Abstract: A system and method are provided for sizing and fitting a head mounted wearable computing device for a user based on image data of the head of the user, including a known reference device having a known scale. The system and method may include capturing image data including a face of the user to be fitted for the head mounted wearable computing device. The known reference device having the known scale is compared to features detected in the image data to determine a scaling factor. The scaling factor is used to size, or assign measures to facial features detected in the image data. A three-dimensional model of the head of the user may be generated from the captured image data.

Abstract: A system and method of predicting the fitting of a head mounted wearable device for a user is provided. The system detects fixed facial landmarks in image data captured by a computing device of the user. Measurements associated with the detected fixed facial landmarks may be processed by one or more machine learning models and associated algorithm(s) to predict the probability of fitting of a head mounted wearable device on the head of the user.

Abstract: A system and method of detecting display fit measurements and/or ophthalmic measurements for a head mounted wearable computing device including a display device is provided. An image of a fitting frame worn by a user of the computing device is captured by the user, through an application running on the computing device. One or more keypoints and/or features and/or landmarks are detected in the image including the fitting frame. A three-dimensional pose of the fitting frame is determined based on the detected keypoints and/or features and/or landmarks, and configuration information associated with the fitting frame. The display device of the head mounted wearable computing device can then be configured based on the three-dimensional pose of the fitting frame as captured in the image.

Abstract: A computer-implemented method includes receiving a two-dimensional (2-D) side view face image of a person, identifying a bounded portion or area of the 2-D side view face image of the person as an ear region-of-interest (ROI) area showing at least a portion of an ear of the person, and processing the identified ear ROI area of the 2-D side view face image, pixel-by-pixel, through a trained fully convolutional neural network model (FCNN model) to predict a 2-D ear saddle point (ESP) location for the ear shown in the ear ROI area. The FCNN model has an image segmentation architecture.

Abstract: A system and method of detecting display fit measurements and/or ophthalmic measurements for a head mounted wearable computing device including a display device is provided. An image of a fitting frame worn by a user of the computing device is captured by the user, through an application running on the computing device. One or more visual markers each including a distinct pattern are detected in the image including the fitting frame. A model of the fitting frame, and configuration information associated with the fitting frame, are determined based on the detection of the pattern. A three-dimensional pose of the fitting frame is determined based on the detected visual marker(s) and patterns, and the configuration information associated with the fitting frame. The display device of the head mounted wearable computing device can then be configured based on the three-dimensional pose of the fitting frame as captured in the image.

Abstract: This method and system supplements medical ultrasound imaging devices with a user installable touchscreen monitor and processor that delivers additional functionality to the device. This addition allows for multiple machine learning models working in sequence to provide real-time and near real-time new information to the user. This new information is being produced by multiple machine learning models that are installed on the device and selected by the user using the touch screen to identify features, perform computer aided diagnostics, automatic image calibration, automatically highlight regions of interest, feature highlighting, automatic annotation of images and report generation. The data gathered from the image processing and user inputs are then converted to an industry standard data format. This new information is combined into a report for user review and transferred to the hospital picture archive and communication system, the electronic health record and the hospital billing system.

Abstract: Systems and methods of detecting pupillary positions on a head of a subject are described. While a camera and a light source are positioned in front of the head of the subject, the camera is caused to capture image data representative of a frontal portion of the head of the subject. From the image data, a face of the subject is detected. Up to two eyes are identified from the detected face. A reflection of the light source on each identified eye is detected. A pupillary position of each identified eye is estimated based on a position of the detected light source reflection on the identified eye. One or more of interpupillary distance, left pupil distance, and right pupil distance may be determined from the estimated pupillary positions.

Abstract: A projection system for projecting an image with an increased apparent resolution is provided. The projection system includes one or more projectors, a resampler module and a deconvolution module. The resampler module is configured to upsample an incoming high-resolution signal, perform an integer shift operation on a signal, and downsample to two or more low-resolution signals. The deconvolution module is configured to filter the upsampled high-resolution signal using a spatial domain deconvolution operation, the spatial domain deconvolution operation approximating frequency domain optical corrections based on characteristics of the one or more projectors. Preferably, the spatial domain deconvolution operation uses an N×N spatial kernel extracted from a spatial domain approximation of a Wiener filter.