An object manipulation apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: calculate, based on an image in which one or more objects to be grasped are contained, an evaluation value of a first behavior manner of grasping the one or more objects; generate information representing a second behavior manner based on the image and a plurality of evaluation values of the first behavior manner; and control actuation of grasping the object to be grasped in accordance with the information being generated.

Data encryption and Human Pose Estimation based on imaging a body segment. A key for encrypting a data file is generated based on image data that represent a unique biometric feature of a body segment of a user or motion of the user. An image engine executes artificial intelligence to identify matching image data for decrypting the data file. The image engine is further trained to predict changes in image data due to aging, stress, and the like. An avatar associated with the user, which is generated based on a movement pattern of the user, is configurable for generating an encryption key and for use in an avatar-based language.

Intelligent systems are disclosed that respond to user intent and desires based upon activity that may or may not be expressly directed at the intelligent system. In some embodiments, the intelligent system acquires a depth image of a scene surrounding the system. A scene geometry may be extracted from the depth image and elements of the scene may be monitored. In certain embodiments, user activity in the scene is monitored and analyzed to infer user desires or intent with respect to the system. The interpretation of the user's intent as well as the system's response may be affected by the scene geometry surrounding the user and/or the system. In some embodiments, techniques and systems are disclosed for interpreting express user communication, e.g., expressed through hand gesture movements. In some embodiments, such gesture movements may be interpreted based on real-time depth information obtained from, e.g., optical or non-optical type depth sensors.

Computer implemented methods and devices for determining dimensions or distances of head features are provided. The method includes identifying a plurality of features in an image of a head of a person. A real dimension of at least one target feature of the plurality of features or a real distance between at least one target feature of the plurality features and a camera device used for capturing the image is estimated based on probability distributions for real dimensions of at least one feature of the plurality of features and a pixel dimension of the at least one feature of the plurality of features.

A machine-learning architecture may be trained to determine point cloud data associated with different types of sensors with an object detected in an image and/or generate a three-dimensional region of interest (ROI) associated with the object. In some examples, the point cloud data may be associated with sensors such as, for example, a lidar device, radar device, etc.

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. The system and method may include capturing image data including a face of a user to be fitted for the head mounted wearable computing device. A three-dimensional head pose and gaze measurements may be extracted and a three-dimensional model may be developed from the captured image data. The system may detect display fit measurements and/or ophthalmic fit measurements from the three-dimensional model, and may provide one or more head mounted wearable computing devices that meet the display fit and/or ophthalmic fit requirements.

An optical dimensioning system includes one or more light emitting assemblies configured to project one or more predetermined patterns on an object; an imaging assembly configured to sense light scattered and/or reflected off the object, and to capture an image of the object while the patterns are projected; and a processing assembly configured to analyze the image of the object to determine one or more dimension parameters of the object. The light emitting assembly may include a single piece optical component configured for producing a first pattern and second pattern. The patterns may be distinguishable based on directional filtering, feature detection, feature shift detection, or the like. A method for optical dimensioning includes illuminating an object with at least two detectable patterns; and calculating dimensions of the object by analyzing pattern separate of the elements comprising the projected patterns. One or more pattern generators may produce the patterns.

A method, apparatus and system for 3D object detection and segmentation are provided. The method comprises the steps of: extracting multi-view 2D features based on multi-view images captured by a plurality of cameras; generating a 3D feature volume based on the multi-view 2D features; and performing a depth estimation, a semantic segmentation, and a 3D object detection based on the 3D feature volume. The method, apparatus, and system of the disclosure are faster, computation friendly, flexible, and more practical to deploy on vehicles, drones, robots, vehicles, mobile devices, or mobile communication devices.

Disclosed are systems, apparatuses, processes, and computer-readable media to implement a heterogenous biometric authentication process in a control system. A process includes obtaining radar information identifying measured properties of at least one object in an environment, generating pre-processed radar information for input into a neural network at least in part by processing the obtained radar information, generating an object detection output for the at least one object at least in part by detecting the at least one object using the neural network with the pre-processed radar information as input, and modifying, based on the obtained radar information, the object detection output for the at least one object.

A geographic area mapping system may enable collecting, from a set of mobile devices, radio frequency data, the radio frequency data comprising information about a set of network connections in the geographic area; collecting lidar data for the geographic area; generating a mapping between the collected radio frequency data and the collected lidar data for the geographic area; and providing a visualization of the mapped radio frequency data and lidar data for the geographic area.