Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing ray-tracing and mesh generation to illuminate two-dimensional digital vector images. In response to inserting a light source into a two-dimensional digital vector image including one or more vector elements, the disclosed system embeds the vector element(s) into a polygonal mesh generated for a region of the digital vector image. The disclosed system samples a plurality of rays at a plurality of points within the region and detect intersections of the rays with the vector element(s) or a bounding shape surrounding the region. Additionally, the disclosed system determines color values for mesh points in the polygonal mesh based on the intersections of the rays by interpolating color values associated with the intersected points. Furthermore, in some embodiments, the disclosed system subdivides the polygonal mesh according to a priority queue based on error scores associated with the determined color values.

Systems, devices, and techniques are disclosed for style-based dynamic content generation. A seed image, entity design data, entity style data, and text items may be received. Bounding boxes that identify areas of the seed image for the placement of the text items may be generated for the seed image. Variant images may be generated from the seed image, the entity design data, and the entity style data. The variant images may be generated by placing text items in the bounding boxes based on the entity design data and rendering text of the text items using the entity style data.

Embodiments described herein provide methods and systems for open vocabulary object detection of images. given a pre-trained vision-language model and an image-caption pair, an activation map may be computed in the image that corresponds to an object of interest mentioned in the caption. The activation map is then converted into a pseudo bounding-box label for the corresponding object category. The open vocabulary detector is then directly supervised by these pseudo box-labels, which enables training object detectors with no human-provided bounding-box annotations.

A processor-implemented method of video processing using includes receiving, via an artificial neural network (ANN), a video including a first frame and a second frame. A saliency map is generated based on the first frame of the video. The second frame of the video is sampled based on the saliency map. A first portion of the second frame is sampled at a first resolution and a second portion of the second frame is sampled at a second resolution. The first resolution is different than the second resolution. A resampled second frame is generated based on the sampling of the second frame. The resampled second frame is processed to determine an inference associated with the video.

An information transmission system includes an encoder, a display, an imaging apparatus, and a decoder. The encoder is mounted on a first aircraft and is configured to encode information to create a secret code. The display is mounted on the first aircraft and is configured to display the secret code created by the encoder at a predetermined position on a surface of a body of the first aircraft. The imaging apparatus is mounted on a second aircraft and is configured to capture an image of the first aircraft. The decoder is mounted on the second aircraft and is configured to detect an image region including the secret code from the image captured by the imaging apparatus, decode the secret code by performing image processing on the detected image region, and read the information.

An image capturing device is provided on a working machine and takes a captured image of surroundings including an object around the working machine. A first setting part sets a detection region in the captured image where the object is detected. A responsive action part executes a predetermined responsive action in a case that the object is in the detection region in the captured image. The first setting part sets a boundary that is a limit of the detection region in the captured image. The boundary extends in a front orthogonal direction and has a certain shape.

A chip recognition system in which a chip is configured to at least partially have a specific color indicative of a value of the chip includes: a recording device that uses a camera and records a state of the chip as an image; an image analysis device that subjects the image so recorded to image analysis and recognizes at least the specific color and a reference color that is present in the image and differs from the specific color; and a recognition device at least including an artificial intelligence device that uses a result of the image analysis by the image analysis device and specifies the specific color of the chip, wherein the artificial intelligence device of the recognition device has been subjected to teaching using, as training data, a plurality of images of the chip and the reference color irradiated with different illumination intensities.

A device may receive accelerometer data and video data for a vehicle and may identify bounding boxes and object classes for objects near the vehicle. The device may identify tracks for the objects and may filter out tracks that are not associated with vehicles or vulnerable road users to generate one or more tracks or an indication of no tracks. The device may generate a collision cone identifying a drivable area of the vehicle to identify objects more likely to be involved in a collision and may filter out tracks from the one or more tracks, based on the bounding boxes, and to generate a subset of tracks or another indication of no tracks. The device may determine scores for the subset of tracks and may identify a track of the subset of tracks with a highest score. The device may perform actions based on the identified track.

Systems and methods are provided for remotely detecting a status associated with an autonomous vehicle and generating control actions in response to such detections. In one example, a computing system can access a third-party communication associated with an autonomous vehicle. The computing system can determine, based at least in part on the third-party communication, a predetermined identifier associated with the autonomous vehicle. The computing system can determine, based at least in part on the third-party communication, a status associated with the autonomous vehicle, and transmit one or more control messages to the autonomous vehicle based at least in part on the predetermined identifier and the status associated with the autonomous vehicle.

A method at a server for container location verification within a container yard, the method including requesting image data from at least one image sensor apparatus affixed to a container within the container yard; receiving the image data; and processing the image data to identify a location of a target container.