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 (?), and a starting position; initialize a current position to the starting position; and increment components of the current position based on ? and the irrational numbers to generate the unbounded LDP.

Abstract: A system and method for performing a curve fit on a plurality of data points. In an initial phase, a subset Pmax of the plurality of points which represents an optimal curve is determined. This phase is based on a statistical model which dictates that after trying at most Nmin random curves, each connecting a randomly selected two or more points from the input set, one of the curves will pass within a specified radius of the subset Pmax of the input points. The subset Pmax may then be used in the second phase of the method, where a refined curve fit is made by iteratively culling outliers from the subset Pmax with respect to a succession of optimal curves fit to the modified subset Pmax at each iteration. The refined curve fit generates a refined curve, which may be output along with a final culled subset Kfinal of Pmax.

Abstract: A geometric pattern matching method to locate instances of a template image in a target image using a plurality of models with different feature combinations. A learning phase involves learning geometric information about the template image and creating a model for each of a plurality of different respective parent features. Each model comprises a relationship tree from the respective parent feature to one or more other child features, and hence each model may have different feature combinations. In a matching phase, the method may examine a plurality of different models to determine matches in the target image being analyzed. The matching phase may select different models dynamically based on prior matching results.

Abstract: A system and method for selecting a best match of a received input signal from a set of candidate signals, wherein two or more of the candidate signals are uncorrelated. In a preprocessing phase a unified signal transform (UST) is determined from the candidate signals. The UST converts each candidate signal to a generalized frequency domain. The UST is applied at a generalized frequency to each candidate signal to calculate corresponding generalized frequency component values (GFCVs) for each candidate signal. At runtime, the input signal of interest is received, and the UST is applied at the generalized frequency to the input signal of interest to calculate a corresponding GFCV. The best match is determined between the GFCV of the input signal of interest and the GFCVs of each of the set of candidate signals. Finally, information indicating the best match candidate signal from the set of candidate signals is output.

Abstract: A system and method for locating regions in a target image that match a template image with respect to color and pattern information. The method may comprise performing a first-pass search using color information obtained in a color characterization analysis of the template image in order to find a plurality of color match candidate locations. For each color match candidate location, a region proximal to the location may then be searched in detail, based on pattern information obtained in a pattern analysis of the template image.

Abstract: A system and method for scanning for an object within a region using a Low Discrepancy Sequence scanning scheme. 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) calculate a Low Discrepancy Sequence of points in the region; 2) generate a motion control trajectory from the Low Discrepancy Sequence of points (e.g., by generating a Traveling Salesman Path (TSP) from the Low Discrepancy Sequence of points and then re-sampling the TSP to produce a sequence of motion control points comprising the motion control trajectory); 3) scan the region along the motion control trajectory to determine one or more characteristics of the object in response to the scan. The method may also generate output indicating the one or more characteristics of the object.

Abstract: A scanning system and method for locating a point within a region. The method may: 1) determine or locate a region of interest in the region; 2) determine one or more characteristics of the region of interest within the region, wherein the region of interest includes the point of interest; 3) determine a continuous trajectory based on the one or more characteristics of the region of interest; 4) measure the region of interest at a plurality of points along the continuous trajectory to generate a sample data set; 5) perform a surface fit of the sample data set using the approximate model to generate a parameterized surface; and 6) calculate a location of the point of interest based on the parameterized surface. The method may include measuring the region at and/or near the calculated location to confirm the solution, and may also include generating output comprising the results.

Abstract: A system and method for locating regions in a target image that match a template image with respect to color and pattern information. The template image is characterized with regard to pattern and color. The method comprises performing a first-pass search using color information from the color characterization of the template image to find one or more color match candidate locations. For each color match candidate location, a luminance, i.e., gray scale, pattern matching search is performed on a region proximal to the location, producing one or more final match regions. For each final match region a hue plane pattern match score may be calculated using pixel samples from the interior of each pattern. A final color match score may be calculated for each final match region. A weighted sum of luminance pattern match, hue pattern match, and color match scores may be calculated, and the scores and sum output.

Abstract: A system and method for selecting a best match of a received input signal from a set of candidate signals, wherein two or more of the candidate signals are uncorrelated. In a preprocessing phase a signal transform (UST) is determined from the candidate signals. The UST converts each candidate signal to a generalized frequency domain. The UST is applied at a generalized frequency to each candidate signal to calculate corresponding generalized frequency component values (GFCVs) for each candidate signal. At runtime, the input signal of interest is received, and the UST is applied at the generalized frequency to the input signal of interest to calculate a corresponding GFCV. The best match is determined between the GFCV of the input signal of interest and the GFCVs of each of the set of candidate signals. Finally, information indicating the best match candidate signal from the set of candidate signals is output.

Abstract: A scanning system and method for scanning for an object within a region, or for locating a point within a region. Embodiments of the invention include a method for scanning for an object within a region using a Low Discrepancy Curve (LDC) scanning scheme. The method may: 1) generate a Low Discrepancy Sequence (LDS) of points in the region; 2) calculate an LDC in the region based on the LDS of points; and 3) scan the region along the LDC to determine one or more characteristics of the object in response to the scan. In calculating the LDC in the region based on the LDS of points, the method may connect sequential pairs of the LDS with contiguous orthogonal line segments (each parallel to a respective axis of the region), then sample the segments, generating points which may be used to generate the LDC, such as by a curve fit.

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 a curve fit on a plurality of data points. In an initial phase, a subset Pmax of the plurality of points which represents an optimal curve is determined. This phase is based on a statistical model which dictates that after trying at most Nmin random curves, each connecting a randomly selected two or more points from the input set, one of the curves will pass within a specified radius of the subset Pmax of the input points. The subset Pmax may then be used in the second phase of the method, where a refined curve fit is made by iteratively culling outliers from the subset Pmax with respect to a succession of optimal curves fit to the modified subset Pmax at each iteration. The refined curve fit generates a refined curve, which may be output along with a final culled subset Kfinal of Pmax.

Abstract: A system and method for measuring the similarity of multiple-color images and for locating regions of a target image having color information that matches, at least to a degree, the color information of a template image. A color characterization method operates to characterize the colors of an image and to measure the similarity between multiple-color images. For each image pixel, the method determines a color category or bin for the respective pixel based on HSI values of the respective pixel, wherein the color category is one of a plurality of possible color categories in HSI color space. In various embodiments, the weight of the pixel may be fractionally distributed across a plurality of color categories, e.g., as determined by applying fuzzy pixel classification with a fuzzy membership function. The percentage of pixels assigned to each category is then determined. The percentage of pixels in each color category is then used as a color feature vector to represent the color information of the color image.

Abstract: A system and method for scanning for an object within a region using a conformal scanning scheme. 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) determine the characteristic geometry of the region; 2) generate a conformal scanning curve based on the characteristic geometry of the region by performing a conformal mapping between the characteristic geometry and a first scanning curve to generate the conformal scanning curve, i.e., mapping points of the first scanning curve to the characteristic geometry of the region; and 3) scan the region using the conformal scanning curve. These measurements of the region produce data indicative of one or more characteristics of the object. The method may also generate output indicating the one or more characteristics of the object.

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.

Abstract: System and method for automatically generating a graphical program to perform an image processing algorithm. A user may develop an image processing algorithm in an image prototyping environment. The image prototyping environment enables the user to easily apply various image processing functions to an image and immediately see the results, in order to develop the desired algorithm. As the user applies each image processing function to an image, the function may be recorded as a step in a script. Once the user has developed an algorithm, the user may request the image prototyping environment to automatically generate a program implementing the image processing algorithm. In various embodiments, the prototyping environment may be operable to generate different types of programs, including text-based and graphical programs.

Abstract: A color characterization method operates to analyze each respective pixel of at least a subset of the pixels of an image object. The image is obtained in HSI format, or alternatively converted from another format to HSI. For each respective pixel, the method determines a color category or bin for the respective pixel based on values of the respective pixel. The color category is one a plurality of possible color categories or bins in the HSI color space. As the pixels are analyzed and assigned to color categories, the method stores information in tho computer regarding the number or percentage or pixels in each of the color categories. A color matching method uses the color characterization method. The color matching method determines similarity of colors between a template image object and a region of interest (ROI).

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: A system and method for improving the accuracy and convergence rate in determining the affine transformation of one image or array of values with respect to another image or array of values. A particular embodiment of the present invention comprises a reference and an input image. A gradient matrix, &lgr;, may be constructed to contain gradient information of the position values of the reference image. Also, an estimate matrix, &rgr;, may be constructed to contain initial estimates of at least one of position, angle and scale of the input image. The input image is then subtracted from the reference image pixel by pixel producing an error matrix, e. The error matrix, e, is then multiplied with the matrices, &lgr; and &rgr;. The result is the new change in the position, angle and scale of the input image. New values for the position, angle and scale are calculated from the changes in the position, angle and scale values of the input image.

Abstract: A machine vision system and method for performing illumination line analysis on an image of an object to detect defects in the object. The method may comprise projecting a pattern of lines on a surface of the object, and then generating an image of the surface of the object. The analysis method tracks left and right edges of each of the illumination lines to determine width and curvature of each of the lines, preferably using a bi-directional edge detection technique applied to a path perpendicular to the current orientation of the line. Information regarding the left and right edges of the line may be used to determine local widths and local orientations of the line. This information may be used to determine if a thinning or blooming of the line occurs, or if a change in curvature of the line occurs, which may indicate a possible defect in the object.