Techniques are described herein that overcome the limitations of conventional techniques by bridging a gap between user interaction with digital content using a computing device and a user's physical environment through use of augmented reality content. In one example, user interaction with augmented reality digital content as part of a live stream of digital images of a user's environment is used to specify a size of an area that is used to filter search results to find a “best fit”. In another example, a geometric shape is used to represent a size and shape of an object included in a digital image (e.g., a two-dimensional digital image). The geometric shape is displayed as augmented reality digital content as part of a live stream of digital images to “assess fit” of the object in the user's physical environment.

A visible light sensor may be configured to sense environmental characteristics of a space using an image of the space. The visible light sensor may be controlled in one or more modes, including a daylight glare sensor mode, a daylighting sensor mode, a color sensor mode, and/or an occupancy/vacancy sensor mode. In the daylight glare sensor mode, the visible light sensor may be configured to decrease or eliminate glare within a space. In the daylighting sensor mode and the color sensor mode, the visible light sensor may be configured to provide a preferred amount of light and color temperature, respectively, within the space. In the occupancy/vacancy sensor mode, the visible light sensor may be configured to detect an occupancy/vacancy condition within the space and adjust one or more control devices according to the occupation or vacancy of the space. The visible light sensor may be configured to protect the privacy of users within the space via software, a removable module, and/or a special sensor.

Systems and methods for tagging portions a spherical video segment are provided. The spherical video segment may include visual content. User input indicating a moment in time in the spherical video segment and a viewing angle corresponding to the moment in time may be received. Tag information for the spherical video segment may be generated. The tag information may identify the moment in time and the viewing angle such that a subsequent presentation of the spherical video segment, proximate to the moment in time, includes a notification that the viewing angle is outside a display field of view based on the viewing angle being located outside the display field of view.

A reading order extrapolation and management system and process for facilitating auditory comprehension of electronic documents. As an example, a user may access contents of an electronic document via an application and request a speech-synthesized recitation of any media in the electronic document. The application may make use of a reading order that has been specifically generated and improved by reference to eye tracking data from users reading the document. A reading order can be assigned to a document and implemented when, for example, a screen reader is engaged for use with the document. Such systems can be of great benefit to users with visually impairments and/or distracted users seeking a meaningful audio presentation of textual content.

A method for detecting a specific object based on a specific model includes: capturing a set of images, wherein objects in each image include a desired object; transmitting the set of images to a cloud server; in response to found objects being obtained from the set of images based on at least one object detection algorithm in the cloud server, displaying the found objects fora user to confirm which object is desired; in response to the desired object being confirmed from the found objects that are displayed, loading the specific model of the desired object from the cloud server, wherein the specific model of the desired object is trained on the cloud server based on at least the set of images and related CNN algorithm; and performing the specific model to detect the specific object on a captured image.

A medical image processing device a reference image that is a medical image with which boundary line information related to a boundary line that is a boundary between an abnormal region and a normal region and landmark information related to a landmark that is a characteristic structure of the subject are associated and a captured image that is the medical image captured in real time, detects the landmark from the captured image, calculates a ratio of match between the landmark included in the reference image and the landmark included in the captured image, estimates a correspondence relationship between the reference image and the captured image on the basis of the ratio of match and information regarding the landmarks included in the reference image and the captured image, and generates a superimposition image in which the boundary line associated with the reference image is superimposed on the captured image on the basis of the correspondence relationship.

Apparatuses, methods, systems, and program products are disclosed for capturing an image and determining whether a switch is in a filtering position or a non-filtering position. When the switch is in the filtering position, the camera apparatus detects a switch position and determines whether the switch is in the filtering position or the non-filtering position. The camera captures the image, including audio/visual signals, while the switch is detected to be in the filtering position and modifies the image to inhibit biometric data collection from the image in response to the switch being in the filtering position when the image is captured.

A computer-implemented method for generating a synthetic training data set for training a machine learning computer vision model for performing at least one user defined computer vision task, in which spatially resolved sensor data are processed and evaluated with respect to at least one user defined object of interest, including receiving at least one model of a user defined object of interest; determining at least one render parameter and multiple render parameters; generating a set of training images by rendering the at least one model of the object of interest based on the at least one render parameter; generating annotation data for the set of training images with respect to the at least one object of interest; and providing a training data set including the set of training images and the annotation data for being output to the user and/or for training the computer vision model.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that generates lane path descriptors for use by autonomous vehicles. One of the methods includes receiving data that defines valid lane paths in a scene in an environment. Each valid lane path represents a path through the scene that can be traversed by a vehicle. User interface presentation data can be provided to a user device. The user interface can contain: (i) a first display area that displays a first visual representation of the sensor measurement; and (ii) a second display area that displays a second visual representation of the set of valid lane paths. User input modifying the second visual representation of the set of valid lane paths can be received; and in response to receiving the user input, the set of valid lane paths of the scene in the environment can be modified.

The present application is at least directed to a system including a processor and non-transitory memory including computer-executable instructions, which when executed by the processor, perform receiving, via a user operating the system, a selection of a mode for recognizing objects. The processor is also configured to execute the instructions of causing a camera operably coupled to the system to operate in the selected mode for recognizing objects. The processor is further configured to execute the instructions of receiving, via the user operating the system, an image of a selected object. The processor is even further configured to execute the instructions of evaluating, via a trained machine learning model, one or more attributes of the selected object. The processor is yet further configured to execute the instructions of generating an image description based on at least a subset of the evaluated one or more attributes. The processor is yet even further configured to execute the instructions of communicating the generated image description to the user via a user interface.