Abstract: Apparatus, method and storage medium associated with a beverage dispensing apparatus are disclosed herein. In embodiments, a beverage dispensing apparatus may include a dispenser to dispense a beverage into a beverage container placed underneath the dispenser; one or more sensors to sense and collect depth data associated with the beverage container; and a controller coupled to the dispenser and the one or more sensors to control the dispenser's dispensation of the beverage, in accordance with a capacity of the beverage container inferred based at least in part on the collected depth data associated with the beverage container. Other embodiments may be disclosed or claimed.

Abstract: A luminous intensity compensator, comprising a light source; a sensor, configured to receive sensor information representing a position of the light source, and to output sensor data representing the position of the light source; one or more processors, configured to determine from the sensor data a distance between the light source and a reference point; determine a loss factor of a wavelength based on the determined distance between the light source and the reference point; determine a compensated luminous intensity to yield a target luminous intensity of the wavelength after luminous intensity reduction due to the loss factor; and output control data to control the light source to emit the wavelength at the compensated luminous intensity.

Abstract: A system for unmanned aerial vehicle alignment including: an image sensor, configured to obtain an image of unmanned aerial vehicles and provide to a processor image data corresponding to the obtained image, the processor, configured to determine from the image data image positions of the unmanned aerial vehicles, determine an average position of the unmanned aerial vehicles relative to a first axis based on the image positions, determine an average line that extends along a second axis through the average position, wherein the first and second axes are perpendicular to each other, determine a target position of one of the unmanned aerial vehicles based on a relationship between its respective image position and a target alignment, and determine an adjustment instruction to direct said one of the unmanned aerial vehicles toward the target position, and provide the adjustment instruction to said one of the unmanned aerial vehicles.

Abstract: A risk detection and avoidance device comprises one or more image sensors, configured to provide image sensor input data representing a sensor image of a vicinity of the device; and one or more processors, configured to detect from the image sensor data an overpass and one or more persons on the overpass; determine from the image sensor data a falling-object probability associated with the detected one or more persons on the overpass, according to a first logic; determine from the image sensor data one or more falling-object evasion factors associated with a vicinity of the device; and if the falling-object probability exceeds a predetermined threshold, determine a harm avoidance maneuver based at least on the one or more falling-object evasion factors, according to a second logic, and send an instruction comprising the harm avoidance maneuver.

Abstract: Systems and methods may provide a plurality of distortion meshes that compensate for radial and chromatic aberrations created by optical lenses. The plurality of distortion meshes may include different lens specific parameters that allow the distortion meshes to compensate for chromatic aberrations created within received images. The plurality of distortion meshes may correspond to a red color channel, green color channel, or blue color channel to compensate for the chromatic aberrations. The distortion meshes may also include shaped distortions and grids to compensate for radial distortions, such as pin cushion distortions. In one example, the system uses a barrel-shaped distortion and a triangulation grid to compensate for the distortions created when the received image is displayed on a lens.

Abstract: An apparatus, method and computer readable medium for a voice-controlled camera with artificial intelligence (AI) for precise focusing. The method includes receiving, by the camera, natural language instructions from a user for focusing the camera to achieve a desired photograph. The natural language instructions are processed using natural language processing techniques to enable the camera to understand the instructions. A preview image of a user desired scene is captured by the camera. Artificial Intelligence (AI) is applied to the preview image to obtain context and to detect objects within the preview image. A depth map of the preview image is generated to obtain distances from the detected objects in the preview image to the camera. It is determined whether the detected objects in the image match the natural language instructions from the user.

Abstract: Herein is disclosed an unmanned aerial vehicle alignment system comprising one or more image sensors, configured to obtain an image of a plurality of unmanned aerial vehicles and provide to one or more processors image data corresponding to the obtained image; one or more processors, configured to detect from the image data image positions of the plurality of unmanned aerial vehicles; derive a target position based on a relationship between an image position and a target alignment; and determine an adjustment instruction to direct an unmanned aerial vehicle toward the target position.

Abstract: Herein is disclosed a detection device comprising one or more sensors, configured to receive sensor input from a vicinity of a first vehicle, and to generate sensor data representing the received sensor input; one or more processors, configured to detect a second vehicle from the received sensor data; determine from the sensor data a region of a first type relative to the second vehicle and a region of a second type relative to the second vehicle; and control the first vehicle to avoid or reduce travel in the one or more regions of the first type or to travel from a region of the first type to a region of the second type.

Abstract: According to various aspects, an unmanned aerial vehicle may be described, the unmanned aerial vehicle including: one or more sensors configured to gather thermal information associated with a vicinity of the unmanned aerial vehicle; one or more processors configured to determine at least one control information based on the thermal information and to control the unmanned aerial vehicle based on the at least one control information.

Abstract: According to various aspects, an unmanned aerial vehicle controlling device may include: a receiver configured to receive a spherical image of a vicinity from an unmanned aerial vehicle; a display configured to display a first person view of the spherical image; a plurality of motion sensors configured to sense a movement within a tracked space; one or more processors configured to map the sensed movement within the tracked space to a mapped movement within the vicinity of the unmanned aerial vehicle and generate a control signal based on the mapped movement; and a transmitter configured to transmit the control signal to the unmanned aerial vehicle.

Abstract: Embodiments described herein provide for blind spot rendering optimizations for eye tracking head mounted displays. One embodiment provides an apparatus comprising first logic to receive eye-tracking data from an eye tracking system, second logic to determine a blind spot region for a scene based on the eye tracking data, and third logic to provide identifying data for the blind spot region to a renderer. The renderer is configured to render pixels of the scene that fall within the blind spot region at a lower rendering quality relative to the remainder of the scene.

Abstract: Systems and methods may provide a plurality of distortion meshes that compensate for radial and chromatic aberrations created by optical lenses. The plurality of distortion meshes may include different lens specific parameters that allow the distortion meshes to compensate for chromatic aberrations created within received images. The plurality of distortion meshes may correspond to a red color channel, green color channel, or blue color channel to compensate for the chromatic aberrations. The distortion meshes may also include shaped distortions and grids to compensate for radial distortions, such as pin cushion distortions. In one example, the system uses a barrel-shaped distortion and a triangulation grid to compensate for the distortions created when the received image is displayed on a lens.

Abstract: Herein is disclosed an unmanned aerial vehicle segment-imagery system comprising at least a first unmanned aerial vehicle and a second unmanned aerial vehicle, the first unmanned aerial vehicle further comprising one or more processors, configured to control an aerial movement of the first unmanned aerial vehicle; one or more lasers, configured to emit a laser light; and a laser targeting system, configured to cause the laser to strike a second unmanned aerial vehicle.

Abstract: According to various aspects, a vehicle may include: one or more receivers, wherein the one or more receivers receive an animal tracking signal comprising animal attribute data from an animal tracking device configured to store animal attribute data; one or more processers, where in the processors are configured to process the animal attribute data to determine if an animal will be in the path of the vehicle and control the vehicle based on the determination that the animal will be in the path of the vehicle, and to further control the vehicle based on the animal attribute data and/or an animal behavior based on the animal attribute data.

Abstract: A luminous intensity compensator, comprising a light source; a sensor, configured to receive sensor information representing a position of the light source, and to output sensor data representing the position of the light source; one or more processors, configured to determine from the sensor data a distance between the light source and a reference point; determine a loss factor of a wavelength based on the determined distance between the light source and the reference point; determine a compensated luminous intensity to yield a target luminous intensity of the wavelength after luminous intensity reduction due to the loss factor; and output control data to control the light source to emit the wavelength at the compensated luminous intensity.

Abstract: The invention is directed to a method and a device for controlling images in a head mounted display equipped with an eye tracker and worn by a user, comprising the following steps: detecting, with the eye-tracker, an eye gaze of at least one of the eyes of the user; controlling the images depending on the detected eye gaze; wherein the step of controlling the images comprises not rendering or not updating pixels (22) of the images that are not visible by the user at the detected eye gaze.

Abstract: Systems, apparatuses and methods may provide for technology that identifies a change in virtual camera orientation that will increase a virtual walkable space associated with a rendered environment in a head mounted display (HMD) system. Additionally, a saccadic movement state may be detected with respect to a wearer of the HMD system, wherein the change in virtual camera orientation is conducted when the saccadic movement state is present. In one example, the change in virtual camera orientation may be bypassed when the saccadic movement state is not present.

Abstract: According to various aspects, an obstacle map generator is provided, including: one or more processors configured to receive one or more depth images from a depth imaging system, determine, for each depth image of the one or more received depth images, a first set of pixels and a second set of pixels, each pixel of the first set of pixels has a depth value assigned thereto and each pixel of the second set of pixels has no depth value assigned thereto or has a depth value outside a predefined depth value range assigned thereto, assign a pre-defined depth value to one or more pixels of the second set of pixels, and generate an obstacle map based on the determined first set of pixels and the one or more pixels of the second set of pixels having the pre-defined depth value assigned thereto.

Abstract: Herein is disclosed an unmanned aerial vehicle segment-imagery system comprising at least a first unmanned aerial vehicle and a second unmanned aerial vehicle, the first unmanned aerial vehicle further comprising one or more processors, configured to control an aerial movement of the first unmanned aerial vehicle; one or more lasers, configured to emit a laser light; and a laser targeting system, configured to cause the laser to strike a second unmanned aerial vehicle.

Abstract: According to various aspects, a collision avoidance method may include: receiving depth information of one or more depth imaging sensors of an unmanned aerial vehicle; determining from the depth information a first obstacle located within a first distance range and movement information associated with the first obstacle; determining from the depth information a second obstacle located within a second distance range and movement information associated with the second obstacle, the second distance range is distinct from the first distance range, determining a virtual force vector based on the determined movement information, and controlling flight of the unmanned aerial vehicle based on the virtual force vector to avoid a collision of the unmanned aerial vehicle with the first obstacle and the second obstacle.