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

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 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 locating regions in a target image matching a template image with respect to color and pattern information. The template image is characterized with regard to pattern and color. A first-pass search is made using color information from the color characterization of the template image to find color match candidate locations preferably via a hill-climbing technique. For each color match candidate location, a luminance pattern matching search is performed, optionally using a hill-climbing technique, on a region proximal to the location, producing 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 final score is calculated from luminance pattern match, color match, and possibly hue pattern match, scores, and the scores and sum output.

Abstract: A system and method for improved image characterization, object placement, and mesh design utilizing Low Discrepancy sequences. The Low Discrepancy sequence is designed to produce sample points which maximally avoid one another, i.e., the distance between any two sample points is maximized. The invention may be applied specifically to methods of image characterization, pattern matching, acquiring image statistics, object location, image reconstruction, motion estimation, object placement, sensor placement, and mesh design, among others. Image characterization is performed by receiving an image and then sampling the image using a Low Discrepancy sequence, also referred to as a quasi-random sequence, to determine a plurality of sample pixels in the image which characterize the image. Sensor placement is performed by generating a Low Discrepancy sequence for the desired placement application, and then selecting locations for the optimal placement of sensors using the generated Low Discrepancy sequence.

Abstract: A system and method for performing pattern matching to locate zero or more instances of a template image in a target image. The method first comprises sampling the template image using a Low Discrepancy sequence, also referred to as a quasi-random sequence, to determine a plurality of sample pixels in the template image which accurately characterize the template image. The Low Discrepancy sequence is designed to produce sample points which maximally avoid each other. After the template image is sampled or characterized, the method then performs pattern matching using the sample pixels and the target image to determine zero or more locations of the template image in the target image. The method may also perform a local stability analysis around at least a subset of the sample pixels to determine a lesser third number of sample pixels which have a desired degree of stability, and then perform pattern matching using the third plurality of sample pixels.

Abstract: A system and method for performing pattern matching to locate zero or more instances of a template image in a target image. The method first comprises sampling the template image using a Low Discrepancy sequence, also referred to as a quasi-random sequence, to determine a plurality of sample pixels in the template image which accurately characterize the template image. The Low Discrepancy sequence is designed to produce sample points which maximally avoid each other. After the template image is sampled or characterized, the method then performs pattern matching using the sample pixels and the target image to determine zero or more locations of the template image in the target image. The method may also perform a local stability analysis around at least a subset of the sample pixels to determine a lesser third number of sample pixels which have a desired degree of stability, and then perform pattern matching using the third plurality of sample pixels.

Abstract: A system and method for performing pattern matching to locate zero or more instances of a template image in a target image. The method first comprises sampling the template image using a Low Discrepancy sequence, also referred to as a quasi-random sequence, to determine a plurality of sample pixels in the template image which accurately characterize the template image. The Low Discrepancy sequence is designed to produce sample points which maximally avoid each other. After the template image is sampled or characterized, the method then performs pattern matching using the sample pixels and the target image to determine zero or more locations of the template image in the target image. The method may also perform a local stability analysis around at least a subset of the sample pixels to determine a lesser third number of sample pixels which have a desired degree of stability, and then perform pattern matching using the third plurality of sample pixels.