 3D) space. Given two atoms with locations x Pairwise potential functions For computational efficiency, we model the interaction among atoms with a simple pairwise force function, so that the total force exerted on axi atom by its neighbors is simply the svtm of the forces exerted by esich one of them. Even with this simplification, there exist many reasonable choices for the pairwise force function. To avoid having two or more atoms with the same, or nearly the same, coordinates, the force between them may be repulsive (positive) if they are too close to each other. Likewise, to prevent large empty spaces with no atoms, the force between two atoms may be attractive (negative) if they are too far away from each other. To facilitate numerical computations, the force may be bounded. To prevent every atom in the lattice from exerting a force on every other atom, the force may be zero beyond a cutoff distance. Furthermore, the force function may be continuous as a function of the interatomic distance. The force function proposed by Shimada (1993) has these properties. Thva, in one set of embodiments, we use Shimada"s force function, as described below. However, «uiy of a variety of force functions consistent with these properties may be employed. Let d denote the nominal distance between two atoms, the distance at which the force goes from being repulsive to being attractive. Then, the force / between two atoms located at Xi and xy may be given by the cubic polynomial: fu2fV, 0 0, i where u is the normalized distance between the two atoms defined by d The coefiicients of this polynomial function ensure that the force is bounded and contin¬uous, that it equals zero at zi = 1 and u>\, and that it is positive for 0 Generally, the force on an atom is a vector. Here, the direction of this vector is implied by the sign of /(u), and by the locations x^ and Xj of the two atoms. It is more convenient to work with a scalar potential than with the multiple compoaents of a vector force. Therefore, following a wellknown convention, we define the force to be the negative of the gradient of a scalar potential: ^{u) = 3 _J 256 8 "■^24" 16"" "^"^2" 0, f The constant of integration ^ has been chosen so that 4>{u) is continuous at it = . ""igure 2b illustrates this potential function. As expected, the potential function ^{u) las a minimum at normalized distance ti = 1, where the force function f{u) is zero. Potential energies and potential fields Given a potential function ^(u) of normalized distance u, we define the atomic potential energy to be the following sum of pairwise potentials: 1 " " ^ = A(Xi,X2,...,X„) ^T^l^lD^ Xi  X, d(x,) J" (1) where Xi, xa,...,Xn are the coordinates of n atoms in a lattice, and d(x) is the nominal interatomic distgjice function of position x. The nominal distance function d(x) need not be constant; but, to ensure a smoothly graded lattice, we require that it be smooth. Specifically, we require that Vd We may also define the atomic potential energy A in terms of an atomic potential field: JL. riir — "v.n (2) 3=1 so that L rf(xi) 1 " A = A{Xi ,X2,...,Xn)^J2 «(X "^ i=l In other words, the atomic potential energy is defined to be half the sum of values obtained by evaluating the atomic potential field at the atom coordinates. Likewise, we define aa image potential energy J5 = B(xi,X2,...,x„) = E6(xi), (3) where &(x) is an image potential field based on the input image. In many contexts, an image potential field is simply an image (or a smoothed version of an image), typically represented by a 2D (or 3D) array of numbers stored in a computer memory. Here, we use the term "potential field" to emphasize (and later exploit) the similarity between the atomic and image potential fields. In one set of embodiments, it is assumed that the image has been processed so that the image potential field attains a minimum value (e.g., 6(x) « — 1) within feattires of interest, and a maximum value (e.g., 6(x) w 0) in uninteresting regions. In this case, minimimizing the image potential energy B is equivalent to moving atoms into minima corresponding to features of interest in the image. In a second set of embodiments, it is assumed that the image has been processed so that the image potential field attains a maximum value (e.g., b{x) « 1) within features of interest, and a minimum value (e.g., b(x) « 0) in uninteresting regions. In this case, minimimizing the image potential energy B is equivalent to moving atoms away from the maxima corresponding to features of interest in the image. In a third set of embodiments, the image potential field may attain a maximum value (e.g. 6(x) PH 1) along a first subset of the image features, a minimum value (e.g. 6(x) w —1) along a second subset of the image features, and an intermediate value (e.g. 6(x) Pd 0) away from the image features. More generally, we move atoms to minimize the following weighted sirna of the atbmic and image potential energies: F = P(xa,X2,...,x„) = (l/3)yl + ;SjB, (4) which we call the total potential energy. The scale factor /? determines the relative contributions of A and B to the total potential energy P. When /3 = 0, atoms tend toward a perfectly regular lattice that is not aligned with the image. When /3 = 1, atoms are sensitive to only image sample values; i.e., atoms move only on the basis of their attraction to minima in the image and/or their repulsion from maxima in the image. Thus, atoms tend to congregate in the minima and vacate the maxima in the image, yielding a highly irregular lattice. T^ically, we choose /3 « , and obtain a lattice that is approximately regular and respects image features. In terms of the potential fields a(x) and 6(x), the total potential energy is P = f:i(l^)a(x,) + /36(x,). In terms of the total potential field, defined as p(x) = (l/3)a(x)+/3fc(x), (5) the total potential energy is P = lf:p{^i)+/3bix.i). (6) Like the nominal distance function d(x), the scale factor /3 in equations (5) and (6) may be a smoothly varying function of position x. (As for d(x), smoothness implies that derivatives of y0(x) are negUgible.) This generalization enables the balance between lattice regularity and sensitivity to (e.g., attraction to or repulsion from) image features to vary spatially. Sensitivity of the lattice to image features may be more important in one part of the image than in some other part. For simplicity here, we let /? denote a constant scale factor. The total potential energy P is a nonquadratic function of the atom coordinates Xi,X2,... ,x„, with many local minima. (For example, note that interchanging the co¬ordinates of any two atoms does not change P.) Therefore any search for a minimum. usually one close to the initial lattice coordinates, must be iterative. In aji efficient itera¬tive search, we must evaluate repeatedly partial derivatives of P with respect to the atom coordinates. Consider, for example, the change in P with respect to the ^coordinate of the i"th atom: In evaluating the partial derivative dA/dxi of the atomic potential energy, we recall that the term >[(xi — Xy/d(xj)] w (^[x^ — Xi/d(xi)] appears twice in the double sum of equation (1). Therefore, XiXj d(xj) J d{yij) xi  XJI ^in?i_ (8) 9B db, . Similar results may be obtained easily for paxtial derivatives with respect to the y (and, in 3D, the z) coordinate of each atom. Computation Computation of the total potential energy P requires the computation of its components A and B. To compute the image potential energy B according to equation (3), we must evaluate the image potentiad field 6(x) for each atom location x = x^. Images are typically sampled uniformly, and the simplest and most efiicient approximation to b{Xi) is the value of the image potential field 6(x) at the image sample nearest to the point Xj. More accurate approximations (interpolations) are possible, but we used this simple and fast nearestneighbor interpolation in all of the examples shown here. Equation (3) implies that the cost (the computational complexity) of computing B is 0{n), where n is the number of atoms. In contrast, the double sum in equation (1) imphes that the cost of the most straight¬forward method for computing A is O(n^). In practical appUcations, n is large enough that an 0{n^) cost for computing A would be much greater than the 0{n) cost of evalu¬ating B. To reduce the cost of computing A, we may exploit our design of the potential function (p{u), which is zero for normalized distances u greater than the cutoff distance 5. Only the atoms nearest to an atom located at position x = x^ contribute to the atomic potential field a{xi) at that position. This observation leads to the problem of determining which atom neighbors are within a distance §rf(xi) of each atom located at x = Xj. Solution of this problem is nontrivial, because atoms move repeatedly during optimization of the lattice. For example, if we construct lists of neighboring atoms, one list for each atoin, we must update these hsts (or at least check to see whether they require updating) whenever atoms move. For lattices with near constant density, the cost of constructing and up¬dating such lists using simple data structures is 0{mn), where m is the average number of neighbor atoms within the cutoff distance. For variabledensity lattices, more com¬plex data structures are required and the cost becomes 0(mn log n). (See, for example, Bentley, J. L., and Friedman, J. H., Data Structures for Range Searching, Computing Surveys, vol. 11, no. 4, 1979. See, also, U.S. patent 4,908,781, to Levinthal, C, and Fine, R., Computing Device for Calculating Energy and Pairwise Central Forces of Par¬ticle Interactions, 1990, and U.S. patent 5,596,511, to Toyoda, S., Ikeda, H, Hashimoto, E., and Miyakawa, N., Computing Method and Apparatus for a ManyBody Problem, 1997.) Our expression of the atomic potential energy A in terms of the atomic potential field a(x) suggests a simpler solution. We interpret equation (2) as a recipe for computing an atomic potential field a(x) that is sampled like the image potential field b(x). Specifically, we represent a(x) with a 2D or 3D array having the same dimensions as the array used to represent the image b{x). We first initialize a(x) to zero for all x sampled. Then, for each atom located at position x = Xy, we accumulate a sampled potential function ^[jx — Xj/d(xj)]. This accumulation is limited spatially to samples inside the circle (or sphere) of radius f d(xj) centered at position Xj, where the contribution of the sampled potential function is nonzero. Figures 3a and 3b Uiustrate two such potential functions, for nominal distances d = 4 and d = 8, respectively. Gray levels between black and white correspond to sampled func¬tion values between 0.05 and 0.05, respectively. The atomic potential field o(x) is simply the accumulation of many such functions. For computational efiiciency, these sampled potential functions may be precomputed and tabulated for different nominal distances d. Then, given d(x) for any location x, the appropriate values of the potential function may be determined efiiciently by table lookup (or table lookup and interpolation). Preferred embodiment Our analysis suggests two quite different algorithms for computing the total potential energy and its partial derivatives. The preferred embodiment of this invention uses equations (2) and (5) to compute the total potential field p(x), and then uses equation (6) to compute the total potential energy, and equation (10) to compute its partial derivatives. The following pseudocode listing describes this algorithm in detail. ALGORITHM 1: COMPUTE P, ^, ^, AND £ 102 103: 104 105 106 107: 108: 109 110 111 112 113 101: initialize total potential field p(x) = j96(x) for all atom locations Xj = xi,X3,... ,Xn { for all X such that x — Xj } } initialize total potential energy P = 0 for all atom locations Xi = xi,X2,...,Xn { accumulate P = P+ [p(x yi> ^i + 1)  P{^i, Vii^i 1)] } . Lines 101 through 106 compute the total potential field j?(x), sampled Uke the image potential field 6(x). Given this field, lines 107 through 113 compute the total potential energy P and its partial derivatives ^, ^, and ^. In line 109, the values of the total potential field ajid the image potential field at atom location Xj may be approximated as mentioned above, by selection of the corresponding field values at the nearest image sam¬ple position, or by any desired interpolation scheme. The partial derivatives in lines 110112 are computed from the total potential field using simple centeredfinitedifference approximations; however, alternative (e.g., higherorder) numerical approximations to derivatives may be tised instead. For definiteness, this listing assumes a 3D coordinate space. For a 2D space, one simply ignores the z coordinates and partial derivatives ^. Assuming that atom locations are consistent with the nominal distance function d(x), the computational cost of Algorithm 1 is 0(JV), where N is the number of samples in the image. Recall that we sample the total potential field like the image, and that each atom contributes a spatiallylimited potential function (Uke those in Figvires 3) to those samples in the total potential field that lie nearest to the atom. Therefore, the cost of accumulating the contributions firom all atoms is proportional to the nxmxber of samples N m the field. The cost of this algorithm is comparable to that of conventional image processing, and it requires data structures no more complex than the simple array that represents the image. Furthermore, the cost is the same for nonconstant nominal distance functions d(x) as for constant nominal distance d. Alternative embodiment An alternative embodiment of this invention does not compute the total potential field. Rather, it uses equations (1), (3), and (4) to compute the total potential energy, and equations (7), (8), and (9) to compute its partial derivatives. The following pseudocode listing describes this algorithm in detail. ALGORITHM 2: COMPUTE P, ^, £, AND ^ 201 202: 203 initialize total potential energy P = Q for all atom locations Xi = xi, X2,..., x„ { accumulate P — P + j3b{xi) 204: 205 206 207: 208: 209: 210: 211 212 213 214; } } initialize ^ = ^/3[b(xi + 1, yi, m)  Hxi  1, yt, m)] initialize ^ = ^/3[b(xi, yt + 1, Zi)  b(xi, yi  1, Zi)] mitialize ^ = ^P[b{xi, yi, Zi + 1) b(xi, yi, zt  1)] for all atom locations Xj such that \xi — x.j\ accumulate ^ = ^ + A(yi  yj) accumulate ^ = ^ + A{zi — Zj) For eafih atom, hne 203 accumulates the image potential energy B and line 208 ac¬cumulates the atomic potential energy A for each atom neighbor into the total potential energy P. Likewise, lines 204 through 206 and 210 through 212 accumulate the contribu¬tions of the partial derivatives of the image and atomic potential energies to the partial derivatives of the total potential energy. Line 207 impHea the use of an auxiliary data structure that enables atom neighbors nearest to an atom located at x = x^ to be determined quickly. This data structure must be reconstructed or in some way updated whenever atoms move. Atoms move repeatedly in any iterative searcli for atom coordinates that minimize the total potential energy. Therefore, the cost of maintaining this data structme may be significant. For the most eflBcient data structures, the cost is higher for nonconstant nominal distance functions d{x) than for constant nominal distance d. Lattice Initialization As noted above, the total potential energy P is a nonquadratic function of the atom coordinates, with many local minima. During lattice optimization, we move atoms iteratively in seeuch of a minimum. In practice, we neither seek nor find the global minimum. Rather, we find an optimized lattice of atoms that is close to an initial lattice. Therefore, it is desirable that the initial lattice • minimize (locally) the atomic potentiaJ energy, • be highly regular, and • be consistent with the nonainal distance function d(x). CoDstant nominal distance For constant nominal distance d, we can easily construct an initiaJ lattice with these properties. Figure 4 illustrates such a lattice for a 2D space. In this ideal lattice, the atoms may be connected to form equilateral triangles. The distance between any atom and its six nearest neighbors is simply the constant nominal distance d. At this distance, the force exerted by any one atom on another is exactly zero, and the atomic potential energy is locally minimized. Figure 5 illustrates a regular lattice for a 3D space. This is a facecenteredcubic (FCC) lattice, in which atoms are arranged in horizontal layers that look like the 2D lattice of Figure 4, but with eaxdi layer shifted slightly to fill in the holes of the layers above and below. For clarity in Figure 5, the atoms in different layers are rendered with spheres colored in different shades of gray. The distance between any atom and its twelve nearest neighbors equals the constant nominal distance d. In contrast to equilateral triangles, which can fill a 2D space entirely (as in Figure 4), equilateral tetrahedra cannot fill a 3D space. Nevertheless, atoms in an FCC lattice can be triangulated to obtain highly regular tetrahedra. Variable nominal diataJice The initial arrangement of atoms is more difficult for a nonconstant nominal distance function d(x). The first complication is that the function d(x) must be computed, if not otherwise specified. As an example of how one might compute this function, consider the image (i.e., the image potential field 6(x)) displayed in Figure 6. This image is a horizontal slice through a 3D seismic image (not shown) that was processed to highlight faults, discontinuities in subsurface geology. The dark linear features in this image represent traces of faults that intersect this horizontal slice. The faults are approximately vertical, almost orthogonal to this horizontal slice. Figure 7 shows the result of smoothing the seismic image to obtain an estimate of the nominal distance function d(x). The darkest regions in this figure correspond to a minimum distance of d = 6 samples, and the lightest regions correspond to a maximum distance of d = 18 samples. These minimum and maximum distances were specified explicitly, based on the level of detail we observed in the seismic image. Distances are smaller m. the middleleft portion of the image, and larger in the lowerright portion. The average distance is approximately 9.7 samples. In some applications, the nominal distance function d{x) may be specified explicitly or constructed interactively using a computer program for painting images. With regard to the present invention, the actual manner in which the function d!(x) is computed is not important. As stated above, we require only that this function be smooth; i.e., that Vd The second complication is that of arranging atoms in a lattice consistent with the nominal distance fimction d(x). Here, we describe two algorithms for this arrangement. Preferred embodiment The preferred embodiment of this invention uses an algorithm for generating pseudoregular lattices. The following pseudocode listing describes this algorithm in detail. ALGORITHM 3: INITIALIZE A PSEUDOREGULAR LATTICE 301 302 303 304: 305: 306 307: 308 initialize an imagelike array of boolean flags w(x) = false construct aji empty list of atoms construct an empty queue of atom sites append the location Xi of the center of the image to the queue while the gueue is not empty { get and remove the first site Xj from the gueue if Xi Ues within the coordinate bounds of the image { set sphere — spherical region centered at x, with diameter 7d(xi) 309 310 311 312 313 314 315 } if, for all samples inside the sphere, io(x) = false { for all samples inside the sphere, set tu(x) = true append £in atom with coordinates Xi to the list append ideal sites for atom neighbors to the end of the queue } } The array w(x) initialized in line 301 is a temporary work array, with dimensions equal to those of the image. Its sole purpose is to mark locations of atoms in the lattice as they are generated by the algorithm. (The test in line 309 ensures that locations so marked will not be marked again.) In the preferred embodiment of this invention, this array can be the same as that used to store the total potential field p(x) in Algorithm 1, so that no additional memory is required. Figvire 8 shows an example of the array io(x), computed for the nominal distance function shown in Figure 7. Each circular region in Figure 8 is centered at an atom location, and has a diameter proportional to the value of the nominal distance function d(x) at that atom location. The constant of proportionality is the factor 7 in line 308 of this algorithm. By choosing this factor to be less than one, we permit some atoms in the initial lattice to be closer together than the nominal distance function d(x) implies, knowing that other atoms will be further away. We determined experimentally that the factor 7 = 0.8 yields pseudoregular lattices consistent with smooth nominal distance functions d(x). The ideal sites in line 312 are the locations of atom neighbors in the regular lattices shown in Figures 4 and 5. (The distances to these atom neighbors axe not scaled by 7.) Therefore, for constant d, Algorithm 3 yields a regular lattice like one of these. For nonconstant rf(x), it yields a pseudoregular lattice. In ajay case, the processing of ideal sites placed in the queue causes the lattice to grow outward from the first site placed in the queue. Therefore, the first site acts as a seed from which the lattice is grown. Line 304 of Algorithm 3 chooses that first site to be the center of the image. Alternative seed locations may be used. For example, if a mesh for simulation of fluid flow is desired, then the locations of one or more fluid sources may be used to seed the growth of a lattice. As another example, a centerofmass of features in the image may be selected as the seed location. The mesh shown in Figure 9 is the Delaunay triangulation of a pseudoregular initial lattice created using Algorithm 3 and the nominal distance function shown in Figinre 7. While most of the triangles in this mesh of the initial lattice axe highly regular, the complete Delaunay triangulation shown here yields some long thin triangles near the convex hull of the lattice points. Such irregular triangles near the mesh boundaries may simply be ignored in subsequent computations. Alternative embodiment An alternative embodiment of this invention uses an entirely different algorithm for gen¬erating pseudorandom initial lattices. The following pseudocode Usting describes this algorithm in detail. ALGORITHM 4: INITIALIZE A PSEUDORANDOM LATTICE 401 402 403 404 405 406 407: 408 construct an empty list of atoras for all X sampled by the image { set d = d(x) if 2D, set p = 2/\/3d2 if 3D, set p = 2/V2d^ generate a pseudorandom number r, uniformly distributed in [0:1) if r } Line 404 (for 2D images) or 405 (for 3D images) of this algorithm computes the nominal lattice density p, corresponding to a value of the nominal distance function d(x). In a 2D space, lattice density is inversely proportional to the distance squared between atoms; in a 3D space, it is inversely proportional to distance cubed. The constants of proportionality 2/v^ (line 404) and 2/V^ (line 405) correspond to the ideal lattices shown in Figures 4 and 5, respectively. Lattice density is the probability that an atom exists at any location sampled by the image. Lines 406 and 407 use a pseudorandom number generator to add an atom to the lattice with that probability. This algorithm uses commonplace computer software for generating a pseudoraaidom number uniformly distributed in the interval [0 : 1). This pseudorandom algorithm is simpler and faster than the pseudoregular Algo¬rithm 3. It also generates initial lattices that are completely isotropic, with none of the preferred directions or planes of symmetry exhibited by pseudoregular lattices. Depend¬ing on the appUcation, this may or may not be an advantage. Unfortunately, a pseudorandom initial lattice is highly irregular. Atoms in such a lattice exhibit no geometric pattern, which makes the otherwise useful term "pseudo¬random lattice" an oxymoron. Figure 10 shows a mesh corresponding to a pseudorandom initial lattice generated for the seismic image, using Algorithm 4. Although this mesh is statistically consistent with the nominal distance function shown in Figure 7, it is highly irregular, with many triangles that aie far from equilateral. Lattice optimization will make this pseudorandom initial lattice more regular, but will require more work (more iterations) than optimization of the pseudoregular initial lattice shown in Figure 9. Lattice optimization The lattice optimizer moves atoms in an initial lattice to obtain an optimized lattice. In one set of embodiments, the optimized lattice is both regular and more aligned with image features than the initial lattice. In other words, atoms in the optimized lattice far away from image features tend towards a pseudoregular structure that respects the nominal distance function, while atoms near image features tend to be coincident with those features. In another set of embodiments, the optimized lattice is both regular and more rarefied along the image features than the initial lattice. The lattice optimizer uses commonplace computer software for minimizing an arbitrary function of many variables. The lattice optimizer applies this softwareto minimize the lattice total potential energy, which is a function of atom coordinates. Various choices for the generic function minimizer axe possible. In the preferred embodiment, we use the limitedmemory BroydenFletcherGoldfarbShanno (LBFGS) minimizer. (See, for example, Byrd, R. H., Nocedal, J., and Schnabel, R. B., Represen¬tations of QuasiNewton Matrices and Their Use in Limited Memory Methods, Technical Report NAM03, Northwestern University, Department of Electrical Engineering and Computer Science, 1996. Note, particiilarly, the simple twoloop recursion on page 17.) Like other minimizers, the LBFGS method iteratively evaluates a function and its partial derivatives, in its search for a minimvmi. The LBFGS minimizer requires more conjiputer memory but fewer function evaluar tions than the method of conjugate gradients, another wellknown method. However, the additional memory required is insignificant when compared with the memory required to represent an image. Furthermore, the cost of each function evaluation (computing the lattice total potential energy) is significantly more costly than the other computations performed by the minimizer. Therefore, the LBFGS mioimizer is wellsuited to this invention. Preferred embodiment The preferred embodiment of this invention uses the following algorithm for lattice opti¬mization. ALGORITHM 5: OPTIMIZE THE LATTICE 501 502 503 504; 505 506, 507 508; 509 510; 511 get the initial lattice atom coordinates xi, xa,...,x„ construct a potential energy computer construct a minimizer compute the initial lattice total potential energy P do{ set Po = P randomly perturb xi,X2,■■ > ,Xn do{ set Pi = P let minimizer decrease P by adjusting xi,X2,... ,x„ } while PiP> e\Pi 512: } while PoP> e\PoThis algorithm begins at Une 501 with an initial lattice of atoms, such as the pseudoregular lattice produced by Algorithm 3 or the pseudorandom lattice generated by Al¬gorithm 4. It then (Une 502) constructs a potential energy computer, responsible for computing the totaJ potential energy P and its partial derivatives. It then (line 503) constructs a minimizer, which will use the potential energy computer to minimize P. (Construction of the potential energy computer and the minimizer includes allocation of memory and initialization of a number of variables and tables.) It then (line 504) computes the total potential energy P of the initial lattice. The remainder of the algorithm comprises two nested loops. The inner loop begin¬ning at line 508 lets the minimizer adjust the atom coordinates xi, X2,..., x„ to decrease the total potential energy P. This loop continues \mtil the decrease in P becomes in¬significant, as determined by the small threshold e in line 511. A typical threshold is € = 0.001. Recall that the total potential energy is a function with many local minima. The inner loop begins with the current atom coordinates, and tends toward the minimum nearest to those coordinates. We have observed that this local minimum may have a total potential energy larger than that of another minimum nearby. The outer loop beginning at Une 505 enables the algorithm to move from one local minimum to another one, imtU the decrease in total potential energy P is insignificant. The random perturbations of atom coordinates in line 507 are small, typically no more than 10% of the nominal distance d(xt), for each atom location x^. We use a commonplace pseudorandom number generator to compute these perturbations. Subsequent iterations of the inner minimization loop typically yield a significant decrease in total potential energy P. The inner and outer loops in Algorithm 5 use the same test for convergence. Both loops terminate when the decrease in total potential energy P becomes insignificant. Alternative convergence criteria are commonplace in numerical optimization, and our choices here are not important. For example, one might terminate these loops when the maximum change in atom coordinates is less than some threshold. Figure 11 shows the result of optimizing the initial pseudoregular lattice for the seismic image, where the image has been processed to have values near minus one along image featmres and zero away from image featvures. Recall equation (4), which states that the total potential energy P is a weighted sum of atomic and image potential energies. In this example, we used an image weight /3 = 0.3. The resulting optimized lattice is both highly regular (again, ignoring triangles near the convex hull of the lattice points) and wellaligned with image features. For images with small or narrow features, it may be useful to perform the first few iterations of Algorithm 5 using a slightly smoothed version of the image. These first iterations enable atoms to move towards features that might otherwise be missed, because they are too far away from initial lattice locations. Atoms are attracted initially to the smoothed features, and then, in subsequent iterations, to highresolution features in the original unsmoothed image. Curve and surface extraction In a spacefilling mesh, curves (in 2D) or surfaces (in 3D) appeal imphcitly. For ex¬ample, in the 2D mesh shown in Figure 11, any contiguous sequence of linear edges of triangles represents a curve. In a 3D tetrahedral mesh, any union of contiguous triangu¬lar faces of tetrahedra represents a surface. Of course, most such curves or surfaces are not interesting, because they are not aligned with image features. Figtire 12 highlights those edges of triangles that appear to be most aligned with faults in the seisnxic image. The edges shown here were chosen simply because all image sample values beneath them were below a specified threshold (0.2). No attempt was made to force connections among individual edges. Nevertheless, many edges are connected and do form continuous curves. A spacefilling mesh facilitates more elaborate curve or surface extraction algorithms than the simple thresholding used in this example. As one consequence of this invention, we anticipate the development of new extraction algorithms. In contrast to extraction (or segmentation) algorithms commonly used today, these new algorithms will produce curves or surfaces guaranteed to be consistent with a spacefilling mesh. Alternative lattices and meshes In the preceding examples, the image potential field was defined so that atoms are at¬tracted to image features. In some situations, it is desirable to create a pseudoregular lattice in which atoms are repelled from image features. Such a lattice may be obtained using the optimization Algorithm 5 with the same atomic potential function but with an image potential field that attains a maximum value (e.g. one) along image features and a mininum value (e.g. zero) away from image features. Figure 13 illustrates the result of such an optimization for the seismic image. Atoms in this optimized lattice tend to line up alongside (not on) image features. Figure 13 also illustrates a Voronoi mesh generated from the optimized lattice. The Voronoi mesh is the dual of the Delaunay triangulation. Thias, atoms lie within mesh elements rather than at the vertices of mesh elements. Because atoms in the optimized lattice tend to line up alongside image features, the boundaries of Voronoi mesh elements tend to line up on those features. Voronoi meshes have numerous applications, including the simulation of fluid flow. Often, Voronoi meshes lead to finitedifference solutions to partial differential equations, whereas simplicial triangular/tetrahedral meshes lead to finiteelement solutions. Both t3T)es of solutions are widely used today. In both types, it is desirable to have mesh element boundaries align with image features, because those features often correspond to discontinuities in physical properties. More generally, it may be desirable to create a pseudoregular lattice of atoms that are attracted to certain features in aji image, and repelled from other features. For example, it may be advantageous to place communication transceivers in a regulax pattern whose local density corresponds to human population density. It may also be desirable for those same transceivers to be located in areas of high elevation (e.g., tops of mountain ridges) and to avoid areas of low elevation (e.g., canyons or river valleys). Thus, an image potential field may include maxima and minima, as well as intermediate plateaus. Additional examples The method of this invention may also be appUed to medical images. Figure 14 shows a magnetic resonance image (MRI) of a human head. This digital image is freely available from the National Library of Medicine, as part of the Visual Human Project. Figure 15 shows the image after processing to enhance edges, discontinuities in the original MRI. A simple and wellknown Prewitt edge enhancement algorithm was used. This edgeenhanced image may be used as the image potential field 6(x) in the method of this invention. Figure 16 shows a mesh optimized for alignment with the image of Figure 15. As for the seismic image, a nonconstant nominal distance function d(x) was computed by smoothing the image. Nominal distance values range from a minimmn of d = 4 to a maximum of d = 12. The image scale factor is /3 = 0.4. Figure 17 highlights line segments in the mesh that are most aligned with edges in the image. The segments shown were selected using the same simple thresholding algorithm that was used for the seismic image. Figures 18, 19, 20, and 21 provide another example from medical imaging. Like the image of the head, this image of a human torso is freely available from the National Library of Medicine. Image processing and lattice optimization for this image is identi¬cal to that for the head image, except that nominal distance values here range from a minimum of d = 5 to a maximum of d = 15. Computer systems, memiory media and a method embodiment It is noted that the method of the present invention for generating pseudoregular lattices which respect features in a digital image may be implemented on any of a variety of com¬puter systems such as desktop computers, minicomputers, workstations, multiprocessor systems, parallel processors of v^ious kinds, distributed computing networks, etc. The method of the present invention may be realized in one or more software programs or modules which are stored onto any of a variety of memory media such as CDROM, mag¬netic disk, bubble memory, semiconductor memory (e.g. any of various tjrpes of RAM or ROM). Furthermore, these one or more software programs and/or the results they generate may be transmitted over any of a variety of carrier media such as optical fiber, metallic wire, free space £uid/or through any of a variety of networks such as the Internet and/or the PSTN (public switched telephone network). Figure 22 illustrates one embodiment 500 of a computer system operable to perform the lattice generation method of the present invention. Computer system 500 may include a processor 502, memory (e.g. random access memory 506 and/or nonvolatile memory devices 504), one or more input devices 508, one or more display devices 510, and one or more interface devices 512. These component subsystems may be interconnected accord¬ing to any of a variety of configurations. Nonvolatile memory devices 504 may include devices such as tape drives, disk drives, semiconductor ROM or EEPROM, etc. Input devices 508 may include devices such as a keyboard, mouse, digitizing pad, trackball, touchsensitive pad axid/or light pen. Display devices 510 may include devices such as monitors, projectors, headmounted displays, etc. Interface devices 512 may be config¬ured to acquire digital image data firom one or more acquisition devices and/or from one or more remote computers or storage devices through a network. Any of a variety of acquisition devices may be used depending on the type of object or process being imaged. The one or more acquisition devices may sense any of various forms of mechanical energy (e.g. acoustic energy, displacement and/or stress/strain) and/or electromagnetic energy (e.g. light energy, radio wave energy, current and/or voltage). Processor 502 may be configured to read program instructions and/or data from RAM 506 and/or nonvolatile memory devices 504, and to store computational results into RAM 506 and/or nonvolatile memory devices 504. The program instructions direct processor 502 to operate on an input image based on any combination of the method embodiments described herein. The input image may be provided to computer system 500 through any of a variety of mechanisms. For example, the input image may be acquired into nonvolatile memory 504 and/or RAM 506 using the one or more interface devices 512. As another example, the ixiput image may be supplied to computer system 500 through a memory medium such as disk or tape which is loaded into/onto one of the nonvolatile memory devices 504. In this case, the input image will have been previously recorded onto the memory medium by computer system 500 or by some other computer system. It is noted that the input image may not necessarily be raw sensor data obtained by an acquisition device. For example, the input image may be the result of one or more preprocessing operations on a set of raw sensor data. The one or more preprocessing operations may be performed by computer system 500 and/or one or more other com¬puters. Furthermore, the input image may be completely independent of sensor data as in the case of an image generated by a designer using a CAD package. Figure 23 illustrates one embodiment 600 of a method for generating a lattice of points that is pseudoregular and that respects features in a digital image. In step 605, an image (e.g. an image of an object and/or process) may be ax;quired into a locally accessible memory such as nonvolatile memory 504 and/or RAM 506 of computer system 500. In step 610, the image may be preprocessed to enhance or reveal features of interest in the image. If the features of interest axe sufficiently clear in the image as acquired in step 605, step 610 may be omitted. (As mentioned above, the image as acquired in step 605 may have already been subjected to preprocessing operations.) The present invention contemplates the use of any desired processing technique or combination of techniques for the preprocessing of step 610. For example, the image may be subjected to edge detection, convolution filtering, Fourier transformation, non¬linear filtering, thresholding, etc. The. preprocessing may generate the image potential field sampled Uke the digital image. Furthermore, the preprocessing may operate on the image to generate the nominal distajice function d(x). The preprocessing may be automated or may be responsive to user inputs. For example, a user may highlight features and/or regions of interest in the image. In step 615, a lattice of points consistent with the nominal distance function d(x) may be intialized in the space spanned by the image. The lattice may be initialized by the methods of Algorithm 3 or Algorithm 4. In one alternative embodiment, the initial lattice consistent with the nominal distance fvinction may be generated from a cheap lattice such as a rectangular lattice or equilateral lattice by executing a number of iterations of the lattice optimization with scaling parameter j3 equal to zero. However, to avoid tearing of the lattice, the distance between atoms in the cheap lattice may be set equal to the minimum of the nominal distance function. In another alternative embodiment, the initial lattice may be generated from a cheap lattice by executing a number of iterations of the lattice optimization with a nonzero value for the scaUng parameter /3, and with the distance function d{x) replacing the image potential field b(x). In step 620, the initial lattice of points may be optimized by Algorithm 5. The optimized lattice of points may be used in any of a variety of applications. For example, the lattice may be triangulated to generate a spacefilling mesh, and the mesh may be used to execute a meshbased simulation (such as a reservoir simulation), image coding algorithm, signal analysis method, etc. In some embodiments, the optimized lattice may be used by an appUcation without the intervening step of generating a mesh. Conclusion, ramifications, and scope The examples provided above demonstrate the utility of the method of this invention in generating a lattice of points that is both pseudoregular and responsive to features in a digital image. The optimized lattice may have variable density, conforming to a spaciallyvarying level of detail in the image. The examples have also shown that triangulation of such a lattice may yield a space¬filling mesh that is well aligned with image features. The highly regular elements in this mesh maJce it a suitable framework for subsequent computations, such as the simulation of fluid flow. Our combination of a lattice framework and an image may be used as the basis for improved algorithms for feature extraction. Although the examples shown above exhibit 2D images, the method of this invention applies equally well to 3D images. Where necessary, differences between 2D and 3D equations and algorithms have been highlighted in their descriptions. The method of the present invention naturally generalizes to Ndimensional images, where N is any integer greater than zero. The images in the examples were vmiformly sampled on a rectangular 2D grid. How¬ever, various embodiments are contemplated where the method operates with alternate image topologies. For example, a 2D image may be sampled on the surface of a sphere. Thus, in one embodiment, the method of the present invention may generate a pseudoregular lattice of points on such a spherical surface, consistent with a distance function defined on that surface, and then aligned with image features displayed on that surface. A variety of topologies in various dimensions are contemplated. The method of this invention has been presented in terms of a minimization problem. It is an obvious mathematical fact that the minimization of a function / is equivalent to maximizing its negative —/. Thus, alternative embodiments are contemplated where the lattice optimization operates by maximizing a composite function comprising a combina¬tion of a first function of interatomic distances between points (i.e. atoms) in a lattice and a second function of positions of the lattice points. Although the description above contains many specific details, these should not be construed as limiting the scope of the invention. Rather, they merely provide illustrations of currently preferred embodiments of this invention. For example, the simple polyno¬mial pairwise potential function could be replaced with another function having similar properties. Likewise, a variety of algorithms may be used to initialize a lattice of atoms, in addition to the two algorithms presented above. Thus the scope of this invention should be determined by the appended claims and their legal equivalents, rather than by the examples of embodiments provided above. WE CLAIM: 1. A method for determining a lattice of points suitable for a simulation, the method comprising: operating on an image to generate an image potential field, wherein said image potential field attains an external potential value along features of interest in said image; initializing the lattice of points in a region of space corresponding to said image; and repeatedly adjusting locations of said lattice of points in said region to extremize a total potential energy, wherein said total potential energy comprises a combination of an atomic potential energy and an image potential energy, wherein the atomic potential energy comprises a sum of atomic potentials for pairs of said lattice points, wherein the image potential energy comprises a sum of image potentials, and wherein each of said image potentials corresponds to an evaluation of the image potential field at one of the lattice points. 2. The method as claimed in claim 1, wherein said operating on the first image to generate the image potential field comprises detecting edges in said first image, and wherein said features of interest comprise edges in said first image. 3. The method as claimed in any of the preceding claims, wherein said operating on said image to generate the image potential field comprises: identifying features of interest in the images; assigning a first constant value to the image potential field at first spatial locations corresponding to at least a subset of said features of interest; and assigning a second constant value to the image potential field at second spatial locations not corresponding to said features of interest. 4. The method as claimed in 3, wherein said first constant value is a maximum value of said image potential field. 5. The method as claimed in claim 3, wherein said first constant value is a minimum value of said image potential field. 6. The method as claimed in claim 3, wherein said operating on said image to generate the image potential field comprises smoothing a first version of the image potential field, generated by said assigning the first constant value and the second constant value, to obtain a second version of the image potential field. 7. The method as claimed in claim 6, wherein said second version of the image potential field is used to compute the image potentials for a first set of repetitions of said repeatedly adjusting locations, and wherein said first version of the image potential fiinction is used to compute the atomic image potentials for a second set of repetitions of said repeatedly adjusting locations. 8. The method as claimed in any of the preceding claims, comprising: randomly displacing the locations of said lattice of points in said space; and performing multiple iterations of said randomly displacing and said repeatedly adjusting to obtain a succession of local extrema of the total potential energy. 9. The method of any of the preceding claims, wherein said initializing employs a distance function that specifies a preferred distance from each point in said lattice to other points nearby said point, and wherein said preferred distance is a function of location in said region. 10. The method as claimed in claim 9, wherein said initializing the lattice of points in the region of space comprises: reading a first tentative lattice point from a queue; examining a first neighborhood around said first tentative lattice point to determine if the first neighborhood contains no points of said lattice; and if the first neighborhood contains no points of said lattice, executing an update operation comprising: adding the first tentative lattice point to the lattice, and appending new tentative lattice points to the queue, wherein said new tentative lattice points are assigned coordinate positions corresponding to an exactly regular lattice with respect to the first tentative lattice point and the distance function. 11. The method as claimed in claim 10 comprising: storing one or more seed points on the queue; and repeatedly performing said reading, examining and conditionally executing the update operation until the subset of said space is filled. 12. The method as claimed in claim 10, wherein the first neighborhood around said first tentative lattice point is an Ndimensional ball with diameter proportional to the distance function evaluated at the first tentative lattice point, wherein N is the dimension of said space corresponding to said image. 13. The method as claimed in any of the preceding claims, wherein said initializing the lattice of points in the region of space comprises: computing a local probability value at a candidate position in the region based on a distance fimction; generating a random number value; determining if the random number value satisfies an inequality condition with respect to the local probability value; and conditionally adding the candidate position to the lattice of points if the random number value satisfies the inequality condition with respect to the local probability value. 14. The method as claimed in claim 13, wherein said initializing the lattice of points comprises: moving the candidate position through a set of image positions sampled by said first image; and performing said computing, generating, determining and conditionally adding for each of said image positions. 15. The method as claimed in any of the preceding claims, wherein the combination is of the form (1 a)A + aB, wherein A is the atomic potential energy, B is the image potential energy, and wherein parameter a takes any value in the rage from zero to one inclusive. 16. The method as claimed in any of the preceding claims comprising triangulating the lattice of points to generate a mesh from the lattice of points, wherein said mesh comprises a union of mesh elements, and wherein the vertices of the mesh elements are the lattice points. 17. The method as claimed in claim 16, wherein said triangulating the lattice of points comprises performing a Delaunay triangulation of the lattice of points. 18. The method as claimed in claim 16, comprising identifying a union of boundaries of said mesh elements which approximates one or more of said features of interest. 19. The method as claimed in claim 16 performing a simulation on the mesh, wherein the simulation generates output representing a behavior of a physical system. 20. The method as claimed in claim 19, wherein the physical system comprises an underground reservoir comprising one or more fluids. 21. A system capable of performing the method as claimed in any one of the preceding claims. 

1037chenp2003 abstractduplicate.pdf
1037chenp2003 assignment.pdf
1037chenp2003 claimsduplicate.pdf
1037chenp2003 correspondeencesothers.pdf
1037chenp2003 correspondeencespo.pdf
1037chenp2003 description (complete)duplicate.pdf
1037chenp2003 description (complete).pdf
1037chenp2003 drawingsduplicate.pdf
Patent Number  214301  

Indian Patent Application Number  1037/CHENP/2003  
PG Journal Number  13/2008  
Publication Date  31Mar2008  
Grant Date  07Feb2008  
Date of Filing  02Jul2003  
Name of Patentee  LANDMARK GRAPHICS CORPORATION  
Applicant Address  2101 City West Boulevard, Building 1 Suite 200, Houston, Texas 77042,  
Inventors:


PCT International Classification Number  G06T 17/20  
PCT International Application Number  PCT/US2001/048239  
PCT International Filing date  20011210  
PCT Conventions:
