An image inspection device includes: an image acquisition unit to acquire an inspection target image; a geometric transformation processing unit to estimate a geometric transformation parameter for aligning a position of an inspection target in the inspection target image with a first reference image in which a position of the inspection target is known, and geometrically transform the inspection target image using the estimated geometric transformation parameter, thereby generating an aligned image in which the position of the inspection target is aligned with the first reference image; an image restoration processing unit to restore the aligned image, using an image generation network to receive an input image generated using the inspection target image and infer the aligned image as a correct image; and an abnormality determination unit to determine an abnormality of the inspection target using a difference image between the aligned image and the restored aligned image.

A system for surveying a track includes two outer measuring devices and a central measuring device disposed therebetween, relative to the longitudinal direction of the track. Each measuring device has a specific position relative to the track in order to detect geometric track parameters. One outer measuring device includes a camera with a recording area in which a measuring object of the other outer measuring device and a measuring object of the central measuring device are disposed. The camera is connected to an evaluation device for pattern recognition. All of the position parameters of the track required for precise lining and levelling of the track are thus recorded by a single camera. A method of operating the system is also provided.

A method includes receiving selection of a target within an image captured by an image sensor of a payload and displayed on a user interface of the payload, detecting a deviation of the target from an expected target state within the image, generating, based at least partly on the deviation, a payload control signal including a first angular velocity for rotating the payload about an axis of the carrier to reduce the deviation about the axis in a subsequent image, and generating a base support control signal including a second angular velocity for rotating the payload with respect to the axis. When the first and second angular velocities are received, the carrier is controlled to rotate the payload at a third angular velocity about the axis. The third angular velocity is the first angular velocity, the second angular velocity, or a combination of both.

The invention relates to a method for operating a stereoscopic medical microscope, wherein deteriorated and/or invalid calibration data are recognized, wherein for this purpose mutually corresponding image representations of at least one feature arranged in capture regions of cameras of a stereo camera system of the medical microscope are captured by means of the cameras, the captured image representations are evaluated by means of feature-based image processing, wherein the at least one feature is recognized in this case in the captured image representations and a misalignment and/or a decalibration of the cameras of the stereo camera system are/is recognized on the basis of the at least one feature recognized; and wherein at least one measure is carried out depending on an evaluation result. Furthermore, the invention relates to a medical microscope.

Provided are an image rectification method and apparatus, and an electronic system. The image rectification method includes: acquiring a first image and a second image of the same shooting object by means of a first shooting apparatus and a second shooting apparatus which are coaxially disposed; and correcting the second image according to shooting parameters of the first shooting apparatus and the second shooting apparatus to obtain a second rectified image, such that the parallax between the second rectified image and the first image in a vertical direction or a horizontal direction is zero. In the method, by taking a first image as a reference, and by means of adjusting the shooting parameters of the first shooting apparatus and a second shooting apparatus, only the second image is rectified, thereby improving the operation efficiency of image rectification, and improving the accuracy and stability of an image rectification result.

The present disclosure provides a distance determination method, apparatus and system, relating to the technical field of image processing. The method includes the following steps: acquiring a master visual image photographed by a master camera and an original auxiliary visual image photographed by an auxiliary camera; acquiring an initial matching point pair between the master visual image and the original auxiliary visual image through feature extraction and feature matching; correcting the original auxiliary visual image sequentially, based on the initial matching point pair and different constraints, so as to obtain a target auxiliary visual image, wherein the different constraints includes: a constraint of a minimum rotation angle and a constraint of a minimum parallax; and determining a focusing distance according to the master visual image and the target auxiliary visual image. The focusing distance can be determined more accurately.

A method for extracting sentiments or mood from art images includes: receiving at least one of the art images as an input image; preprocessing the input image; extracting features from the preprocessed input image, the extracting including predicting a color label corresponding to a dominant perceptual color detected from the preprocessed input image a dominant subject from the preprocessed input image, detecting low-level image features from the preprocessed input image, and extracting mood feature information based on a description information included in the input image; classifying the extracted features into a plurality of mood/sentiments classes, using an artificial neural network; and predicting at least one of a mood or a sentiment that is present in the input image based on the dominant perceptual color and the plurality of mood/sentiments classes.

A method for estimating text heights of text line images includes estimating a text height with a sequence recognizer. The method further includes normalizing a vertical dimension and/or position of text within a text line image based on the text height. The method may also further include calculating a feature of the text line image. In some examples, the sequence recognizer estimates the text height with a machine learning model.

A method comprising: obtaining an image; identifying a rotation angle for the image by processing the image with a first neural network; rotating the image by the identified rotation angle to generate a rotated image; classifying the image with a second neural network; and outputting an indication of an outcome of the classification, wherein the first neural network is trained, at least in part, based on a categorical distance between training data and an output that is produced by the first neural network.

A method includes obtaining a fisheye image of a scene and identifying multiple regions of interest in the fisheye image. The method also includes applying one or more transformations to transform and rotate one or more of the regions of interest in the fisheye image to produce one or more transformed regions. The method further includes generating a collage image having at least one portion based on the fisheye image and one or more portions containing the one or more transformed regions. In addition, the method includes performing object detection to identify one or more objects captured in the collage image.