Abstract: The present invention is directed to systems and methods for detecting objects in a radar image stream. Embodiments of the invention can receive a data stream from radar sensors and use a deep neural network to convert the received data stream into a set of semantic labels, where each semantic label corresponds to an object in the radar data stream that the deep neural network has identified. Processing units running the deep neural network may be collocated onboard an airborne vehicle along with the radar sensor(s). The processing units can be configured with powerful, high-speed graphics processing units or field-programmable gate arrays that are low in size, weight, and power requirements. Embodiments of the invention are also directed to providing innovative advances to object recognition training systems that utilize a detector and an object recognition cascade to analyze radar image streams in real time.

Abstract: Embodiments of the present invention relate to systems and methods for improving the training of machine learning systems to recognize certain objects within a given image by supplementing an existing sparse set of real-world training images with a comparatively dense set of realistic training images. Embodiments may create such a dense set of realistic training images by training a machine learning translator with a convolutional autoencoder to translate a dense set of synthetic images of an object into more realistic training images. Embodiments may also create a dense set of realistic training images by training a generative adversarial network (“GAN”) to create realistic training images from a combination of the existing sparse set of real-world training images and either Gaussian noise, translated images, or synthetic images.

Abstract: Embodiments of the present invention relate to systems and methods for improving the training of machine learning systems to recognize certain objects within a given image by supplementing an existing sparse set of real-world training images with a comparatively dense set of realistic training images. Embodiments may create such a dense set of realistic training images by training a machine learning translator with a convolutional autoencoder to translate a dense set of synthetic images of an object into more realistic training images. Embodiments may also create a dense set of realistic training images by training a generative adversarial network (“GAN”) to create realistic training images from a combination of the existing sparse set of real-world training images and either Gaussian noise, translated images, or synthetic images.

Abstract: Embodiments of the present invention relate to systems and methods for improving the training of machine learning systems to recognize certain objects within a given image by supplementing an existing sparse set of real-world training images with a comparatively dense set of realistic training images. Embodiments may create such a dense set of realistic training images by training a machine learning translator with a convolutional autoencoder to translate a dense set of synthetic images of an object into more realistic training images. Embodiments may also create a dense set of realistic training images by training a generative adversarial network (“GAN”) to create realistic training images from a combination of the existing sparse set of real-world training images and either Gaussian noise, translated images, or synthetic images.

Abstract: Embodiments of the present invention relate to systems and methods for improving the training of machine learning systems to recognize certain objects within a given image by supplementing an existing sparse set of real-world training images with a comparatively dense set of realistic training images. Embodiments may create such a dense set of realistic training images by training a machine learning translator with a convolutional autoencoder to translate a dense set of synthetic images of an object into more realistic training images. Embodiments may also create a dense set of realistic training images by training a generative adversarial network (“GAN”) to create realistic training images from a combination of the existing sparse set of real-world training images and either Gaussian noise, translated images, or synthetic images.

Abstract: Embodiments of the present invention relate to systems and methods for improving the training of machine learning systems to recognize certain objects within a given image by supplementing an existing sparse set of real-world training images with a comparatively dense set of realistic training images. Embodiments may create such a dense set of realistic training images by training a machine learning translator with a convolutional autoencoder to translate a dense set of synthetic images of an object into more realistic training images. Embodiments may also create a dense set of realistic training images by training a generative adversarial network (“GAN”) to create realistic training images from a combination of the existing sparse set of real-world training images and either Gaussian noise, translated images, or synthetic images.

Abstract: Embodiments of the present invention relate to systems and methods for improving the training of machine learning systems to recognize certain objects within a given image by supplementing an existing sparse set of real-world training images with a comparatively dense set of realistic training images. Embodiments may create such a dense set of realistic training images by training a machine learning translator with a convolutional autoencoder to translate a dense set of synthetic images of an object into more realistic training images. Embodiments may also create a dense set of realistic training images by training a generative adversarial network (“GAN”) to create realistic training images from a combination of the existing sparse set of real-world training images and either Gaussian noise, translated images, or synthetic images.

Abstract: The present invention is directed to systems and methods for detecting objects in a radar image stream. Embodiments of the invention can receive a data stream from radar sensors and use a deep neural network to convert the received data stream into a set of semantic labels, where each semantic label corresponds to an object in the radar data stream that the deep neural network has identified. Processing units running the deep neural network may be collocated onboard an airborne vehicle along with the radar sensor(s). The processing units can be configured with powerful, high-speed graphics processing units or field-programmable gate arrays that are low in size, weight, and power requirements. Embodiments of the invention are also directed to providing innovative advances to object recognition training systems that utilize a detector and an object recognition cascade to analyze radar image streams in real time.

Abstract: Embodiments of the present invention are directed to providing new systems and methods for using deep learning techniques to generate embeddings for high dimensional data objects that can both simulate prior art embedding algorithms and also provide superior performance compared to the prior art methods.

Abstract: The present invention is directed to systems and methods for detecting objects in a radar image stream. Embodiments of the invention can receive a data stream from radar sensors and use a deep neural network to convert the received data stream into a set of semantic labels, where each semantic label corresponds to an object in the radar data stream that the deep neural network has identified. Processing units running the deep neural network may be collocated onboard an airborne vehicle along with the radar sensor(s). The processing units can be configured with powerful, high-speed graphics processing units or field-programmable gate arrays that are low in size, weight, and power requirements. Embodiments of the invention are also directed to providing innovative advances to object recognition training systems that utilize a detector and an object recognition cascade to analyze radar image streams in real time.

Abstract: The present invention is directed to systems and methods for detecting objects in a radar image stream. Embodiments of the invention can receive a data stream from radar sensors and use a deep neural network to convert the received data stream into a set of semantic labels, where each semantic label corresponds to an object in the radar data stream that the deep neural network has identified. Processing units running the deep neural network may be collocated onboard an airborne vehicle along with the radar sensor(s). The processing units can be configured with powerful, high-speed graphics processing units or field-programmable gate arrays that are low in size, weight, and power requirements. Embodiments of the invention are also directed to providing innovative advances to object recognition training systems that utilize a detector and an object recognition cascade to analyze radar image streams in real time.

Abstract: A technique is provided for generating quantitative projection images from projection images. The pixels of the quantitative projection images correspond to quantitative composition estimates of two or more materials. The quantitative projection images are reconstructed to generate a quantitative volume in which each voxel value corresponds quantitatively to the two or more materials or a mixture of the two or more materials.

Abstract: A technique is provided for generating quantitative projection images from projection images. The pixels of the quantitative projection images correspond to quantitative composition estimates of two or more materials. The quantitative projection images are reconstructed to generate a quantitative volume in which each voxel value corresponds quantitatively to the two or more materials or a mixture of the two or more materials.

Abstract: A technique is provided for comparative image analysis and/or change detection using computer assisted detection and/or diagnosis (CAD) algorithms. The technique includes registering two or more images, comparing the images with one another to generate a change map, and detecting anomalies in the images based on the change map.

Abstract: Some configurations of method for reconstructing a volumetric image of an object include obtaining a tomosynthesis projection dataset of an object. The method also includes utilizing the tomosynthesis projection dataset and additional information about the object to minimize a selected energy function or functions to satisfy a selected set of constraints. Alternatively, constraints are applied to a reconstructed volumetric image in order to obtain an updated volumetric image. A 3D volume representative of the imaged object is thereby obtained in which each voxel is reconstructed and a correspondence indicated to a single one of the component material classes.

Abstract: A technique is provided for generating quantitative projection images from projection images. The pixels of the quantitative projection images correspond to quantitative composition estimates of two or more materials. The quantitative projection images are reconstructed to generate a quantitative volume in which each voxel value corresponds quantitatively to the two or more materials or a mixture of the two or more materials.

Abstract: Briefly in accordance with one embodiment, the present technique provides a multi-energy tomosynthesis imaging system. The system includes an X-ray source configured to emit X-rays from multiple locations within a limited angular range relative to an imaging volume. The imaging system also includes a digital detector with an array of detector elements to generate images in response to the emitted X-rays. The imaging system further includes a detector acquisition circuitry to acquire the images from the digital detector. The imaging system may also include a processing circuitry configured to decompose plurality of images based on energy characteristics and to reconstruct the plurality of images to generate a three-dimensional multi-energy tomosynthesis image.

Abstract: Briefly in accordance with one embodiment, the present technique provides a multi-energy tomosynthesis imaging system. The system includes an X-ray source configured to emit X-rays from multiple locations within a limited angular range relative to an imaging volume. The imaging system also includes a digital detector with an array of detector elements to generate images in response to the emitted X-rays. The imaging system further includes a detector acquisition circuitry to acquire the images from the digital detector. The imaging system may also include a processing circuitry configured to decompose plurality of images based on energy characteristics and to reconstruct the plurality of images to generate a three-dimensional multi-energy tomosynthesis image.

Abstract: Briefly in accordance with one embodiment, the present technique provides a multi-energy tomosynthesis imaging system. The system includes an X-ray source configured to emit X-rays from multiple locations within a limited angular range relative to an imaging volume. The imaging system also includes a digital detector with an array of detector elements to generate images in response to the emitted X-rays. The imaging system further includes a detector acquisition circuitry to acquire the images from the digital detector. The imaging system may also include a processing circuitry configured to decompose plurality of images based on energy characteristics and to reconstruct the plurality of images to generate a three-dimensional multi-energy tomosynthesis image.

Abstract: A method for facilitating an enhancement of a visibility of an object in an x-ray image includes generating an x-ray image including at least one object, generating an estimate of a background surrounding the at least one object, subtracting the background estimate from the x-ray image to generate an estimate of pixel intensities due the object, mapping the estimate of pixel intensities due to the object, and combining the mapped estimate of pixel intensities due to the object and the x-ray image to generate an enhanced image.