Abstract: A radiographic imaging apparatus (100) is configured to generate movement maps (30) of pixels (21) belonging to a first image (11) based on the first image (11) and a second image (12) captured at different times, to move a pixel (21) of the first image (11) based on a smoothed movement map (30a) in which high-frequency components of the movement maps (30) have been suppressed in a spatial direction and generate a deformed image (11a), and to combine the deformed image (11a) and the second image (12).

Abstract: An imaging method (100) includes: acquiring first training images of one or more imaging subjects using a first image acquisition device (12); acquiring second training images of the same one or more imaging subjects as the first training images using a second image acquisition device (14) of the same imaging modality as the first imaging device; and training a neural network (NN) (16) to transform the first training images into transformed first training images having a minimized value of a difference metric comparing the transformed first training images and the second training images.

Abstract: An information processing device includes circuitry. The circuitry calculates a matching degree between two images of an image pair. The matching degree of the image pair is one of a plurality of matching degrees of a plurality of image pairs. The circuitry generates information on sameness between two image groups of an image group pair based on the plurality of matching degrees of the plurality of image pairs. The image group pair includes the plurality of image pairs.

Abstract: A flexible computer architecture for performing digital image analysis is described herein. In some examples, the computer architecture can include a distributed messaging platform (DMP) for receiving images from cameras and storing the images in a first queue. The computer architecture can also include a first container for receiving the images from the first queue, applying an image analysis model to the images, and transmitting the image analysis result to the DMP for storage in a second queue. Additionally, the computer architecture can include a second container for receiving the image analysis result from the second queue, performing a post-processing operation on the image analysis result, and transmitting the post-processing result to the DMP for storage in a third queue. The computer architecture can further include an output container for receiving the post-processing result from the third queue and generating an alert notification based on the post-processing result.

Abstract: An electronic device includes a depth sensor and an inertial measurement unit. The electronic device detects a presence of the user of the electronic device by analyzing a combination of inertial sensor signals from the inertial measurement unit and depth sensor signals from the depth sensor.

Abstract: Provided herein are systems, methods and computer readable media for identifying content to represent web pages and creating a representative image from the content. An example method may include retrieving a web document using a uniform resource locator (URL) contained in a dequeued work item, determining, from the web document, candidate images for creation of the representative image including extracting image references, wherein the image references are extracted by identifying image tags with source attributes, values of which are URLs locating images, filtering the URLs using a blacklist of expressions designed to match the URLs of images comprising one or more predefined undesirable characteristics, and retrieving the images which do not match any of the expressions using an HTTP client, and creating the representative image, comprising at least modifying a chosen image selected from among the candidate images.

Abstract: Methods, systems, devices and apparatuses for generating a high-quality MRI image from under-sampled or corrupted data The image reconstruction system includes a memory. The memory is configured to store multiple samples of biological, physiological, neurological or anatomical data that has missing or corrupted k-space data and a deep learning model or neural network. The image reconstruction system includes a processor coupled to the memory. The processor is configured to obtain the multiple samples. The processor is configured to determine the missing or corrupted k-space data using the multiple samples and the deep learning model or neural network. The processor is configured to reconstruct an MRI image using the determined missing or corrupted k-space data and the multiple samples.

Abstract: An image processing system or electronic device may implement processing circuitry. The processing circuitry may receive an image, such as financial document image. The processing circuitry may determine a character count for the financial document image or particular portions of the financial document image without recognizing any particular character in the financial document image. In that regard, the processing circuitry may determine a top left score for pixels in the financial document, the top left score indicating or representing a likelihood that a particular pixel corresponds to a top left corner of a text character. The processing circuitry may also determine top right score for image pixels. Then, the processing circuitry may identify one or more text chunks using the top left and top rights scores for pixels in the financial document image. The processing circuitry may determine a character count for the identified text chunks.

Abstract: Embodiments provide slide navigation technology that addresses challenges in digital pathology of navigating and viewing high resolution slide images. Example systems comprise a virtual slide stage (VSS) having at least one sensor that detects user movement of a target placed on the VSS, and an input component, coupled to the VSS, which provides quick function movement control of the target via quick functions. The systems also comprise a connector component that connects the VSS to a user device and transmits output from the at least one sensor and input component to the user device. The systems further comprise a computer processor, in communication with the VSS, which processes the output using a computational model to generate data representing movement profiles of the target. The computer processor executes a software component, causing the output, translated based on the movement profiles, to be relayed via a viewing application on the user device.

Abstract: A method for PET image reconstruction acquires PET data by a PET scanner; reconstructs from the acquired PET data a seed PET image; builds a feature space from the seed PET image and anatomical images co-registered with the seed PET image; performs a penalized maximum-likelihood reconstruction of a PET image from the seed PET image and the feature space using a penalty function that is calculated based on the differences between each voxel and its neighbors both on the PET image and in the feature space regardless of their location in the image.

Abstract: A device may receive images of an object and information identifying the object, process the images using an artificial intelligence technique to identify parts of the object that are depicted in the images, and receive information identifying a location of damage on the object and information regarding the damage on the object. The device may process the information identifying the location of damage to identify a damaged part of the object, identify images depicting the damaged part, and identify, in the images, a location of the damaged part. The device may generate a first content item for display at the location of the damaged part in the images and generate a second content item for display with the images based on user interaction with the first content item, where the second content item includes information based on the information regarding the damage on the object.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

Abstract: The present disclosure provides a tissue density analysis system. The system includes an acquisition module configured to obtain image data and tissue density distribution data; a display module configured to display the obtained tissue density distribution data in one or more charts; a processing module configured to adjust the tissue density distribution data displayed in the one or more charts; and a storage module configured to store the image data, the tissue density distribution data and an instruction.

Abstract: A mechanism is described for facilitating the transfer of features learned by a context independent pre-trained deep neural network to a context dependent neural network. The mechanism includes extracting a feature learned by a first deep neural network (DNN) model via the framework, wherein the first DNN model is a pre-trained DNN model for computer vision to enable context-independent classification of an object within an input video frame and training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature, the second DNN model an update of the first DNN model, wherein training the second DNN model includes training the second DNN model based on a dataset including context-dependent data.

Abstract: A device for assembling at least two shots of a scene acquired by at least one sensor includes a memory and processing circuitry. The processing circuitry is configured to save, in the memory, a first data set contained in a first signal generated by each pixel of the sensor and indicative of a first shot of the scene, and a second data set contained in a second signal generated by each pixel of the sensor and indicative of a second shot of the scene. The processing circuitry is further configured to assemble the first and second shots on the basis of the content of the first and second data sets of a plurality of pixels in order to form a resulting scene.

Abstract: An information processing apparatus monitors a plurality of persons on a premises by one or more image sensors installed on the premises. The information processing apparatus includes a controller configured to determine a tendency of behavior of at least one person of the plurality of persons according to a length of a blank time, the blank time being a time during which the at least one person does not appear in an image captured by the one or more image sensors.

Abstract: The present disclosure generally relates to a device and a method of decoding a color picture from a bitstream.

Abstract: Aspects of the technology described herein relate to detecting degrade ultrasound imaging frame rate. Some embodiments include receiving ultrasound data from the ultrasound device, generating ultrasound images from the ultrasound data, taking one or more measurements of ultrasound imaging frame rate based on the ultrasound images, comparing the one or more measurements of ultrasound imaging frame rate to an reference ultrasound imaging frame rate value, and based on a result of comparing the one or more measurements of ultrasound imaging frame rate to the reference ultrasound imaging frame rate value, providing a notification and/or disabling an ultrasound imaging preset and/or an ultrasound imaging mode with which the ultrasound device was configured.

Abstract: Systems and methods for automatically excluding artifacts from an analysis of a biological specimen image are disclosed. An exemplary method includes obtaining an immunohistochemistry (IHC) image and a control image, determining whether the control image includes one or more artifacts, upon a determination that the control image includes one or more artifacts, identifying one or more artifact regions within the IHC image by mapping the one or more artifacts from the control image to the IHC image, and performing image analysis of the IHC image where any identified artifact regions are excluded from the image analysis.

Abstract: An apparatus and method for encoding objects in a camera-captured image with a deep neural network pipeline including multiple convolutional neural networks or convolutional layers. After identifying at least a portion of the camera-capture image, a first convolutional layer is applied to the at least the portion of the camera-captured image and multiple subregion representations are pooled from the output of the first convolutional layer. One or more additional convolutions are performed. At least one deconvolution is performed and concatenated with the output of one or more convolutions. One or more final convolutions are performed. The at least the portion of the camera-captured image is classified as an object category in response to an output of the one or more final convolutions.