Abstract: A target tracking method and apparatus is provided. The target tracking apparatus includes a memory configured to store a neural network, and a processor configured to extract feature information of each of a target included in a target region in a first input image, a background included in the target region, and a searching region in a second input image, using the neural network, obtain similarity information of the target and the searching region and similarity information of the background and the searching region based on the extracted feature information, obtain a score matrix including activated feature values based on the obtained similarity information, and estimate a position of the target in the searching region from the score matrix.

Abstract: Correcting motion-based inaccuracy in point cloud data generated by one or more sensors carried by a scanning platform, and associated systems and methods are disclosed herein. A representative method includes associating a motion model with a target object of the point cloud, estimating adjusting factors based on the motion model, and adjusting scanning points in the point cloud using the adjusting factors.

Abstract: A CPU acquires a distance image which indicates a distance to an imaging target and is captured by a TOF camera that captures the distance image using, as the imaging target, a mammography apparatus which is an abnormality determination target. In addition, the CPU acquires reference distance image as reference distance information related to a reference value of a distance between the abnormality determination target in a reference state and the TOF camera. Further, the CPU performs determination on an abnormality appearing in an outward appearance of the abnormality determination target on the basis of the distance image and the reference distance image.

Abstract: Systems and methods for determining a current state of at least one traffic light (“TL”) for use in controlling an autonomous vehicle are described herein. Implementations can generate a corresponding numerical measure, for each of multiple candidate states of the TL, based on processing an image capturing a region that is predicted to include the TL using machine learning classifier(s). Implementations further generate a corresponding final measure, for each of the candidate states, based on processing each of the corresponding numerical measures using TL model(s), select one of the candidate states as a current state of the TL based on the final measures, and control the autonomous vehicle based on the current state of the TL. Notably, each of the corresponding final measures can be generated as a function of the corresponding numerical measures of the candidate states, and the corresponding numerical measures of the other candidate states.

Abstract: A method for acquiring annotated data with the aid of surgical microscopy systems comprises obtaining desired criteria which are intended to be satisfied by desired data to be annotated, and storing the set of desired criteria in a plurality of surgical microscopy systems. In each surgical microscopy system, images are then recorded and current criteria which correspond to the recorded images are determined. The current criteria are compared with the desired criteria. If the desired criteria sufficiently correspond to the current criteria, a confirmation is requested from a user as to whether said user would like to annotate data. If the user provides the confirmation, annotations for images are received from the user and stored together with the images.

Abstract: A computing device generates a call in a programming language of a computing application requesting a feature of videos stored in a media repository. A query system receive the call and determines a command associated with obtaining the feature requested by the call. The determined command corresponds to an image analysis to perform on at least a portion of the stored videos. The query system determines, based at least in part on the determined command, an artificial intelligence model to execute on at least the portion of the stored videos. The query system determine, by executing the determined artificial intelligence model, a model output that includes the requested feature. The query system provides, in the programming language of the computing application, an indication of the requested feature.

Abstract: System, methods, and other embodiments described herein relate to training a model to stylize low-light images for improved perception. In one embodiment, a method includes encoding, by a style model, an input image to identify first content information. The method also includes decoding, by the style model, the first content information into an albedo component and a shading component. The method also includes generating, by the style model, a synthetic image using the albedo component and the shading component. The method also includes training the style model according to computed losses between the input image and the synthetic image.

Abstract: In a measurement of a microscope image, a measurement can be conducted with high accuracy when measuring a measuring object including a step having a depth larger than a depth of focus or comparing patterns at different positions along the optical axis of a microscope. A microscope image measuring device includes: a microscope for obtaining a magnified image of a surface of a measuring object by irradiating the surface with white incident light; a spectral camera for obtaining a spectral image of the magnified image; and an image processing part for extracting the spectral image at each wavelength and performs an image measuring process. The microscope forms an image of a different focal position at each wavelength on the imaging surface of the spectral camera, and the image processing part extracts a spectral image with a wavelength where a measuring point has the highest contrast, and performs edge detection.

Abstract: Computer-implemented method and system automatically detects an out-of-view CT scan by receiving a voxel density file, determining a threshold iso-value of density between air density and a material density of one or more scanned objects in the voxel density file, and evaluating, using the threshold iso-value of density, one or more horizontal slices of the voxel density file to determine whether at least a portion of the one or more scanned objects is out of view.

Abstract: Many embodiments of the invention include systems and methods for evaluating motion from a video, the method includes identifying a target individual in a set of one or more frames in a video, analyzing the set of frames to determine a set of pose parameters, generating a 3D body mesh based on the pose parameters, identifying joint positions for the target individual in the set of frames based on the generated 3D body mesh, predicting a motion evaluation score based on the identified join positions, providing an output based on the motion evaluation score.

Abstract: A system for monitoring organ health during treatment for cancer and the like makes use of physiological imaging of the kind used for treatment monitoring and organ-specific processing to provide a comprehensive assessment of treatment side-effects.

Abstract: Methods and systems for displaying an image of a medical procedure (e.g., an intraoperative image) with additional information (e.g., data) that can augment the image of the medical procedure are provided. Augmenting the image can include overlaying data from a first image to a second image. Overlaying the data can involve determining, for a point or multiple points in the first image, a matching location or multiple matching locations in a second image. The first image and the second image can be of a patient. Determining the matching location can involve using a rotation and scale invariant geometrical relationship. The matching locations can be used as the basis for the overlay.

Abstract: An image enhancement method includes: acquiring an image in a YUV format (110); performing N max layers of wavelet decomposition on a brightness component and chrominance components respectively (120); starting from the (N max)th layer, performing wavelet reconstruction on the low frequency sub-bands of the chrominance components of each layer on which edge preserving filtering has been performed according to the low frequency sub-band of the brightness component of the corresponding layer, and the high frequency sub-bands of the chrominance components continuously towards an upper layer until an image of an original size is obtained (130); performing, according to the brightness component, edge preserving filtering on the chrominance components that have been subjected to the wavelet reconstruction (140); integrating the chrominance components that have been subjected to the edge preserving filtering and the brightness component (150).

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

Abstract: Various methods and systems are provided for automatically classifying a plurality of image slices using body region bounding boxes identified from a localizer image. In one embodiment, a localizer image may be mapped to a plurality of bounding boxes, corresponding to a plurality of body regions, using a trained machine learning model. Coordinates of the plurality of bounding boxes may be used to determine body region boundaries, such that the body regions are non-intersecting and coherent. The body regions identified in the localizer image may then be correlated to image slice ranges, and image slices within each image slice range may be labeled as belonging to the corresponding body region.

Abstract: Methods and systems are provided for detecting focal lesions in multi-phase or multi-sequence medical imaging studies of medical images. One system includes memory for storing medical images and an electronic processor. The electronic processor is configured to: detect native candidate lesions for first phase computer tomography (CT) scans, detect candidate lesions for second phase CT scans, and project the candidate lesions detected for the first phase CT scans and/or the second phase CT scans on a same common domain. Once the candidate lesions are registered together, the electronic processor performs a matching algorithm for each of the native candidate lesions of the first phase CT scans with the registered candidate lesions from the second phase CT scans to determine presence and contours of valid lesions for the medical images, and to discard candidate lesions that are not acceptable.

Abstract: An apparatus includes a memory and a processor. The memory stores a dictionary and a machine learning algorithm trained to classify text. The processor receives an image of a page, converts the image into a set of text, and identifies a plurality of tokens within the text. Each token includes one or more contiguous characters that are both preceded and followed by whitespace within the text. The processor identifies invalid tokens by removing tokens of the plurality of tokens that correspond to words of the dictionary. The processor calculates, based on a ratio of a total number of valid tokens to a total number of tokens, a score. In response to determining that the score is greater than a threshold, the processor applies the machine learning algorithm to classify the text into a category and stores the image and/or text in a database according to the category.

Abstract: An inverted microscope system provided with a transillumination subsystem that illuminates a sample includes: an eyepiece lens; an objective; a tube lens; a projection device, disposed below the objective, that projects a projected image based on projected image data onto an image plane where an optical image is formed; a first modulation element included in the transillumination subsystem; a second modulation element disposed between the objective and the tube lens; and a processor and a memory, the processor being configured to perform the following steps: generating an analysis result that specifies a candidate cell that is a reproductive cell suitable for fertilization, based on at least the digital image data acquired by the imaging device; and generating the projected image data based on the generated analysis result, wherein the projected image includes a first assisting image that specifies the candidate cell as an assisting image that assists with micro-insemination.

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

Abstract: There is shown and described a deep learning based system and method for skin diagnostics as well as testing metrics that show that such a deep learning based system outperforms human experts on the task of apparent skin diagnostics. Also shown and described is a system and method of monitoring a skin treatment regime using a deep learning based system and method for skin diagnostics.