An image processing method and a device configured to implement the same are disclosed. The device comprises: a hybrid imaging device configured to obtain optical input; and a processing device in signal communication with the hybrid imaging device. The processing device comprises: a motion detection circuit that performs feature tracking based on a first component of an obtained optical input; a motion estimation circuit that performs motion compensation based on output of the motion detection unit; a frame reconstruction circuit that reconstructs image frame based on both the output of the motion estimation unit and a second component of the optical input; and an output unit that outputs image frame at a predetermined global frame rate.

Aspects described provide systems and methods that relate generally to image analysis and, more specifically, identifying individual components and elements in an image. The systems and methods include a valuation application executing one or more application program interfaces (APIs) communicating with one or more websites via a network, where the user is prompted to enter information and/or take pictures or videos of their vehicle that they would like to sell. The valuation application utilizes a machine learning model to identify and value the various vehicle components within the images and videos. Based on the machine learning model, the valuation application identifies each component according to the images and videos and performs a search to determine the value of the components identified. The valuation application tabulates and summarizes the vehicle component resale values and resell information for the user to view.

Systems and methods for image-based perception. The methods comprise: capturing images by a plurality of cameras with overlapping fields of view; generating, by a computing device, spatial feature maps indicating locations of features in the images; identifying, by the computing device, overlapping portions of the spatial feature maps; generating, by the computing device, at least one combined spatial feature map by combining the overlapping portions of the spatial feature maps together; and/or using, by the computing device, the at least one combined spatial feature map to define a predicted cuboid for at least one object in the images.

Systems, methods, and apparatus for identifying, tracking, and disrupting UAVs are described herein. A tracking system can receive sensor data associated with an object in a particular airspace from one or more radio frequency sensors. The tracking system can analyze the sensor data relating to the object to identify a type of RF signal being used by the object. A portable countermeasure device can generate one or more disruption signals on one or more targeted bands of spectrum based on the type of RF signal being used by the object.

Disclosed are a system and a method for ultrasound elastography and a method for dynamically processing frames in real time. The system includes an elasticity processing apparatus having an elasticity information detecting module for extracting elasticity information representing the elasticity of a target to be detected; a quality parameter calculating module for calculating at least a quality parameter reflecting quality of each elasticity image corresponding to the elasticity information; and a frame processing module for determining whether to output corresponding elasticity image based on the quality parameter of each elasticity image. When calculating a strain of consecutive images, the parameter reflecting the quality of each image is also computed, through which, the current elasticity image is determined whether to be displayed, thus avoiding the situation that colors of acquired successive elasticity images may vary greatly due to large difference existing in stress.

A system and method for inferring operator intent by detecting operator focus incorporates cameras positioned within a cockpit or control space of a vehicle and oriented at an operator of the vehicle. The cameras capture images of the operator in a control seat; the images are analyzed (either individually or sequentially) to determine a gaze and/or body pose of the operator (including, e.g., a position and orientation of the torso and limbs). By comparing the determined gaze and/or body pose to the positions and orientations of potential focus targets within the control space (e.g., windows, display units, and/or control panels that the operator may engage with visually and/or physically), the system predicts the most likely future focus target or targets: what the operator is most likely to visually and/or physically engage with next. Operator intent may be further analyzed to identify potentially abnormal or anomalous behavior.

Systems and methods for image-based perception. The methods comprise: obtaining, by a computing device, images captured by a plurality of cameras with overlapping fields of view; generating, by the computing device, spatial feature maps indicating locations of features in the images; defining, by the computing device, predicted cuboids at each location of an object in the images based on the spatial feature maps; and assigning, by the computing device, at least two cuboids of said predicted cuboids to a given object when predictions from images captured by separate cameras of the plurality of cameras should be associated with a same detected object.

One example system for displaying immersive scenes includes a processor and at least one memory device. The memory device includes instructions that are executable by the processor to cause the processor to receive a collection of metadata associated with an immersive scene, identify each of a plurality of properties of the immersive scene based on the collection of metadata, receive a dynamic immersive background, receive a plurality of video streams associated with a video conference, and display each of the plurality of video streams in the immersive scene based at least in part of the plurality of properties of the immersive scene and on the dynamic immersive background.

When three-dimensional audio is produced by using headphones, particular HRTF-filters are used to modify sound for the left and right channels of the headphone. As the morphology of every ear is different, it is beneficial to have HRTF-filters particularly designed for the user of headphones. Such filters may be produced by deriving ear geometry from a plurality of images taken with an ordinary camera, detecting necessary features from images and fitting said features to a model that has been produced from accurately scanned ears comprising representative values for different sizes and shapes. Taken images are sent to a server (52) that performs the necessary computations and submits the data further or produces the requested filter.

A method for calibrating a photodetector array supplying a video stream includes: a determination step, wherein an offset table is determined for each current image of the video stream based on at least two corrections from among the following: a first correction from a comparison of the current image to a corresponding predetermined reference table; a second correction from a calculation of a column error of the current image; and a third correction from a high-pass temporal filtering of the video stream; and a calculation step, wherein a current value of an offset table, equal to a sum between a previous value of the offset table and a weighted sum of at least two corrections, is calculated, with each coefficient of the offset table being associated with a respective photodetector of the array.