Abstract: Techniques described herein may involve audio settings based on an environment. An example implementation may involve a playback device playing back first audio content and during playback of at least a portion of the first audio content, detecting, via the at least one microphone, an audio signal. At least a portion of the detected audio signal may include a reflection of the first audio content played back by the playback device. The playback device may determine an equalization setting based on at least the determined one or more reflection characteristics and apply the determined equalization setting during playback of second audio content.

Abstract: Systems and methods for electromagnetic (EM) distortion detection and compensation are disclosed. In one aspect, the system includes an instrument, the system configured to: determine a reference position of the distal end of the instrument at a first time based on EM location data, determine that the distal end of the instrument at a second time is static, and determine that the EM location data at the second time is indicative of a position of the distal end of the instrument having changed from the reference position by greater than a threshold distance. The system is further configured to: determine a current offset based on the distance between the position at the second time and the reference position at the first time, and determine a compensated position of the distal end of the instrument based on the EM location data and the current offset.

Abstract: A method for optimizing a knee arthroplasty surgical procedure includes receiving pre-operative data comprising (i) anatomical measurements of the patient, (ii) soft tissue measurements of the patient's anatomy, and (iii) implant parameters identifying an implant to be used in the knee arthroplasty surgical procedure. An equation set is selected from a plurality of pre-generated equation sets based on the pre-operative data. During the knee arthroplasty surgical procedure, patient-specific kinetic and kinematic response values are generated and displayed using an optimization process. The optimization process includes collecting intraoperative data from one or more surgical tools of a computer-assisted surgical system, and using the intraoperative data and the pre-operative data to solve the equation set, thereby yielding the patient-specific kinetic and kinematic response values. A visualization is then provided of the patient-specific kinetic and kinematic response values on the displays.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing scale-diverse segmentation neural networks to analyze digital images at different scales and identify different target objects portrayed in the digital images. For example, in one or more embodiments, the disclosed systems analyze a digital image and corresponding user indicators (e.g., foreground indicators, background indicators, edge indicators, boundary region indicators, and/or voice indicators) at different scales utilizing a scale-diverse segmentation neural network. In particular, the disclosed systems can utilize the scale-diverse segmentation neural network to generate a plurality of semantically meaningful object segmentation outputs. Furthermore, the disclosed systems can provide the plurality of object segmentation outputs for display and selection to improve the efficiency and accuracy of identifying target objects and modifying the digital image.

Abstract: Methods for visualizing treatment outcomes are provided. In some embodiments, a method includes obtaining a first 2D image of a patient's teeth from a mobile device, and obtaining an arch model representing an outcome for the patient's teeth after orthodontic treatment. The method can include matching the patient's teeth in the first 2D image to corresponding teeth in the arch model, using a parametric model. The method can also include determining a target position for the patient's teeth, based on a position of the matched corresponding teeth in the arch model. The method can further include generating a second 2D image depicting the patient's teeth in the target position.

Abstract: A method for monitoring the positioning of the teeth including production of a three-dimensional digital initial reference model of the arches of the patient and, for each tooth, definition, from the initial reference model, of a three-dimensional digital reference tooth model; acquisition of updated image of at least one two-dimensional image of the arches in actual acquisition conditions; analysis of each updated image and production, for each updated image, of an updated map; optionally, determination, for each updated image, of rough virtual acquisition conditions approximating the actual acquisition conditions; searching, for each updated image, for a final reference model corresponding to the positioning of the teeth during the acquisition of the updated image, for each tooth model, comparison of the positionings of the tooth model in the initial reference model and in the reference model obtained at the end of the preceding steps to determine the movement of the teeth.

Abstract: Provided are methods, systems, and devices for generating semantic objects and an output based on the detection or recognition of the state of an environment that includes objects. State data, based in part on sensor output, can be received from one or more sensors that detect a state of an environment including objects. Based in part on the state data, semantic objects are generated. The semantic objects can correspond to the objects and include a set of attributes. Based in part on the set of attributes of the semantic objects, one or more operating modes, associated with the semantic objects can be determined. Based in part on the one or more operating modes, object outputs associated with the semantic objects can be generated. The object outputs can include one or more visual indications or one or more audio indications.

Abstract: In one or more implementations, a method and system are disclosed in which at least one processor receives information associated with an upcoming arthroplasty and demographic factors of a patient in the upcoming arthroplasty. The processor(s) access i) procedure information representing arthroplasty previously performed for each of a plurality of patients and ii) implant information representing types and sizes of implants from a plurality of manufacturers. Further, the processor(s) determine respective unadjusted probabilities of each of a plurality of implant size options within a range of implant sizes, based on i) of at least one statistical model, ii) the accessed procedure information and the implant information, and iii) the demographic factors of the patient.

Abstract: Systems and methods of the present disclosure can facilitate determining a three-dimensional surface representation of an object. In some embodiments, the system includes a computer, a calibration module, which is configured to determine a camera geometry of a set of cameras, and an imaging module, which is configured to capture spatial images using the cameras. The computer is configured to determine epipolar lines in the spatial images, transform the spatial images with a collineation transformation, determine second derivative spatial images with a second derivative filter, construct epipolar plane edge images based on zero crossings of second derivative epipolar planes image based on the epipolar lines, select edges and compute depth estimates, sequence the edges based on contours in a spatial edge image, filter the depth estimates, and create a three-dimensional surface representation based on the filtered depth estimates and the original spatial images.

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: 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: 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: 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: 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.