Abstract:
System and method for estimating a rotational shift between a first discrete curve and a second discrete curve, where the second discrete curve is a rotationally shifted version of the first discrete curve. First and second discrete curves are received. A rotational shift between the first discrete curve and the second discrete curve is estimated based on the first discrete curve and the second discrete curve. A cumulative rotational shift is updated based on the estimated rotational shift. A rotationally shifted version of the second discrete curve is generated based on the cumulative rotational shift. The estimating, updating, and generating are performed in an iterative manner using the respective rotationally shifted discrete curve for each iteration until a stopping condition occurs, thereby determining a final estimate of the rotational shift between the first discrete curve and the second discrete curve. The final estimate may be used to perform curve matching.
Abstract:
System and method for programmatically generating a second graphical program associated with a second programming development environment based on a first graphical program associated with a first programming development environment. The second graphical program may be generated programmatically, without relying on user input, or may prompt for user input to determine various options to use in generating the second graphical program. The second graphical program may implement the functionality of, or a portion of the functionality of, the first graphical program. The method preferably generates the second graphical program such that the second programming development environment is operable to treat the second graphical program identically to a graphical program interactively developed by a user using the second programming development environment. Thus, once the second graphical program has been generated, the user may use the second programming development environment to edit the second graphical program, execute the second graphical program, etc.
Abstract:
A system and method for programmatically generating a graphical program or a portion of a graphical program in response to receiving program information is disclosed. During execution of a graphical program generation (GPG) program, the GPG program receives program information specifying functionality of the graphical program to be generated. In one embodiment the program information does not specify specific nodes in the graphical program or connections among the nodes. In response to the program information, the GPG program programmatically generates a graphical program (or graphical program portion) that implements the specified functionality.
Abstract:
System and method for determining the presence of an object of interest from a template image in an acquired target image, despite of or using various types of affine transformations of the object of interest in the target image. A template image discrete curve is determined from the template image corresponding to the object of interest, and a template curve canonical transform calculated based on the curve. The canonical transform is applied to the template curve to generate a mapped template curve. The target image is received, a target image discrete curve determined, and a target curve canonical transform computed based on the target curve canonical transform. The target canonical transform is applied to the target curve to generate a mapped target curve. Geometric pattern matching is performed using the mapped template and target image discrete curves to generate pattern matching results, and the pattern matching results are output.
Abstract:
System and method for determining the presence of an object of interest in a target image. Regions of a target image may be located that match an object of interest, e.g., in a template image, with respect to various information, e.g., luminance, color and/or other types of boundary information. The invention includes improved methods for mapping point sequences (e.g., pixel sequences) or curves to new point sets or curves for curve matching. The method determines the presence of an object of interest in a target image despite of or using various types of topological transformations of the object of interest in the target image. One or more mapping operators are determined based on template curves and/or example target curves. Pattern matching is performed on one or more target images using the mapping operator(s) to generate pattern matching results, and the pattern matching results output.
Abstract:
System and method for re-sampling discrete curves, thereby efficiently characterizing point sets or curves in a space. The method may also provide improved means for mapping point sets or curves to new point sets or curves for curve matching. A weight vector or function is determined based on a plurality of discrete curves, e.g., from one or more template data sets or images. The weight function enhances differences between weighted discrete curves. A set of orthonormal polynomials is determined based on the computed weight function, where the set of orthonormal polynomials comprises a set of orthogonal eigenfunctions of a Sturm-Liouville differential equation. Values for a plurality of zeros for one of the set of orthonormal polynomials is determined that comprise resampling points for the plurality of discrete curves. Each of the plurality of discrete curves is resampled based on the determined values of the plurality of zeros.
Abstract:
A system and method for generating a curve in a region, e.g., a Low Discrepancy Curve. The method may generate an unbounded Low Discrepancy Point (LDP); apply one or more boundary conditions to the unbounded LDP to generate a bounded LDP located within the region; repeat said generating and said applying one or more boundary conditions one or more times, generating a Low Discrepancy Sequence (LDS) in the region; store the LDS; and generate output comprising the LDS, wherein the LDS defines the curve in the region. The method may scan the region according to the defined curve. In generating the unbounded LDP, the method may select two or more irrational numbers, a step size epsilon (ε), and a starting position; initialize a current position to the starting position; and increment components of the current position based on ε and the irrational numbers to generate the unbounded LDP.
Abstract:
A system and method for programmatically generating and modifying graphical programs, in response to receiving program information. The program information may specify functionality of the graphical program or graphical program portion. During execution of a graphical program generation (GPG) program, the GPG program may be operable to receive the program information. In response to the program information, the GPG program may programmatically generate a graphical program (or graphical program portion) that implements the specified functionality. Thus, the GPG program may generate different graphical programs, depending on the program information received. The GPG program may have any of various purposes or applications. In some embodiments, the GPG program may be a program or application which a user utilizes to construct or characterize a computational process. In response to the specified computational process, the GPG program may programmatically generate a graphical program to implement the computational process. In other embodiments, the GPG program may be a program or application that directly aids the user in creating a graphical program. In addition to these examples, a GPG program may receive any other type of information and programmatically generate a graphical program based on the received information. After programmatically generating the graphical program, the GPG program may receive subsequent program information specifying a modification to the graphical program and may programmatically modify the graphical program based on the program information.
Abstract:
A system and method for generating a curve, such as a Low Discrepancy Curve, on a surface, such as an abstract surface with a Riemannian metric. The system may comprise a computer which includes a CPU and a memory medium which is operable to store one or more programs executable by the CPU to perform the method. The method may: 1) parameterize the surface; 2) select a curve, such as a Low Discrepancy Curve, in a parameter space, for example, a simple space such as a unit square; 3) re-parameterize the surface, for example, re-parameterize the surface such that a ratio of line and area elements of the surface based on a Riemannian metric is constant; and 4) map the curve onto the surface using the re-parameterization. The method may also generate output comprising information regarding the mapped curve, for example, displaying the mapped curve on a display device.
Abstract:
A system and method for performing pattern matching to locate an instance of one or more of a plurality of template images in a target image. In a preprocessing phase a unified signal transform (UST) is determined from the template images. The UST converts each template image to a generalized frequency domain. The UST is applied at a generalized frequency to each template image to calculate corresponding generalized frequency component values (GFCVs) for each template image. At runtime, the target image is received, and the UST is applied at the generalized frequency to the target image to calculate a corresponding GFCV. The UST may be applied to pixel subsets of the template and target images. A best match is determined between the GFCV of the target image and the GFCVs of each template image. Finally, information indicating the best match template image from the set of template images is output.