An imaging system includes an imaging scope, a camera, an image processor, and a system controller. The imaging scope is configured to illuminate an object and capture light reflected from the object. The camera has a light sensor with a light-sensitive surface configured to receive the captured light from the imaging scope, and generate a digital image representative of the captured light. The image processor is configured to receive the digital image from the camera, and use at least one of a random sample consensus (RANSAC) technique and a Hough Transform technique to (i) identify a boundary between an active portion and an inactive portion of the digital image and (ii) generate boundary data indicative of a characteristic of the boundary. The system controller is configured to receive the boundary data from the image processor, and use the boundary data to select and/or adjust a setting of the imaging system.

An apparatus, system, method, and a control program on a recording medium are provided, each of which: acquires first point cloud data, and second point cloud data different from the first point cloud data; sets a plurality of search start positions each used for alignment form the first point cloud data to the second point cloud data; searches, for each of one or more of the plurality of search start positions, a solution candidate for coordinate transformation from the first point cloud data to the second point cloud data, to generate a plurality of solution candidates; and determines a final solution, from the plurality of solution candidates.

Augmented reality devices and methods for computing a homography based on two images. One method may include receiving a first image based on a first camera pose and a second image based on a second camera pose, generating a first point cloud based on the first image and a second point cloud based on the second image, providing the first point cloud and the second point cloud to a neural network, and generating, by the neural network, the homography based on the first point cloud and the second point cloud. The neural network may be trained by generating a plurality of points, determining a 3D trajectory, sampling the 3D trajectory to obtain camera poses viewing the points, projecting the points onto 2D planes, comparing a generated homography using the projected points to the ground-truth homography and modifying the neural network based on the comparison.

Augmented reality devices and methods for computing a homography based on two images. One method may include receiving a first image based on a first camera pose and a second image based on a second camera pose, generating a first point cloud based on the first image and a second point cloud based on the second image, providing the first point cloud and the second point cloud to a neural network, and generating, by the neural network, the homography based on the first point cloud and the second point cloud. The neural network may be trained by generating a plurality of points, determining a 3D trajectory, sampling the 3D trajectory to obtain camera poses viewing the points, projecting the points onto 2D planes, comparing a generated homography using the projected points to the ground-truth homography and modifying the neural network based on the comparison.

In a device and a corresponding method for joining a plurality of individual digital images to form a total image, a plurality of features is determined in a first individual image by means of a selection unit using a feature-based algorithm and then tracked in a second individual image by means of a tracking unit. A transformation matrix, with which the individual images are joined in an output unit to form the total image, is calculated from the determined feature correspondences in a transformation unit. The individual images can be joined in real time and with a high degree of accuracy by means of the feature-based algorithm in combination with a robust algorithm to calculate the transformation matrix.

An apparatus and method for determining an image similarity based on image features. In one aspect, the image similarity determination is based on an image comparison tool. The image comparison tool may be trained, by a machine-learning system, to estimate a similarity between images based on a subset of image data comprised by image features. The estimate may be an estimate of how similar structures found in the images would be following a geometric transformation of some of the structures. In one aspect, an atlas image for performing automatic segmentation of an image is determined according to a comparison made using the image comparison tool.

An apparatus and method for determining an image similarity based on image features. In one aspect, the image similarity determination is based on an image comparison tool. The image comparison tool may be trained, by a machine-learning system, to estimate a similarity between images based on a subset of image data comprised by image features. The estimate may be an estimate of how similar structures found in the images would be following a geometric transformation of some of the structures. In one aspect, an atlas image for performing automatic segmentation of an image is determined according to a comparison made using the image comparison tool.

A computer-implemented method for analysing the relevance of visual parameters for training a computer vision model. Upon adjusting the set of visual parameters to increase their relevance a new set of visual data and corresponding groundtruth results that can be used in (re)training and/or testing the computer vision model.

A computer-implemented method for analysing the relevance of visual parameters for training a computer vision model. Upon adjusting the set of visual parameters to increase their relevance a new set of visual data and corresponding groundtruth results that can be used in (re)training and/or testing the computer vision model.

Disclosed in the present disclosure are an image registration method and a model training method thereof. The image registration method comprises obtaining a reference image and a floating image to be registered, performing image preprocessing on the reference image and the floating image, performing non-rigid registration on the preprocessed reference image and floating image to obtain a registration result image, and outputting the registration result image. The image preprocessing comprises performing, on the reference image and the floating image, coarse-to-fine rigid registration based on iterative closest point registration and mutual information registration. The non-rigid registration uses a combination of a correlation coefficient and a mean squared error between the reference image and the registration result image as a loss function. Further disclosed in the present disclosure are an apparatus and a system for image registration and a computer-readable medium corresponding to the method. The present disclosure can realize precise and efficient image registration with high applicability between images of different time, different modalities, or different sequences.