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 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 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: 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 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 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: 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 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.