Example implementations include a method, apparatus, and computer-readable medium for sharing analytical resources in a camera network, comprising capturing a video clip comprising a set of image frames via a first camera having only non-artificial intelligence (A.I) features, wherein the first camera is part of a camera network. The implementations further include identifying, in the camera network, a second camera that has an A.I feature. The implementations further include determining whether the second camera has bandwidth to analyze the video clip. The implementations include transmitting, via the first camera, the video clip to the second camera for analysis using the A.I feature, in response to determining that the second camera has bandwidth. The implementations include receiving, by the first camera from the second camera, metadata comprising results of the analysis using the A.I feature and generating, for display on a user interface, the video clip with the metadata.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

Provided is a tool wear monitoring device configured to input a plurality of pieces of image data captured with a microscope camera while changing an angle, and to monitor tool wear. The tool wear monitoring device includes a data analysis unit configured to analyze image data. The data analysis unit binarizes the plurality of pieces of image data captured while changing an angle, extracts data in which a worn region has a maximum area among the plurality of pieces of image data, and analyzes the amount of wear from the extracted data with the maximum area.

In one implementation, a method of displaying overlapping objects is performed at a device including an optical see-through display, one or more processors, and a non-transitory memory. The method includes obtaining a request to display a first object in front of a second object. The method includes modifying a transparency of the first object in a region corresponding to an overlap between the first object and the second object. The method includes displaying the first object in front of the second object while maintaining visibility of the second object through the overlap between the first object and the second object based on the modified transparency.

Determining the yaw angle of a trailer with respect to the longitudinal axis of a towing vehicle is disclosed. This includes capturing first and second images of the trailer using a camera. Trailer orientation with respect to the vehicle is different on the two images. First and second trailer features are determined which are visible on the two images. The first and second features are at different positions of the trailer. A first angle estimation is calculated characterizing the pivot angle in a horizontal plane between the first feature on the first image and the first feature on the second image relative to a towing vehicle fix point. A second angle estimation is calculated characterizing the pivot angle in a horizontal plane between the second feature on the first image and the second feature on the second image relative to the fix point. The yaw angle is calculated based on the first and second angle estimations.

The invention concerns a method for measuring the dimensions of empty glass containers (2) consisting in: selecting at least one region to be inspected of the container, transporting the containers, positioning, on either side of the region to be inspected, at least one focus of an X-ray generator tube and image sensors, acquiring, using image sensors, for each container during its displacement, at least three radiographic images of the inspected region, analyzing the at least three radiographic images so as to determine the three-dimensional coordinates of a set of points to deduce at least one inner diameter of the neck and/or one thickness of the body.

Methods, systems, and apparatus, including medium-encoded computer program products, for pre-processing image data before 3D object tracking include, in at least one aspect, a method including: performing object detection in uncompressed, two-dimensional image data from a camera to produce two-dimensional location data for objects of interest; processing the two-dimensional location data for the objects of interest using a motion criterion to generate possible paths data for the objects of interest; and constructing a flight track of an object in three-dimensional space, from the possible paths data and position information obtained from a sensor, by filtering out false positives in the possible paths data.

Various aspects of a system, a method, and a computer program product for determining a distance to the object on a road are disclosed herein. In accordance with an embodiment, the system includes a memory and a processor. The processor may be configured to receive visual data, location data and motion data of the vehicle corresponding to the first instance in time, and map data corresponding to the location data. The processor may be configured to calculate a distance of the vehicle from the object based on the visual data. The processor may be further configured to validate the location data, the motion data, and the calculated distance of the vehicle from the object, based on the map data. The processor may be further configured to generate output data corresponding to the object, based on the validated location data, the validated motion data, and the validated distance of the vehicle from the object.

A method for detecting defects in appearance of a product from images thereof, applied in an electronic device, obtains positive sample images, negative sample images, and product sample images, divides the product sample images into input image blocks, and inputs the input image blocks into a pre-trained autoencoder to obtain reconstructed image blocks. The electronic device determines corresponding pixel points in the input image blocks, and corresponding pixel difference values, and generates feature connection regions of each input image block according to the positive sample images and the pixel difference values. The electronic device generates a first threshold, selects target regions from the feature connection regions and the first threshold, and generates a second threshold. The electronic device further determines a detection result of a product sample in the product sample image according to an area of the target area and the second threshold.

An eyes measurement system, a method and a computer-readable medium are provided, including a client device with a measurement application and a cloud processing device, where the cloud processing device receives the subject's eye images uploaded by the measurement application. After pre-processing the eye images, the cloud processing device uses a prediction model to obtain the predicted eye measure of the subject such as an MRD1, an MRD2 and an LF, and presents the predicted eye measure of the MRD1, the MRD2 and the LF to the clinicians as a basis for diagnosis. Therefore, the eye images are taken without restricting to the places, and the prediction model is used to accurately obtain the subject's eye measure, thereby providing the clinicians with a clear basis for diagnosis.