Abstract: A control circuit accesses three-dimensional image information for a given three-dimensional object. The control circuit accesses a selection corresponding to a feature of the three-dimensional object, and then automatically generates a plurality of synthetic images of the three-dimensional object as a function of the three-dimensional and the selection of the aforementioned feature. By one approach, these synthetic images include supplemental visual emphasis corresponding to the aforementioned feature. The generated plurality of synthetic images can then be used as a training corpus when training a machine learning model.

Abstract: According to some embodiments, a system and method are provided comprising a vegetation module to receive image data from an image source; a memory for storing program instructions; a vegetation processor, coupled to the memory, and in communication with the vegetation module, and operative to execute program instructions to: receive image data; estimate a vegetation segmentation mask; generate at least one of a 3D point cloud and a 2.5D Digital Surface Model based on the received image data; estimate a relief surface using a digital terrain model; generate a vegetation masked digital surface model based on the digital terrain model, the vegetation segmentation mask and at least one of the 3D point cloud and the 2.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an imagery data source storing image data from a plurality of images; a ground point module; a memory storing program instructions; and a ground point processor, coupled to the memory, and in communication with the ground point module and operative to execute the program instructions to: receive image data for an area of interest (AOI); generate a digital surface map from the received image data, wherein the digital surface map includes an elevation value for each of a plurality of points on the digital surface map; generate a ground point sampling based on the elevation values for the plurality of points on the digital surface map; generate an image boundary sampling based on elevation values for the plurality of points along a plurality of edges of the area of interest; and interpolate the generated ground point sampling and the image boundary sampling to generate a digital terrain map.

Abstract: Devices and methods are provided herein useful to aligning or linking together data from multiple inspections of a part. In some embodiments, the inspection methods include detecting key points on inspection data associated with the part, such as images. Key points provide the locations of features of interest on the part. Key points for the part are represented by feature descriptors. The inspection methods then link together data from two or more inspections by matching the feature descriptors and calculating a distance between matched key points. The inspection methods identify the proper alignment of inspection data from two or more inspections by identifying the alignment that achieves a minimum distance between matched key points on the inspection data. In this manner, the inspection methods align or link together inspection data from two or more different inspections to enable the comparative analysis of inspection data for a part.

Abstract: A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

Abstract: A method for inspecting an object includes determining a first inspection package that includes a first inspection image of the object and a first designation. The method includes determining data indicative of a second inspection package that includes a second inspection image of the object and a second designation. The method includes determining a first property of the object based on the first inspection image of the object, one or more properties maps of the object, and the first designation. The method includes determining a second property of the object based on the second inspection image of the object, the one or more properties maps of the object, and the second designation. The method includes displaying the first property and the second property or displaying data indicative of a comparison of the first property with the second property.

Abstract: A method for inspecting an object includes determining guide image data of the object from a determined orientation, the guide image data including a guide image pixel array and a pixel property for at least one guide image pixel in the guide image pixel array. The method also includes receiving inspection image data indicative of an inspection image and associating the inspection image data with the guide image data with a processor of a computing device. Additionally, the method includes determining a property of the object based on the guide image data and the associated inspection image data.

Abstract: A method for inspecting an object includes receiving or determining inspection image data, the inspection image data including an inspection image pixel array with at least one inspection image pixel in the inspection image pixel array having a pixel property associated therewith. The method includes receiving via a processor a user input associated with a continuous segment of inspection image pixels in the inspection image pixel array. The method includes determining a property of the object based on the pixel properties associated with the continuous segment of inspection image pixels in the inspection image pixel array.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a 3D building modeling module; a memory for storing program instructions; a 3D building modeling processor, coupled to the memory, and in communication with the 3D building modeling module and operative to execute program instructions to: receive a region of interest; receive an image of the region of image from a data source; generate a surface model based on the received image including one or more buildings; generate a digital height model; decompose each building into a set of shapes; apply a correction process to the set of shapes; execute a primitive classification process to each shape; execute a fitting process to each classified shape; select a best fitting model; and generate a 3D model of each building. Numerous other aspects are provided.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an image data source storing image data from a plurality of images; a height map source storing height maps for an area of interest (AOI); a building footprint module; a memory; and a building footprint processor, operative to execute the program instructions to: receive image data for an AOI; receive a height map for the AOI; execute a building segmentation module to generate a building mask that indicates a presence of one or more buildings in the AOI; apply at least one clean mask process to the generated building mask to generate a clean mask; receive the clean mask at an instance building segmentation module; and execute the instance building segmentation module to generate at least one building footprint based on the clean mask and the received image data. Numerous other aspects are provided.

Abstract: A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

Abstract: According to some embodiments, a system and method are provided comprising a vegetation module to receive image data from an image source; a memory for storing program instructions; a vegetation processor, coupled to the memory, and in communication with the vegetation module, and operative to execute program instructions to: receive image data; estimate a vegetation segmentation mask; generate at least one of a 3D point cloud and a 2.5D Digital Surface Model based on the received image data; estimate a relief surface using a digital terrain model; generate a vegetation masked digital surface model based on the digital terrain model, the vegetation segmentation mask and at least one of the 3D point cloud and the 2.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an imagery data source storing image data from a plurality of images; a ground point module; a memory storing program instructions; and a ground point processor, coupled to the memory, and in communication with the ground point module and operative to execute the program instructions to: receive image data for an area of interest (AOI); generate a digital surface map from the received image data, wherein the digital surface map includes an elevation value for each of a plurality of points on the digital surface map; generate a ground point sampling based on the elevation values for the plurality of points on the digital surface map; generate an image boundary sampling based on elevation values for the plurality of points along a plurality of edges of the area of interest; and interpolate the generated ground point sampling and the image boundary sampling to generate a digital terrain map.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an image data source storing image data from a plurality of images; a height map source storing height maps for an area of interest; a building footprint module; a memory storing program instructions; and a building footprint processor, coupled to the memory, and in communication with the building footprint module and operative to execute the program instructions to: receive image data for an area of interest (AOI); receive a height map for the AOI; execute a building segmentation module to generate a building mask that indicates a presence of one or more buildings in the AOI; apply at least one clean mask process to the generated building mask to generate a clean mask; receive the clean mask at an instance building segmentation module; and execute the instance building segmentation module to generate at least one building footprint based on the clean mask and the received image data.

Abstract: The present disclosure relates to training one or more neural networks for vascular vessel assessment using synthetic image data for which ground-truth data is known. In certain implementations, the synthetic image data may be based in part, or derived from, clinical image data for which ground-truth data is not known or available. Neural networks trained in this manner may be used to perform one or more of vessel segmentation, decalcification, Hounsfield unit scoring, and/or estimation of a hemodynamic parameter.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a 3D building modeling module; a memory for storing program instructions; a 3D building modeling processor, coupled to the memory, and in communication with the 3D building modeling module and operative to execute program instructions to: receive a region of interest; receive an image of the region of image from a data source; generate a surface model based on the received image including one or more buildings; generate a digital height model; decompose each building into a set of shapes; apply a correction process to the set of shapes; execute a primitive classification process to each shape; execute a fitting process to each classified shape; select a best fitting model; and generate a 3D model of each building. Numerous other aspects are provided.

Abstract: The present disclosure relates to training one or more neural networks for vascular vessel assessment using synthetic image data for which ground-truth data is known. In certain implementations, the synthetic image data may be based in part, or derived from, clinical image data for which ground-truth data is not known or available. Neural networks trained in this manner may be used to perform one or more of vessel segmentation, decalcification, Hounsfield unit scoring, and/or estimation of a hemodynamic parameter.

Abstract: A method for characterizing anatomical features includes receiving scanned data and image data corresponding to a subject. The scanned data comprises sinogram data. The method further includes identifying a first region in an image of the image data corresponding to a region of interest. The method also includes determining a second region in the scanned data. The second region corresponds to the first region. The method further includes identifying a sinogram trace corresponding to the region of interest. The sinogram trace comprises sinogram data present within the second region. The method includes determining a data feature of the subject based on the sinogram trace and a deep learning network. The method also includes determining a diagnostic condition corresponding to a medical condition of the subject based on the data feature.

Abstract: A method for characterizing anatomical features includes receiving scanned data and image data corresponding to a subject. The scanned data comprises sinogram data. The method further includes identifying a first region in an image of the image data corresponding to a region of interest. The method also includes determining a second region in the scanned data. The second region corresponds to the first region. The method further includes identifying a sinogram trace corresponding to the region of interest. The sinogram trace comprises sinogram data present within the second region. The method includes determining a data feature of the subject based on the sinogram trace and a deep learning network. The method also includes determining a diagnostic condition corresponding to a medical condition of the subject based on the data feature.

Abstract: Embodiments of systems, methods and non-transitory computer readable media for imaging are presented. Preliminary image data corresponding to a first FOV of a subject at a first resolution is acquired using an imaging system including one or more radiation sources and at least one hybrid detector, specifically using at least one section of the hybrid detector having the first resolution. The target ROI is identified using the preliminary image data. Further, the subject is positioned to align the target ROI along a designated axis. Additionally, parameters associated with the sources, the hybrid detector and/or an imaging system gantry are configured for acquiring target image data at a second resolution greater than the first resolution using at least one section of the hybrid detector having the second resolution. Further, one or more images corresponding to at least the target ROI are reconstructed using the target and/or the preliminary image data.