System, methods, and other embodiments detecting and localizing objects within an image in a wide-view format using a synthetic representation. The method includes converting a real image in a wide-view format to a synthetic representation using a style model, wherein the synthetic representation depicts a distorted view of an object. The method also includes identifying features of the object using an extraction model that distinguishes different scales of the synthetic representation and a simulated scene to define structures associated with the distorted view. The method also includes detecting the object using a decoder model that identifies an attribute and a bounding box of the object from the features. The method also includes executing a task using the attribute and the bounding box to localize the object in the simulated scene.

A prediction system for solar photovoltaic generation includes a region setter that sets a target region where a solar panel is installed and an adjacent region adjacent to the target region, a first data collector that collects numerical weather data of the target region and adjacent region and an amount of the solar photovoltaic generation obtained from the solar panel, a second data collector that collects weather image data of the target region and adjacent region by using a satellite image, a controller that determines whether the target region is affected by wind by comparing the weather image data to each other, and a deep learner that predicts the weather image data of the target region, and predicts the amount of the solar photovoltaic generation by using the amount of the solar photovoltaic generation, the numerical weather data and weather image data of the target region.

An identification apparatus for an equipped vehicle includes a camera configured to capture image data in a field of view directed to an exterior region proximate to the equipped vehicle and a controller in communication with the camera. The controller identifies an object in the image data and identifies a proportion of the object in response to pixels representing the object in the image data. The controller then communicates a notification indicating a trailing vehicle in response to the object approaching the camera in excess of a threshold.

A method performed by an image capture device. The method includes determining whether to send a notification or determining whether to open a door based at least in part on at least one image captured by the image capture device.

Systems for identifying threat materials such as CBRNE threats and locations are provided. The systems can include a data acquisition component configured to determine the presence of a CBRNE threat; data storage media; and processing circuitry operatively coupled to the data acquisition device and the storage media. Methods for identifying a CBRNE threat are provided. The methods can include: determining the presence of a CBRNE threat using a data acquisition component; and acquiring an image while determining the presence of the CBRNE threat. Methods for augmenting a real-time display to include the location and/or type of CBRNE threat previously identified are also provided. Methods for identifying and responding to CBRNE threats are provided as well.

A method of annotating frames of a time sequence of frames captured by at least one travelling vehicle comprises, in a frame processing system: determining a three-dimensional (3D) road model for an area captured in the time sequence of frames; receiving first annotation data denoting a known 3D location of a moving object for a first frame of the time sequence of frames; and automatically generating second annotation data for marking an expected moving object location in at least a second frame of the time sequence of frames, by assuming the moving object moves along an expected path determined from the known 3D location and the 3D road model.

The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle.

A printed matter reviewed by a user is read, and a difference image is generated based on a first image obtained as a result of reading the printed matter and an electronic document that is a printing source from which the printed matter is generated. Based on the difference image, a process is performed to identify an instruction relating to a revision additionally written on the printed matter and a character string to be subjected to the revision in the electronic document. Thereafter, a particular process is executed on the electronic document based on the instruction related to the revision and the character string to which the revision is applied.

Systems (100) and methods (1000) for operating a shoulder Speaker Microphone (“SM”) device coupled to a radio. The methods comprising: using a speaker and a microphone of the shoulder SM device to facilitate auditory communications to and from a user of the radio; activating a thermal imaging device integrated with the shoulder SM device when the shoulder SM device resides at a location of an incident event; capturing thermal images by the thermal imaging device of the shoulder SM device; and performing operations by the shoulder SM device to cause the thermal images to be streamed over a network.

A system including a smart camera and a server computer. The smart camera may be configured to capture video data comprising a plurality of video frames, detect a triggering event and select a plurality of images from the plurality of video frames of the video data in response to the triggering event and communicate the plurality of images. The server computer may be configured to receive the plurality of images, analyze the plurality of images to perform an additional detection corresponding to the triggering event and generate a signal in response to the additional detection. The server computer may implement a neural network for analyzing the plurality of images. The neural network may use more computing resources than the smart camera uses for detecting the triggering event. Communicating the plurality of images may use less bandwidth than communicating the video data.