The present disclosure provides a target-image acquisition method. The target-image acquisition method includes acquiring a visible-light image and an infrared (IR) image of a target, captured at a same time point by a photographing device; weighting and fusing the visible-light image and the IR image to obtain a fused image; and obtaining an image of the target according to the fused image. The present disclosure also provides a photographing device and an unmanned aerial vehicle (UAV) using the method above.

There is provided a recognition system adaptable to a portable device or a wearable device. The recognition system senses a body heat using a thermal sensor, and performs functions such as the living body recognition, image denoising and body temperature prompting according to detected results.

An information processing apparatus includes: a first sensor that outputs a first output signal obtained by photoelectrically converting visible light incident through a color filter; a second sensor that outputs a second output signal obtained by photoelectrically converting visible light incident through a color filter or a third output signal obtained by photoelectrically converting light including infrared light incident without going through the color filter; a first processor that generates first image data based on the first output signal, and second image data based on the second output signal or the third output signal; a memory that temporarily stores the first image data and the second image data; a second processor that generates image data obtained by fusing the first image data and the second image data.

An apparatus for mounting on a head including a frame, A face-wearable near-ocular optics and a micro-display for displaying data in front of the eyes is provided. A computing device is coupled to the micro-display. At least one sensor is coupled to the computing device for receiving biometric human information.

According to the present invention, disclosed are a device and a method of generating an object image, recognizing an object, and learning an environment of a mobile robot which perform a deep learning algorithm which allows a robot to create a map and load environment information acquired during the autonomous movement while the autonomous mobile robot is being charged and may be used for an application which finds out a location by finally recognizing objects such as furniture using a method of checking a location of the recognized objects to mark the location on the map.

According to some embodiments of the present disclosure, the disclosure relates to an application system and server kit that create and serve digital twin-enabled applications. This disclosure also relates to a hub-and-spoke classification system. This disclosure also relates to a location-based services framework that leverages a generative content process to improve location prediction. This disclosure also relates to virtual reality and augmented reality applications, as well as digital agents that support various types of applications.

A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may comprise one or more machine learned models, each of which may be trained to output one or more occupancy maps based at least in part on sensor data of different types. The occupancy maps may include a prediction of whether at least a portion of an environment is occupied at a future time by any one of multiple object types. Occupancy maps associated with a same time may be aggregated into a data structure that may be used to validate, reject, or replace the trajectory.

A system for characterizing content relating to a desired outcome is disclosed. The disclosed system can be configured to identify context included in content collected from various content sources, map the identified context into graph nodes and graph edges connecting the graph nodes, identify one or more features of the identified context and adjust at least one of: a graph node and a graph edge based on the identified one or more features, identify a graph incorporating the graph nodes, the graph edges, and at least one of an adjusted graph node and an adjusted graph edge, and provide a recommendation for at least one action for achieving the desired outcome based on the identified graph.

A method, a system, and a non-transitory computer readable medium for evaluating first movements of a first person in view of movements of a second person. The method may include determining the relationships between the first frames and the second frames by a sequence alignment algorithm, the determining of the relationships is based on the inter-frame similarity scores and the multiple state-related scores; and determining at least one evaluation score of the movements of the first person, based on the relationships and the multiple inter-frame similarity scores; wherein the at least one evaluation score is indicative of a similarity between the movements of the first person and the movements of the second person.

A method, an apparatus and an electronic device for face liveness detection based on a neural network model are provided. The method includes: a target visible light image and a target infrared image of a target object to be detected are obtained (S101); a first face image is extracted from the target visible light image, and a second face image is extracted from the target infrared image (S102); a target image array of the target object is generated based on multiple monochromatic components of the first face image and a monochromatic component of the second face image (S103); and feeding the target image array into a pre-trained neural network model for detection, to obtain a face liveness detection result of the target object (S104).