Abstract: A method and system is disclosed for acquiring image data of a subject. The image data can be collected with an imaging system having a detector able to move relative to the subject. A contrast agent can be injected into the subject and image data can be acquired with the contrast agent in various phases of the subject. A volumetric model of multiple phases can be reconstructed selected reconstruction techniques.

Abstract: A system and related methods for identifying characteristics of handbags is described. One method includes receiving one or more images of a handbag, eliminating all but select images from the one or more images of the handbag to obtain a grouping of one or more select images, the select images being those embodying a complete periphery and frontal view of the handbag. For each of the one or more select images, aligning feature-corresponding pixels with an image axis, comparing at least a portion of the one or more select images with a plurality of stored images, and determining characteristics of the handbag based on said comparing.

Abstract: A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

Abstract: A distance estimation system comprised of a laser light emitter, two image sensors, and an image processor are positioned on a baseplate such that the fields of view of the image sensors overlap and contain the projections of an emitted collimated laser beam within a predetermined range of distances. The image sensors simultaneously capture images of the laser beam projections. The images are superimposed and displacement of the laser beam projection from a first image taken by a first image sensor to a second image taken by a second image sensor is extracted by the image processor. The displacement is compared to a preconfigured table relating displacement distances with distances from the baseplate to projection surfaces to find an estimated distance of the baseplate from the projection surface at the time that the images were captured.

Abstract: The method to determine tampering of a security label (102) comprises, associating at least a portion of a first pattern to an external reference (110), wherein a first layer (202) of the security label (102) comprises the first pattern. Further, a second pattern (206) defined in a second layer (204) is used to change the contour of the portion of the first pattern, when the security label (102) is at least partially disengaged from a surface. Subsequently, when there is change in contour of the portion of the first pattern, the portion of the first pattern is disassociated from an external reference (110). Further, the portion of the first pattern and the external reference (110) are scanned and finally tampering of the security label (102) is determined based on the association between the portion of the first pattern and the external reference (110).

Abstract: A method and system of training a machine learning neural network (MLNN) in anatomical degenerative conditions in accordance with anatomical dynamics. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, a first set of mmWave radar point cloud data representing a first gait characteristic of a subject in motion, comprising an arm swing velocity, receiving, in a second layer, a second set of mmWave radar point cloud data representing a second gait characteristic comprising a measure of dynamic postural stability, the input layers being interconnected with an output layer of the MLNN via an intermediate layer, and training a MLNN classifier in accordance with a classification that increases a correlation between a degenerative condition of the subject as generated at the output layer and the sets of mmWave point cloud data.

Abstract: A system and method for determining kinetic parameters associated with a kinetic model of an imaging agent in a liver is provided. An image reconstruction device can receive radiotracer activities corresponding to a predetermined time period. For example, these radiotracer activities can include PET scan data corresponding to a number of time frames. The radiotracer activities can be used to determine a liver time activity curve and a circulatory input function. The liver time activity curve and circulatory input function can be used along with a kinetic model of the liver to produce kinetic parameters. These kinetic parameters can be used to determine hepatic scores, such as a hepatic steatosis score, a hepatic inflammation score, and a cirrhosis score. These scores are indicative of diseases of the liver, including nonalcoholic fatty liver disease, nonalcoholic steatohepatitis, and hepatic fibrosis.

Abstract: Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.

Abstract: This disclosure provides a system and a method. The method may include: determining a processing instruction; acquiring image data based on the processing instruction; determining a configuration file based on the image data, in which the configuration file may be configured to guide implementation of the processing instruction; constructing a data processing pipeline based on the configuration file; executing the data processing process based on the data processing pipeline, in which the data processing process may be generated based on the data processing pipeline; generating a processing result of the image data based on the executed data processing process; and storing the processing result of the image data in a first storage space.

Abstract: Systems and methods are disclosed for determining a patient risk assessment or treatment plan based on emboli dislodgement and destination. One method includes receiving a patient-specific anatomic model generated from patient-specific imaging of at least a portion of a patient's vasculature; determining or receiving a location of interest in the patient-specific anatomic model of the patient's vasculature; using a computing processor for calculating blood flow through the patient-specific anatomic model to determine blood flow characteristics through at least the portion of the patient's vasculature of the patient-specific anatomic model downstream from the location of interest; and using a computing processor for particle tracking through the simulated blood flow to determine a destination probability of an embolus originating from the location of interest in the patient-specific anatomic model, based on the determined blood flow characteristics.

Abstract: The disclosure relates to methods and systems for determining muscle fascicle fracturing in a meat sample. In one embodiment, the methods and systems disclosed herein use automated methods to determine muscle fascicle fracturing. Methods and systems disclosed herein comprise imaging a meat sample with an imaging device to obtain one or more images; and using an iteratively trained training detection model to determine an objective classification, including the presence or absence of muscle fascicle fracturing in the images of the meat sample.

Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing matching fonts by utilizing a glyph-based machine learning model. For example, the disclosed systems can generate a glyph image by arranging glyphs from a digital document according to an ordering rule. The disclosed systems can further identify target fonts as fonts that include the glyphs within the glyph image. The disclosed systems can further generate target glyph images by arranging glyphs of the target fonts according to the ordering rule. Based on the glyph image and the target glyph images, the disclosed systems can utilize a glyph-based machine learning model to generate and compare glyph image feature vectors. By comparing a glyph image feature vector with a target glyph image feature vector, the font matching system can identify one or more matching glyphs.

Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.

Abstract: An image sensor is positioned such that a field-of-view of the image sensor encompasses at least a portion of a rack storing items. The image sensor generates images of the items stored on the rack. Over a period of time, a tracking subsystem tracks a pixel position of the wrist of a person interacting with items stored on the rack, receives image frames of the angled-view images. The tracking subsystem determines whether an item was interacted with by a person and, if so, the identified item is assigned to the person.

Abstract: Disclosed herein is a method of treating a subject having arterial stenosis. The method comprises: (a) providing a plurality of image frames of an artery of the subject taken in sequence; (b) in a plurality of cross-sections of the artery, determining a maximum diameter and a minimum diameter of each of the plurality of cross-sections of the artery among the plurality of image frames of the step (a); (c) calculating an average vasodilation ratio of the artery base on the maximum diameter and the minimum diameter determined in the step (b); and (d) treating the subject based on the average vasodilation ratio calculated in the step (c), by implanting a stent to the subject when the average vasodilation ratio is equal to or greater than 0.2; or administering to the subject an effective amount of a vasodilator when the average vasodilation ratio is less than 0.2.

Abstract: A computer system obtains, in electronic format, a training dataset. The training dataset comprises a plurality of training images from a plurality of agricultural plots. Each training image is from a respective agricultural plot in the plurality of agricultural plots and comprises at least one identified fruit. The computer system determines, for each respective fruit in each respective training image in the plurality of training images, a corresponding contour. The computer system trains an untrained or partially trained computational model using at least the corresponding contour for each respective fruit in each respective training image in the plurality of training images, thereby obtaining a first trained computational model that is configured to identify fruit in agricultural plot images.

Abstract: A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

Abstract: A method includes generating a longitudinal lesion model by performing a training step on a plurality of sets of longitudinal data. Dates of medical scans of different ones of the plurality of sets of longitudinal data have relative time differences corresponding to different time spans, and each set of the plurality of sets of longitudinal data corresponds to one of a plurality of different patients. The longitudinal lesion model is utilized to perform an inference step on a received medical scan to generate, for a lesion detected in the received medical scan, a plurality of lesion change prediction data for a corresponding plurality of different projected time spans ending after the current date. At least one of the plurality of lesion change prediction data is transmitted for display.

Abstract: A system uses data captured by vehicle-mounted sensors to generate a view of a ground surface. The system does this by receiving digital image frames and associating a location and pose of the vehicle that captured the image with each digital image frame. The system will access a three dimensional (3D) ground surface estimation model of the ground surface, select a region of interest (ROI) of the ground surface, and select a vehicle pose. The system will identify digital image frames that are associated with the pose and also with a location that corresponds to the ROI. The system will generate a visual representation of the ground surface in the ROI by projecting ground data for the ROI from the ground surface estimation model to normalized 2D images that are created from the digital image frames. The system will save the visual representation to a two-dimensional grid.