203 results about "Level set function" patented technology

Photomask patterns are represented using contours defined by level-set functions. Given target pattern, contours are optimized such that defined photomask, when used in photolithographic process, prints wafer pattern faithful to target pattern. Optimization utilizes “merit function” for encoding aspects of photolithographic process, preferences relating to resulting pattern (e.g. restriction to rectilinear patterns), robustness against process variations, as well as restrictions imposed relating to practical and economic manufacturability of photomasks.

A method for automatically segmenting a liver in digital medical images includes providing a 3-dimensional (3D) digital image I and a set of N training shapes {φi}i=1, . . . , N for a liver trained from a set of manually segmented images, selecting a seed point to initialize the segmentation, representing a level set function φα(θx+h) of a liver boundary Γ in the image asφα(x)=φ0+∑i=1nαiVi(x),whereφ0(x)=1N∑i=1Nφi(x)is a mean shape, {Vi(x)}i=1, . . . , n are eigenmodes where n<N, αi are shape parameters, and h ε R3 and θε [0,2π]3 are translation and rotation parameters that align the training shapes, minimizing a first energy functional to determine the shape, translation, and rotation parameters to determine a shape template for the liver segmentation, defining a second energy functional of the shape template and a registration mapping weighted by image intensity histogram functions inside and outside the boundary, and minimizing the second energy functional to determine the registration mapping, where the registration mapping recovers local deformations of the liver.

The utility model discloses a integrated method based on multi-level set evolution and high resolution remote sensing image partition, classification and change inspection, which is characterized in that (1) image preprocessing (radiation, registration and filtering); (2) the multi-level set evolutional partition and classification model, after registration, the GIS data determines the initial profile of each level set function and performs the partition and classification to the first phase image; (3) the model described in the (2) is still adopted, and the initial profile of each level set function is optimized, increment type partition and classification is adopted for the second to T phase; (4) the objective after partition is used as unit, the ith and (i+1)th two adjacent phase image classification results are compared to determine the change area; (5) return back to (3) until the partition, classification and change inspection of all T phase image are finished. The utility model has the advantages that: compared with the traditional pixel-oriented K value method, the classification and inspection precision are improved, The utility model is applicable for the change inspection of sequence remote sensing image and has wide application in hazard monitoring and land resource investigation.

The invention discloses a level set SAR (Synthetic Aperture Radar) image segmentation method by combining edges and a regional probability density difference, belonging to the technical field of image processing and mainly solving the problems of difficult segmentation of SAR images with fuzzy edges and inaccurate positioning to real edges of the SAR images, of the traditional level set method. The method comprises the following implementation steps of: firstly, detecting an edge intensity modulus absolute value of Rmax of an SAR image by applying an ROEW operator; secondly, initializing a level set function phi, segmenting the SAR image into an inner region omega1 and an outer region omega2, and solving for the intensity mean values c1 and c2 of the two regions; thirdly, solving for the estimated probability densities of the two regions omega1 and omega2 according to c1 and c2 and calculating the actual probability densities p1 and p2 of the two regions; and fourthly, constructing a total energy function ESAR, solving for a gradient downstream equation by applying a variational method, and updating the level set phi to obtain new segmentation regions omega1 and omega2. Indicated by experimental results, the segmentation method can be used for obtaining more ideal segmentation effect and be used for the edge detection and the target identification of the SAR images.

A method for automatically segmenting a liver in digital medical images includes providing a 3-dimensional (3D) digital image I and a set of N training shapes {φi}i=1, . . . , N for a liver trained from a set of manually segmented images, selecting a seed point to initialize the segmentation, representing a level set function φα(θx+h) of a liver boundary Γ in the image asϕα⁡(x)=ϕ0+∑i=1n⁢αi⁢Vi⁡(x),⁢where⁢ϕ0⁡(x)=1N⁢∑i=1N⁢ϕi⁡(x)is a mean shape, {Vi(x)}i=1, . . . , n are eigenmodes where n<N, αi are shape parameters, and h ε R3 and θε [0,2π]3 are translation and rotation parameters that align the training shapes, minimizing a first energy functional to determine the shape, translation, and rotation parameters to determine a shape template for the liver segmentation, defining a second energy functional of the shape template and a registration mapping weighted by image intensity histogram functions inside and outside the boundary, and minimizing the second energy functional to determine the registration mapping, where the registration mapping recovers local deformations of the liver.

A method for segregating a figure region from a background region in image sequences from dynamic visual scenes, comprising the steps of: (a) acquiring an image; (b) determining local motion estimations and confidences for each position of the image; (c) modifying a level-set function by moving and distorting the level-set function with the local motion estimations and smearing it based on the local motion confidences, to generate a predicted level set function that is geometrically in correspondence with the image and diffused at positions where the confidence of the motion estimation is low; (d) obtaining input features of the image by using a series of cues; (e) calculating a mask segregating the figure region from the background region of the image using the modified level-set function and the obtained input features; (f) extracting the figure region from the image; and repeating steps (a)-(f) until a termination criterion is satisfied.

The invention discloses a particle image velocimetry (PIV) processing method comprising the following steps: A. constructing a level set function phi in an N*M pixel range omega, wherein the level set function phi is a continuous function defined in the N*M pixel range omega; dividing the range omega into a first subregion omega1 and a second subregion omega2 so that a motion field, a divergence field and a rotation field are smoothly connected in the two subregions of omega1 and omega2, but are not continuous on the boundary, wherein a region where the phi is larger than or equal to 0 corresponds to the first subregion omega1, and a region where the phi is smaller than 0 corresponds to the second subregion omega2; B. constructing an energy function E=E1+lambda *E2 of the range omega:, wherein the E is a global energy functional, the E1 is data constraint energy, and the E2 is smooth constraint energy; and C. minimizing the global energy functional to realize the optimal approximation of the motion field, the divergence field and the rotation field. The invention provides the PIV processing method with high precision and good reliability.

The invention discloses a multi-scale structural material integrated design method based on shape features. The method is used for solving the technical problem that existing structural material integrated design methods are poor in practicability. According to the technical scheme, the shape features are used as basic elements of macro and micro design, a regular quadrangular finite element gridis adopted, and a structural shape is described by calculating a level set function of a feature structure; material properties of a microstructure are associated with macrostructure units through a homogenization method, and a multi-scale mechanical model based on a fixed grid is established; and shape position parameters of the shape features are selected to serve as design variables, and macroand micro topological optimization design is realized. Through the method, macrostructure design and microstructure design are associated, the level set method is adopted to describe the model, multiple microstructure forms can be obtained according to local conditions, an optimization result which has a smooth boundary and apparently contains feature information can be obtained, the method can beseamlessly integrated with existing computer-aided design software, and practicability is high.

The invention relates to a covariance matching-based active contour tracking method and belongs to the technical field of visual tracking. In the covariance matching-based active contour tracking method, an image area energy term is modeled by using non-Euclidean geometry. The method comprises the following steps of: manually initializing a curve surrounding an objective and establishing a covariance matrix as a template of an objective contour for an area surrounded by the curve in a first frame; after the contour of the objective is obtained, recording a level set function value of the template to make preparation for a prior shape and calculating a symbolized distance function of the template; from the image of the next frame, deducing a gradient descent flow from a result of the previous frame according to the established energy functional and updating the level set function; and checking whether iteration stops or not. In the method, the tracking result is more accurate; meanwhile, the covariance matrix is used as an area descriptor and all kinds of information in an image sequence and the correlation between all kinds of information are considered comprehensively, and the method does not depend on foreground and background information distribution, so that the tracking method has universality.

The invention discloses an SAR (storage address register) image changing detection method based on an improved C-V model, which mainly solves the problem that the precision of the detection result in the prior art is low. The implementation process of the method comprises the following steps of structuring a different map for two SAR images obtained in a same domain and in different time by a logarithmic ratio method; initializing a level set function as a symbol distance function, and dividing the different map area into two areas according to the negative and positive value of the level set function; respectively calculating gray average values of the two areas; structuring a total energy function including an energy function and a distance regular item based on the global regional information according to the gray level mean values of the twp areas and the level set function; and obtaining the minimum value of the total energy function through continuously updating the value of the level set function, and obtaining the change detection result map. According to the method provided by the invention, the level set function is used for showing a contour curve; as the value of the level set function is updated, the contour curve can be changed towards the boundary of the change area, thus, the global optimization and noise immunity can be realized; the precision of the detection result is improved, and the method can be used for disaster evaluation and environment detection.