Abstract: A method and apparatus to select from an image (30) of a first sample (28), at least one region (44) digitally captured at a first resolution based upon how a counterfeit identification performance attribute of each region (44) digitally captured at the first resolution correlate to the counterfeit identification performance attribute of the region (44) digitally captured at a second resolution higher than the first resolution. The selected region (44) is used to determine whether the image (30) on a second sample is a counterfeit.

Abstract: A counterfeit identification performance attribute (SIPA) sensitivity to changes in resolution of the image for features of an image is determined. The CIPA sensitivity for the features is used to choose at least one feature to determine whether the image on a sample is a counterfeit.

Abstract: A method and apparatus to select from an image (30) of a first sample (28), at least one region (44) digitally captured at a first resolution based upon how a counterfeit identification performance attribute of each region (44) digitally captured at the first resolution correlate to the counterfeit identification performance attribute of the region (44) digitally captured at a second resolution higher than the first resolution.

Abstract: A counterfeit identification performance attribute (SIPA) sensitivity to changes in resolution of the image for features of an image is determined. The CIPA sensitivity for the features is used to choose at least one feature to determine whether the image on a sample is a counterfeit.

Abstract: Methods, and apparatus for performing methods, for classifying an image. Methods include determining a corresponding set of metrics for each region of two or more regions of a pattern of regions of an image, and classifying the image in response to at least the corresponding set of metrics for each of the two or more regions of the pattern of regions.

Abstract: A counterfeit identification performance attribute (CIPA) sensitivity to changes in resolution of the image for features of an image is determined (102). The CIPA sensitivity for the features is used to choose at least one feature to determine whether the image on a sample is a counterfeit (106).

Abstract: Methods, and apparatus for performing methods, for selecting a classifier engine. Methods include, for two or more portions of a set of items of known classification, classifying members of each portion using a particular classifier engine; selecting a portion of the set of items whose classifications satisfy a first criteria; classifying members of the selected portion of the set of items using two or more classifier engines; and selecting a classifier engine whose classification of the selected portion of the set of items satisfies a second criteria.

Abstract: A counterfeit identification performance attribute (CIPA) sensitivity to changes in resolution of the image for features of an image is determined (102). The CIPA sensitivity for the features is used to choose at least one feature to determine whether the image on a sample is a counterfeit (106).

Abstract: An image analyzer (120) aggregates image samples (140) into a cluster (170) based on the image samples (140) being classified from a subset of image metrics applied to a reference sample (130). The image analyzer (120) generates an image quality output (150) by analyzing a distance (180) from the cluster (180) relative to another cluster.

Abstract: A system can comprise a memory to store machine readable instructions and a processing unit to access the memory and execute the machine readable instructions. The machine readable instructions can comprise a feature set extractor to extract a feature set from each of a plurality of digital images of print samples. The feature set can be a filtered feature set that includes a feature set characterizing a printer that printed a given print sample of the print samples. The machine readable instructions can also comprise a cluster component to determine clusters of the print samples based on the feature set of each of the plurality of scanned images of the print samples. The machine readable instructions can further comprise a printer identifier to identify the printer of the print samples based on the clusters of the print samples.

Abstract: Methods, and apparatus for performing methods, for selecting a classifier engine. Methods include, for two or more portions of a set of items of known classification, classifying members of each portion using a particular classifier engine; selecting a portion of the set of items whose classifications satisfy a first criteria; classifying members of the selected portion of the set of items using two or more classifier engines; and selecting a classifier engine whose classification of the selected portion of the set of items satisfies a second criteria.

Abstract: Methods, and apparatus for performing methods, for classifying an image. Methods include determining a corresponding set of metrics for each region of two or more regions of a pattern of regions of an image, and classifying the image in response to at least the corresponding set of metrics for each of the two or more regions of the pattern of regions.

Abstract: Methods for selecting metrics for substrate classification, and apparatus to perform such methods. The methods include determining a value of a metric from an image of a substrate sample for each substrate sample of a plurality of substrate samples, wherein the metric is indicative of a surface texture of each substrate sample and iteratively assigning substrate samples of the plurality of substrate samples to an aggregate of a particular number of aggregates in response to a value of the metric for each substrate sample until a convergence of clustering is deemed achieved, then determining an indication of cluster tightness of the particular number of aggregates. The methods further include selecting or ignoring the metric for substrate classification in response to the indication of cluster tightness of the particular number of aggregates.

Abstract: A system can comprise a memory to store machine readable instructions and a processing unit to access the memory and execute the machine readable instructions. The machine readable instructions can comprise a feature set extractor to extract a feature set from each of a plurality of digital images of print samples. The feature set can be a filtered feature set that includes a feature set characterizing a printer that printed a given print sample of the print samples. The machine readable instructions can also comprise a cluster component to determine clusters of the print samples based on the feature set of each of the plurality of scanned images of the print samples. The machine readable instructions can further comprise a printer identifier to identify the printer of the print samples based on the clusters of the print samples.

Abstract: A system for enhancing security printing includes a segmentation system, a secure database in operative communication with the segmentation system, a secure registry in selective operative communication with the segmentation system, and an analysis system in operative communication with the segmentation system and the secure database, and in selective operative communication with the secure registry. The segmentation system performs zoning analysis on a scanned image to identifying a list of regions in the image. The secure database stores at least one of i) a template, or ii) prior zoning output specification. The secure registry stores region of interest information and information pertaining to strategies for identifying a region of interest. The analysis system identifies the region of interest utilizing at least one of the secure database or the secure registry.

Abstract: An image analyzer (120) aggregates image samples (140) into a cluster (170) based on the image samples (140) being classified from a subset of image metrics applied to a reference sample (130). The image analyzer (120) generates an image quality output (150) by analyzing a distance (180) from the cluster (180) relative to another cluster.

Abstract: Methods for selecting metrics for substrate classification, and apparatus to perform such methods. The methods include determining a value of a metric from an image of a substrate sample for each substrate sample of a plurality of substrate samples, wherein the metric is indicative of a surface texture of each substrate sample and iteratively assigning substrate samples of the plurality of substrate samples to an aggregate of a particular number of aggregates in response to a value of the metric for each substrate sample until a convergence of clustering is deemed achieved, then determining an indication of cluster tightness of the particular number of aggregates. The methods further include selecting or ignoring the metric for substrate classification in response to the indication of cluster tightness of the particular number of aggregates.

Abstract: A method for enhancing security printing includes generating data for a region of interest (14), where the data includes information for at least one of locating or analyzing the region of interest (14) using a secure registry (18). The method further includes encrypting a deterrent (12) with the data, and printing the deterrent (12) and the region of interest (14) on an object (10).

Abstract: A method for testing image pipelines includes transforming an untransformed captured image multiple times using a plurality of pipelines, thereby generating a plurality of transformed images, and comparing a functional set of metrics associated with each of the plurality of transformed images with a functional set of metrics associated with each image in a ground truth set. From the comparison, a determination is made as to which of the plurality of pipelines generates a transformed image that is functionally closest to an image in the ground truth set.

Abstract: A variable data addition method and system are disclosed herein. The method includes generating an original image, and defining at least one sub-section of the original image to transform individually. A reversible transformation is applied to the defined at least one sub-section, thereby altering the original image to form an altered image that is non-readable until an inverse of the reversible transformation is applied to the altered image.