Abstract: A system for monitored tomographic reconstruction, comprising: an x-ray generator configure to generate x-ray beams for scanning an object; detectors configured to capture a plurality of projections for each scan; at least one hardware processor; and one or more software modules that, when executed by the at least one hardware processor, receive the plurality of projections from the detectors and as each of the plurality of projections is received, generate a partial reconstruction, and make a stopping decision with respect to whether or not another projection should be obtained based on a stopping problem and that defines when a reconstructed image quality is sufficient with respect to the expended cost as determined by a stopping rule.

Abstract: Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.

Abstract: Real-time monitored computed tomography (CT) reconstruction for reducing a radiation does. During helical CT scanning of a target object, projections may be acquired in either a full mode which subjects the target object to a full radiation dose, or a reduced mode which subjects the target object to a reduced radiation dose (e.g., by reducing the number of projections acquired, reducing the exposure time, etc.). After a sector is acquired in the full mode, a slice of the target object that is influenced by that sector is identified, and a CT image of that slice is reconstructed using projections that have been previously acquired for that slice. When a stopping rule is satisfied based on this partial reconstruction, the full mode is switched to the reduced mode, and at least one subsequent sector is acquired in the reduced mode.

Abstract: A system for monitored tomographic reconstruction, comprising: an x-ray generator configure to generate x-ray beams for scanning an object; detectors configured to capture a plurality of projections for each scan; at least one hardware processor; and one or more software modules that, when executed by the at least one hardware processor, receive the plurality of projections from the detectors and as each of the plurality of projections is received, generate a partial reconstruction, and make a stopping decision with respect to whether or not another projection should be obtained based on a stopping problem and that defines when a reconstructed image quality is sufficient with respect to the expended cost as determined by a stopping rule.

Abstract: Advanced Hough-Based On-Device Document Localization. In an embodiment, lines are detected in an input image of a document. The lines are searched for candidate quadrilaterals. For at least a subset of the found candidate quadrilaterals, a contour score is calculated, and the candidate quadrilaterals are saved or discarded based on their contour scores. For each saved candidate quadrilateral, a contrast score is calculated. A final candidate quadrilateral is selected, based on the combined contour and contrast scores for the saved candidate quadrilaterals, to represent the borders of the document.

Abstract: Unified framework for analysis and recognition of identity documents. In an embodiment, an image is received. A document is located in the image and an attempt is made to identify one or more of a plurality of templates that match the document. When template(s) that match the document are identified, for each of the template(s) and for each of one or more zones in the template, a sub-image of the zone is extracted from the image. For each extracted sub-image, one or more objects are extracted from the sub-image. For each extracted object, object recognition is performed. This may be done over one iteration (e.g., for a scanned image or photograph) or a plurality of iterations (e.g., for a video). Document recognition is performed based on the one or more templates and the results of the object recognition, and a final document-recognition result is output.

Abstract: Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.

Abstract: Efficient location and identification of documents in images. In an embodiment, at least one quadrangle is extracted from an image based on line(s) extracted from the image. Parameter(s) are determined from the quadrangle(s), and keypoints are extracted from the image based on the parameter(s). Input descriptors are calculated for the keypoints and used to match the keypoints to reference keypoints, to identify classification candidate(s) that represent a template image of a type of document. The type of document and distortion parameter(s) are determined based on the classification candidate(s).

Abstract: A method for detecting security holograms on documents in a video stream is disclosed, including: searching for interest points and calculating descriptors in a frame; filtering of interest points in the previous frame so that only points located inside the quadrangle of the outer borders of the document remain; matching the descriptors of interest points of the current and previous frames; application of an algorithm for estimating the parameters of projective transformation between the frames; projective transformation of the quadrangle of the outer boundaries of the document from the previous frame to obtain the outer boundaries of the document in the current frame; document image normalization; calculating the color saturation and hue; updating the saturation and hue values; further considering the pixels of the normalized document image with brightness values not exceeding a preset threshold; filtration of the obtained image.

Abstract: Efficient location and identification of documents in images. In an embodiment, at least one quadrangle is extracted from an image based on line(s) extracted from the image. Parameter(s) are determined from the quadrangle(s), and keypoints are extracted from the image based on the parameter(s). Input descriptors are calculated for the keypoints and used to match the keypoints to reference keypoints, to identify classification candidate(s) that represent a template image of a type of document. The type of document and distortion parameter(s) are determined based on the classification candidate(s).

Abstract: Computed tomography (CT) image reconstruction from polychromatic projection data. In an embodiment, polychromatic projection data is acquired using a CT system. An optimal correction value for linearization of the polychromatic projection data is determined, and the polychromatic projection data is linearized according to the determined optimal correction value. The image is then reconstructed from the linearized projection data.

Abstract: A bipolar morphological neural network may be generated by converting an initial neural network by replacing multiplication calculations in one or more convolutional layers with approximations that utilize maximum/minimum and/or addition/subtraction operations. The remaining part of the network may be trained after each convolutional layer is converted.

Abstract: Accelerated filtered back projection for computed tomography image reconstruction. In an embodiment, a filtered back projection is performed on a sinogram. The back projection may comprise transitioning the sinogram to a linogram using linear interpolation, and performing an inverse Fast Hough Transform on the linogram to produce a reconstructed CT image. Filtering in the filtered back projection may use an infinite impulse response (IIR) filter.

Abstract: Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.

Abstract: Efficient location and identification of documents in images. In an embodiment, at least one quadrangle is extracted from an image based on line(s) extracted from the image. Parameter(s) are determined from the quadrangle(s), and keypoints are extracted from the image based on the parameter(s). Input descriptors are calculated for the keypoints and used to match the keypoints to reference keypoints, to identify classification candidate(s) that represent a template image of a type of document. The type of document and distortion parameter(s) are determined based on the classification candidate(s).

Abstract: A system for monitored tomographic reconstruction, comprising: an x-ray generator configure to generate x-ray beams for scanning an object; detectors configured to capture a plurality of projections for each scan; at least one hardware processor; and one or more software modules that, when executed by the at least one hardware processor, receive the plurality of projections from the detectors and as each of the plurality of projections is received, generate a partial reconstruction, and make a stopping decision with respect to whether or not another projection should be obtained based on a stopping problem and that defines when a reconstructed image quality is sufficient with respect to the expended cost as determined by a stopping rule.

Abstract: A method for detecting holographic elements in a video stream containing images in the form of documents includes: processing of a video stream in which the document image is stabilized; constructing saturation and color tone maps; analyzing color characteristics in image regions; constructing histograms of color characteristics; estimating a change in the color characteristics at least in part based on data obtained by calculating a difference between the histograms of a current and a previous frame; constructing an integrated map of hologram presence estimates by combining calculated estimates for all video stream frames based at least in part on the estimation of the change in color characteristics; and determining final regions of the holographic elements based at least in part on the integrated map of the hologram presence estimates.

Abstract: A method for detecting holographic elements in a video stream containing images in the form of documents includes: processing of a video stream in which the document image is stabilized; constructing saturation and color tone maps; analyzing color characteristics in image regions; constructing histograms of color characteristics; estimating a change in the color characteristics at least in part based on data obtained by calculating a difference between the histograms of a current and a previous frame; constructing an integrated map of hologram presence estimates by combining calculated estimates for all video stream frames based at least in part on the estimation of the change in color characteristics; and determining final regions of the holographic elements based at least in part on the integrated map of the hologram presence estimates.