Abstract: Example devices, systems, and techniques predict renal denervation efficacy for reducing hypertension in a patient based on pulse information. For example, a system may include processing circuitry configured to obtain pulse information representative of pulses from both wrists of a patient, obtain a plurality of values representative of respective patient metrics for the patient, and apply the pulse information and the plurality of values to a deep learning model trained to represent a relationship of the pulse information and the patient metrics to an efficacy of renal denervation in reducing hypertension. In some examples, responsive to applying the pulse information and the plurality of values to the deep learning model, the processing circuitry obtains, from the deep learning model, a score indicative of renal denervation efficacy in reducing hypertension for the patient, and generates a graphical user interface comprising a graphical representation of the score for the patient.

Abstract: An image processing method includes determining a format of first image data; determining a first interface mode that is currently used, where the first interface mode is determined when the processing device establishes physical connections with a display device, and the format of the first image data format is not supported by the first interface mode; and processing the first image data based on the first interface mode to generate second image data, where a format of the second image data is supported by the first interface mode. Image data will be processed when an image format of the image data is not supported by the first interface mode.

Abstract: Provided are methods for track segment cleaning of tracked objects using neural networks, which can include detecting a first track segment and a second track segment. The method includes applying a machine learning model trained to determine if the first track segment and second track segment capture real objects and if the first track segment and the second track segment are representative of an identical object exterior to a vehicle. The method further includes combining the first track segment and the second track segment to form a single track segment having a single trajectory in response to the first track segment and the second track segment being determined to be representative of the identical object. Systems and computer program products are also provided.

Abstract: A system and method for classifying a structure or material in an image of a subject. The system comprises: a segmenter configured to segment an image into one or more segmentations that correspond to respective structures or materials in the image, and to generate from the segmentations one or more segmentation maps of the image (each of the segmentation maps representing the image) including categorizations of pixels or voxels of the segmentation maps assigned from one or more respective predefined sets of categories; a classifier that implements a classification machine learning model configured to generate, based on the segmentations maps, one or more classifications and to assign to the classifications respective scores indicative of a likelihood that the structure or material, or the subject, falls into the respective classifications; and an output for outputting a result indicative of the classifications and scores.

Abstract: An apparatus for determining defective hair follicles includes an image acquiring unit for acquiring an image of a follicle and a hair for each follicle separated from a scalp of an alopecic patient in an incisional hair transplant or each follicle directly extracted from an alopecic patient in a non-incisional hair transplant, an image processing unit for extracting an outline pattern of the image of the follicle and the hair by performing a contour detection process or an edge detection process on the image, a follicle shape database for storing hair pixel patterns related to various shapes of hairs and follicle pixel patterns related to various shapes of follicles, and a follicle determining unit for determining whether a follicle is normal follicle or defective follicle by comparing the outline pattern of the image with the hair pixel patterns and follicle pixel patterns stored in the follicle shape database.

Abstract: The technology relates to camera systems for vehicles having an autonomous driving mode. An example system includes a first camera mounted on a vehicle in order to capture images of the vehicle's environment. The first camera has a first exposure time and being without an ND filter. The system also includes a second camera mounted on the vehicle in order to capture images of the vehicle's environment and having an ND filter. The system also includes one or more processors configured to capture images using the first camera and the first exposure time, capture images using the second camera and the second exposure time, use the images captured using the second camera to identify illuminated objects, use the images captured using the first camera to identify the locations of objects, and use the identified illuminated objects and identified locations of objects to control the vehicle in an autonomous driving mode.

Abstract: Provided herein is a system and method that acquires data and determines a driving action based on the data. The system comprises a sensor, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform, determining data of interest comprising an object, feature, or region of interest, determining whether a sharpness of the data of interest exceeds a threshold, in response to determining that the sharpness does not exceed a threshold, operating the sensor to increase the sharpness of the data of interest until the sharpness exceeds the threshold, in response to the sharpness exceeding the threshold, determining a driving action of a vehicle based on the data of interest, and performing the driving action.

Abstract: Systems and methods of providing guidance to assist eVTOL aerial vehicles in performing landing and takeoff operations at landing locations in GPS-denied environments are disclosed. An exemplary system includes an aerial vehicle comprising a camera configured to generate images based on information transmitted by a plurality of light sources located adjacent a landing surface for the aerial vehicle and a controller circuit configured to receive the generated images and determine a position and an orientation of the aerial vehicle based on the received images, wherein the light sources are arranged in a predetermined pattern on the landing surface, and wherein a characteristic of light emitted from each of the light sources is modulated with respect to time.

Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

Abstract: Using deep learning, imaging harmonization among images produced with differing PET tracers is achieved. A method may include providing an original PET image of the brain using an original PET tracer, providing a target PET tracer, and converting, using a deep learning neural network, the original PET image into a target PET image simulating the image that would be obtained had the target PET tracer been used.

Abstract: A system for transmitting color and depth information, comprising: an image capturing device capturing a full color image frame of a scene; a depth capturing device capturing a grayscale depth image frame of the scene; a target identifying module for identifying a target in the scene; a bounding box generator configured for generating a bounding box encapsulating the target and for creating bound grayscale depth images and bound full color images; a depth colorizer converting the bound grayscale depth images into a bound full color depth image by linking and normalizing the depth dimension of the bounding box to a transferred color gamut by implementing a transfer function; an image merger that merges the bound full color images and bound full color depth images together into bound merged full color images; and an image encoder compressing the bound merged full color images into an image bitstring.

Abstract: Methods, apparatus and systems for recognizing sign language movements using multiple input and output modalities. One example method includes capturing a movement associated with the sign language using a set of visual sensing devices, the set of visual sensing devices comprising multiple apertures oriented with respect to the subject to receive optical signals corresponding to the movement from multiple angles, generating digital information corresponding to the movement based on the optical signals from the multiple angles, collecting depth information corresponding to the movement in one or more planes perpendicular to an image plane captured by the set of visual sensing devices, producing a reduced set of digital information by removing at least some of the digital information based on the depth information, generating a composite digital representation by aligning at least a portion of the reduced set of digital information, and recognizing the movement based on the composite digital representation.

Abstract: An imaging probe comprises a camera or endoscope with an external detector array, in which the probe is sized and shaped for surgical placement in an eye to image the eye from an interior of the eye during treatment. The imaging probe and a treatment probe can be coupled together with a fastener or contained within a housing. The imaging probe and the treatment probe can be sized and shaped to enter the eye through an incision in the cornea and image one or more of the ciliary body band or the scleral spur. The treatment probe may comprise a treatment optical fiber or a surgical placement device to deliver an implant. A processor coupled to the detector can be configured with instructions to identify a location of one or more of the ciliary body band, the scleral spur, Schwalbe's line, or Schlemm's canal from the image.

Abstract: There is provided a computer implemented method of image processing for detection of damage on a vehicle, comprising: accessing a plurality of time-spaced image sequences depicting a region of a vehicle, captured by a plurality of image sensors positioned at a plurality of different views, identifying a plurality of candidate regions of damage in the plurality of time-spaced image sequences, performing a spatiotemporal correlation between the plurality of time-spaced image sequences, identifying redundancy in the plurality of candidate regions of damage corresponding to a common physical location of the vehicle denoting a single physical damage region, and providing an indication of the common physical location of the vehicle corresponding to the single physical damage region.

Abstract: A method of pre-operatively developing a reverse shoulder arthroplasty plan can include receiving an image of a patient shoulder comprising a humerus and a glenoid. The image can be segmented to develop a 3D shoulder model. Virtual surgery can be performed on the 3D shoulder model to generate a modified shoulder model. The virtual surgery can include resecting and reaming a virtual humerus of the 3D shoulder model, and reaming a virtual glenoid of the 3D shoulder model. Selection of a humeral implant and selection of a glenoid implant can be received. A virtual representation of the humeral implant can be implanted on the virtual humerus and a virtual representation of the glenoid implant on the virtual glenoid to virtually update the modified shoulder model. A range of motion of the patient shoulder can be determined and a reverse shoulder arthroplasty can be finalized based on the range of motion.

Abstract: An image processing apparatus for evaluating cardiac images and a ventricular status identification method are provided. In the method, a region of interest (ROI) is determined from multiple target images, a variation in grayscale values of multiple pixels in the ROIs of each target image is determined, and one or more representative images are obtained according to the variation in the grayscale values. The target image is related to the pixels within an endocardial contour of a left ventricle. A boundary of the ROI is approximately located at two sides of a bottom of the endocardial contour. The ROI corresponds to a mitral valve. The variation in the grayscale values is related to a motion of the mitral valve. The representative image is for evaluating a status of the left ventricle.

Abstract: An object recognition method includes generating a cluster at a fixed period by clustering distance measurement points acquired from an object detection sensor, mapping the cluster to an image captured by an imaging means, setting a region on the image to which the cluster is mapped as a processing region, extracting a feature point from the image in the set processing region, calculating a movement speed of the extracted feature point, and determining whether clusters at different time points are clusters of an identical object based on a difference between positions of two feature points corresponding to the clusters at the different time points and a difference between movement speeds of the two feature points corresponding to the clusters at the different time points.

Abstract: A system and method for determining the locations and types of objects in a plurality of videos. The method comprises pairing each video with one or more sentences describing the activity or activities in which those objects participate in the associated video, wherein no use is made of a pretrained object detector. The object locations are specified as rectangles, the object types are specified as nouns, and sentences describe the relative positions and motions of the objects in the videos referred to by the nouns in the sentences. The relative positions and motions of the objects in the video are described by a conjunction of predicates constructed to represent the activity described by the sentences associated with the videos.

Abstract: A method for medical imaging may include obtaining a plurality of successive images of a region of interest (ROI) including at least a portion of an object's heart. The plurality of successive images may be based on imaging data acquired from the ROI by a scanner without electrocardiography (ECG) gating. The plurality of successive images may be related to one or more cardiac cycles of the object's heart. The method may also include automatically determining, in the plurality of successive images, target images that correspond to at least one of the one or more cardiac cycles of the object's heart.

Abstract: Methods and apparatus for providing attribute-based search of media assets such as video and audio assets, and associated augmented reality functions including dynamic provision of relevant secondary content relating to the identified attribute(s). In one embodiment, media or content assets are ingested into a processing system and processed according to one or more attribute detection, identification, and characterization algorithms. Attributes detected may include for example the presence of tangible items such as clothing or chattels, particular persons such as celebrities, and/or certain contexts such as sporting activities and musical performances, as rendered within the media asset. In one implementation, the characterized assets are provided a unique ID, and stored so as to permit cross-correlation based on, e.g., the identified and characterized attributes. Secondary content (e.g.