Abstract: Methods and systems for model-based image processing are provided. One method includes selecting at least one reference image from a plurality of reference images, partitioning the at least one reference image into a plurality of patches, generating a probability distribution for each of the patches, and generating a model of a probability distribution for the at least one reference image using the probability distributions for each of the patches.

Abstract: As set forth herein, a computer-implemented method facilitates pre-analyzing an image and automatically suggesting to the user the most suitable regions within an image for text-based personalization. Image regions that are spatially smooth and regions with existing text (e.g. signage, banners, etc.) are primary candidates for personalization. This gives rise to two sets of corresponding algorithms: one for identifying smooth areas, and one for locating text regions. Smooth regions are found by dividing the image into blocks and applying an iterative combining strategy, and those regions satisfying certain spatial properties (e.g. size, position, shape of the boundary) are retained as promising candidates. In one embodiment, connected component analysis is performed on the image for locating text regions. Finally, based on the smooth and text regions found in the image, several alternative approaches are described herein to derive an overall metric for “suitability for personalization.

Abstract: A method for reconstructing an image of an object that includes a plurality of image elements. The method includes accessing image data associated with a plurality of image elements, and reconstructing an image of the object by optimizing an objective function, where the objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. The algorithm is composed of nested iterative loops, in which an inner loop iteratively optimizes an objective function approximating the outer loop objective function, and an outer loop that utilizes the solution of the inner loop to optimize the original objective function.

Abstract: The present disclosure includes a system and method for symbol compression using conditional entropy estimation. One method for symbol compression using conditional entropy estimation includes approximating a quantity of symbol encoding bits for a number of symbols using a conditional entropy estimation. Dictionary entries are generated from the number of symbols so as to minimize a total bit-stream quantity. The total bit-stream quantity includes at least the approximated quantity of symbol encoding bits and a quantity of dictionary entries encoding bits. The symbols are encoded using the dictionary entries as a reference.

Abstract: A method for reconstructing image component densities of an object includes acquiring multi-spectral x-ray tomographic data, performing a material decomposition of the multi-spectral x-ray tomographic data to generate a plurality of material sinograms, and reconstructing a plurality of material component density images by iteratively optimizing a functional that includes a joint likelihood term of at least two of the material decomposed sinograms. An x-ray tomography imaging system and a non-transitory computer readable medium are also described herein.

Abstract: A framework for an iterative reconstruction algorithm is described which combines two or more of an ordered subset method, a preconditioner method, and a nested loop method. In one type of implementation a nested loop (NL) structure is employed where the inner loop sub-problems are solved using ordered subset (OS) methods. The inner loop may be solved using OS and a preconditioner method. In other implementations, the inner loop problems are created by augmented Lagrangian methods and then solved using OS method.

Abstract: A method is provided for reconstructing an image of an object that includes image elements. The method includes accessing measurement data associated with the image elements, introducing an auxiliary variable to transform an original problem of reconstructing the image to a constrained optimization problem, and solving the constrained optimization problem using a method of multipliers to create a sequence of sub-problems and solve the sequence of sub-problems. Solving the sequence of sub-problems includes reconstructing the image by optimizing a first objective function. The first objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. An inner loop iteratively optimizes a second objective function approximating the first objective function. An outer loop utilizes the solution of the second objective function to optimize the first objective function.

Abstract: As set forth herein, systems and methods facilitate providing an efficient edge-detection and closed-contour based approach for finding text in natural scenes such as photographic images, digital, and/or electronic images, and the like. Edge information (e.g., edges of structures or objects in the images) is obtained via an edge detection technique. Edges from text characters form closed contours even in the presence of reasonable levels of noise. Closed contour linking and candidate text line formation are two additional features of the described approach. A candidate text line classifier is applied to further screen out false-positive text identifications. Candidate text regions for placement of text in the natural scene of the electronic image are highlighted and presented to a user.

Abstract: As set forth herein, a computer-implemented method is employed to place personalized text into an image. A location within the image is selected where the text is to be placed, and region is grown around the selected location. The 3D geometry of the surface is estimated proximate to the location and sets of parallel straight lines in the image are identified and selected to define a bounding polygon into which text may be inserted. Optionally, a user is permitted to adjust the bounding polygon once it has been automatically generated.

Abstract: An apparatus usable in an image encoding and/or decoding system includes a segmentation unit to convert a first image of a first resolution into a second image of a second resolution, to segment the second image of the second resolution with one or more blocks of a binary mask layer having a foreground and a background, and to convert the segmented second image into a third image of a third resolution as a segmented image.

Abstract: As set forth herein, a computer-implemented method is employed to place personalized text into an image. A location and region within the image is determined where the text is to be placed. The 3D geometry of the surface is estimated proximate to the location where the text is to be placed. A personalized text string is received. The personalized text string is incorporated into the image to appear as if it is rendered onto the surface or object according to the estimated 3D geometry.

Abstract: The present disclosure includes a system and method for symbol compression using conditional entropy estimation. One method for symbol compression using conditional entropy estimation includes approximating a quantity of symbol encoding bits for a number of symbols using a conditional entropy estimation. Dictionary entries are generated from the number of symbols so as to minimize a total bit-stream quantity. The total bit-stream quantity includes at least the approximated quantity of symbol encoding bits and a quantity of dictionary entries encoding bits. The symbols are encoded using the dictionary entries as a reference.

Abstract: As set forth herein, a computer-implemented method facilitates replacing text on cylindrical or curved surfaces in images. For instance, the user is first asked to perform a multi-click selection of a polygon to bound the text. A triangulation scheme is carried out to identify the pixels. Segmentation and erasing algorithms are then applied. The ellipses are estimated accurately through constrained least squares fitting. A 3D framework for rendering the text, including the central projection pinhole camera model and specification of the cylindrical object, is generated. These parameters are jointly estimated from the fitted ellipses as well as the two vertical edges of the cylinder. The personalized text is wrapped around the cylinder and subsequently rendered.

Abstract: A method for reconstructing image component densities of an object includes acquiring multi-spectral x-ray tomographic data, performing a material decomposition of the multi-spectral x-ray tomographic data to generate a plurality of material sinograms, and reconstructing a plurality of material component density images by iteratively optimizing a functional that includes a joint likelihood term of at least two of the material decomposed sinograms. An x-ray tomography imaging system and a non-transitory computer readable medium are also described herein.

Abstract: A method is provided for reconstructing an image of an object that includes image elements. The method includes accessing measurement data associated with the image elements, introducing an auxiliary variable to transform an original problem of reconstructing the image to a constrained optimization problem, and solving the constrained optimization problem using a method of multipliers to create a sequence of sub-problems and solve the sequence of sub-problems. Solving the sequence of sub-problems includes reconstructing the image by optimizing a first objective function. The first objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. An inner loop iteratively optimizes a second objective function approximating the first objective function. An outer loop utilizes the solution of the second objective function to optimize the first objective function.

Abstract: As set forth herein, systems and methods facilitate providing an efficient edge-detection and closed-contour based approach for finding text in natural scenes such as photographic images, digital, and/or electronic images, and the like. Edge information (e.g., edges of structures or objects in the images) is obtained via an edge detection technique. Edges from text characters form closed contours even in the presence of reasonable levels of noise. Closed contour linking and candidate text line formation are two additional features of the described approach. A candidate text line classifier is applied to further screen out false-positive text identifications. Candidate text regions for placement of text in the natural scene of the electronic image are highlighted and presented to a user.

Abstract: As set forth herein, a computer-implemented method facilitates pre-analyzing an image and automatically suggesting to the user the most suitable regions within an image for text-based personalization. Image regions that are spatially smooth and regions with existing text (e.g. signage, banners, etc.) are primary candidates for personalization. This gives rise to two sets of corresponding algorithms: one for identifying smooth areas, and one for locating text regions. Smooth regions are found by dividing the image into blocks and applying an iterative combining strategy, and those regions satisfying certain spatial properties (e.g. size, position, shape of the boundary) are retained as promising candidates. In one embodiment, connected component analysis is performed on the image for locating text regions. Finally, based on the smooth and text regions found in the image, several alternative approaches are described herein to derive an overall metric for “suitability for personalization.

Abstract: An image processing method of an image processing apparatus, the method includes: discerning pixels to be processed on the basis of a preset reference pixel value in a scanned image that comprises plural pixels; drawing a pixel area including the pixels to be processed from the scanned image on the basis of a preset condition; and selecting whether to apply an image process to the pixels to be processed or the drawn pixel area on the basis of a percentage of the pixels to be processed in the drawn pixel area.

Abstract: A method for reconstructing an image of an object that includes a plurality of image elements. The method includes accessing image data associated with a plurality of image elements, and reconstructing an image of the object by optimizing an objective function, where the objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. The algorithm is composed of nested iterative loops, in which an inner loop iteratively optimizes an objective function approximating the outer loop objective function, and an outer loop that utilizes the solution of the inner loop to optimize the original objective function.

Abstract: An apparatus usable in an image encoding and/or decoding system includes a segmentation unit to convert a first image of a first resolution into a second image of a second resolution, to segment the second image of the second resolution with one or more blocks of a binary mask layer having a foreground and a background, and to convert the segmented second image into a third image of a third resolution as a segmented image.