Abstract: In one implementation, a graphical object classification system includes an acquisition module, a signature generation module, and a classification module. The acquisition module accesses a representation of a graphical object. The signature generation module generates an appearance-invariant signature of the graphical object based on the representation. The classification module classifies the graphical object based on the appearance-invariant signature.

Abstract: Systems, methods, and other embodiments associated with graphical user interface (GUI) component classification are described. One example method includes generating a first vector. The first vector may be generated based on image data describing a GUI component. The example method may also include assigning a GUI component classifier to the GUI component. Assigning the GUI component classifier may comprise comparing the first vector to members of a vector set. Members of the vector set may describe GUI elements. The example method may also include providing the GUI component classifier.

Abstract: A non-transitory storage device containing software than, when executed by a processor, causes the processor to generate a projection set of polynomials based on a projection of a space linear combination of candidate polynomials of degree d on polynomials of degree less than d that do not evaluate to less than a threshold on a set of points. The software also causes the processor to compute the singular value decomposition of a matrix containing the difference between candidate polynomials evaluated on the points and the projection set of polynomials evaluated on the points, and to partition the polynomials resulting from the singular value decomposition based on a threshold.

Abstract: A method includes obtaining, by executing a module stored on a non-transitory computer-readable storage device, approximately-zero polynomials for each of multiple classes. The method further includes evaluating the approximately-zero polynomials for each class on a plurality of points to compute distances from each point to each of the classes. The method also includes scaling the approximately-zero polynomials based on the distances and classifying the points based on the scaled approximately-zero polynomials.

Abstract: Tasks are mapped to execution threads of a parallel processing device. Tasks are mapped from the list of tasks to execution threads of the parallel processing device that are free. The parallel processing device is allowed to perform the tasks mapped to the execution threads of the parallel processing device for a predetermined number of execution cycles. When the parallel processing device has performed the tasks mapped to the execution threads of the parallel processing device for the predetermined number of execution cycles, the parallel processing device is suspended from further performing the tasks to allow the parallel processing device to determine which execution threads have completed performance of mapped tasks and are therefore free.

Abstract: According to one example, there is provided a method of determining a workflow in a production environment that comprises a plurality of production resources. The method comprises receiving a job request and determining, based on the contents of the job request and on a stored workflow history of job requests previously processed in the production environment, a predicted workflow to be used when processing the received job request.

Abstract: Automated inspection method for detecting a defect in a printed image, comprising processing a raster image, sending the raster image to a print process, printing a printed image corresponding to the raster image onto a medium, capturing a target image from at least a part of the printed image at a lower resolution than the printed image, at least in a medium moving direction, converting at least a part of the raster image to a reference image, and comparing the reference image to the target image.

Abstract: A method for compressing a plurality of coordinates includes obtaining a plurality of approximately-zero polynomials of dimension dim for a plurality of coordinate parameters. The method further includes selecting dim+1 non-approximately-zero polynomials, and providing a compressed data set that includes the approximately-zero polynomials, the dim+1 non-approximately-zero polynomials, and evaluations of the selected dim+1 non-approximately-zero polynomials based on the coordinates.

Abstract: Systems, methods, and other embodiments associated with graphical user interface (GUI) component identification are described. One example method includes identifying a set of GUI components. The identifying may be based on a screen image of a GUI. The screen image may comprise data describing a visual representation of the GUI. The example method may also include providing data identifying the set of GUI components.

Abstract: A computer can display a graphical user interface (GUI) that includes a control. The GUI can receive text instructions in a natural language that describe a location of the control on the GUI. The text instructions can instruct an application to perform a user interface (UI) event on the control.

Abstract: A method for compressing a plurality of coordinates includes obtaining a plurality of approximately-zero polynomials of dimension dim for a plurality of coordinate parameters. The method further includes selecting dim+1 non-approximately-zero polynomials, and providing a compressed data set that includes the approximately-zero polynomials, the dim+1 non-approximately-zero polynomials, and evaluations of the selected dim+1 non-approximately-zero polynomials based on the coordinates.

Abstract: A method includes obtaining, by executing a module stored on a non-transitory computer-readable storage device, approximately-zero polynomials for each of multiple classes. The method further includes evaluating the approximately-zero polynomials for each class on a plurality of points to compute distances from each point to each of the classes. The method also includes scaling the approximately-zero polynomials based on the distances and classifying the points based on the scaled approximately-zero polynomials.

Abstract: In one implementation, an image-based automation process includes identifying a graphical object of a graphical user interface and performing an action relative to the graphical object at the graphical user interface. The identifying is based on an image including the graphical object.

Abstract: In one implementation, a graphical object classification system includes an acquisition module, a signature generation module, and a classification module. The acquisition module accesses a representation of a graphical object. The signature generation module generates an appearance-invariant signature of the graphical object based on the representation. The classification module classifies the graphical object based on the appearance-invariant signature.

Abstract: A method of reducing blurring in an image of size greater than M columns by N rows of pixels, comprises deriving a blur kernel k representing the blur in the image, and deriving an inverse blur kernel k?1. The inverse blur kernel is given by (I) where h(m) is the sum of the first m terms of the series (II) ? is the Dirac delta, m is greater than 1, and h(m) is a two dimensional matrix of size M×N. The two dimensional matrix h(m) is convolved with the image over the whole image in the image pixel domain to produce an image with reduced blur. The method may be applied to a video sequence allowing the sequence of images to be deblurred in real time.

Abstract: Systems, methods, and other embodiments associated with graphical user interface input element identification are described. One example system identifies a graphical user interface (GUI) element through which an input to an application was received based on comparing images generated from data provided by the application. Data that describes the GUI element may then be provided.

Abstract: Tasks are mapped to execution threads of a parallel processing device. Tasks are mapped from the list of tasks to execution threads of the parallel processing device that are free. The parallel processing device is allowed to perform the tasks mapped to the execution threads of the parallel processing device for a predetermined number of execution cycles. When the parallel processing device has performed the tasks mapped to the execution threads of the parallel processing device for the predetermined number of execution cycles, the parallel processing device is suspended from further performing the tasks to allow the parallel processing device to determine which execution threads have completed performance of mapped tasks and are therefore free.

Abstract: Disclosed is a computer implemented method of detecting a defect in a printed image, the method comprising the steps of: receiving a target image comprising digital image data representing a scan of the printed image; receiving a reference image comprising digital image data representing a reference of the printed image; calculating a structural dissimilarity measure, D, associated with a target pixel located in the target image and a reference pixel located in the reference image; and, determining on the basis of the structural dissimilarity measure whether a defect is present at the target pixel, wherein the structural dissimilarity measure is calculated using a structural measure, s, and a contrast measure, c; the structural measure calculated using a spatial cross-correlation associated with a target region, {right arrow over (x)}, containing the target pixel and a reference region, {right arrow over (y)}, containing the reference pixel, and the contrast measure calculated using a standard deviation assoc

Abstract: A method of reducing blurring in an image of size greater than M columns by N rows of pixels, comprises deriving a blur kernel k representing the blur in the image, and deriving an inverse blur kernel k?1. The inverse blur kernel is given by (I) where h(m) is the sum of the first m terms of the series (II) ? is the Dirac delta, m is greater than 1, and h(m) is a two dimensional matrix of size M×N. The two dimensional matrix h(m) is convolved with the image over the whole image in the image pixel domain to produce an image with reduced blur. The method may be applied to a video sequence allowing the sequence of images to be deblurred in real time.

Abstract: Automated inspection method for detecting a defect in a printed image, comprising processing a raster image, sending the raster image to a print process, printing a printed image corresponding to the raster image onto a medium, capturing a target image from at least a part of the printed image at a lower resolution than the printed image, at least in a medium moving direction, converting at least a part of the raster image to a reference image, and comparing the reference image to the target image.