A method and system are provided for reconstructing a motion-compensated nuclear image of a subject, as well as an arrangement for method. The reconstruction method comprises receiving nuclear image data the acquiring a nuclear image, and a computer program for carrying out the for multiple motion states, reconstructing the data into an image for each motion state, and calculating a deformation vector field for each state for mapping the image onto a reference motion state. Calculating the deformation vector field comprises providing an initial vector field, defining at least one rigid region of the subject, incorporating that rigid region into the initial vector field, and calculating the deformation vector field with the incorporated rigid region. The method further comprises mapping the reconstructed image of each motion state onto the reference state using the deformation vector fields; and combining the mapped images into a motion-compensated nuclear image.

Methods, systems, and computer-readable media for generating a cross-section of a 3D model are disclosed. An example method includes determining a cross-section plane intersecting the 3D model, performing ray-tracing by passing each of a plurality of rays through a corresponding pixel of a viewing plane such that each ray intersects the cross-section plane, determining one or more rays that are within a threshold distance of the 3D model at their respective points of intersection with the cross section plane, and highlighting pixels corresponding to the determined rays.

Local image enhancement processing is executed on an image obtained by imaging a transcription material. The enhanced image is divided into a plurality of patch images and input to a machine learning identifier. The patch images after segmentation output from the machine learning identifier are combined to generate a likelihood map image of skin ridges from the whole image based on a result of the segmentation. Binarization processing is executed on the likelihood map image to generate a binary image. A skin ridge region is extracted based on the binary image to calculate the area of the skin ridge region.

An image processing device includes a processor and a memory that is provided in or connected to the processor. The processor executes a region selection process of selecting a portion of a plurality of tomographic images, which indicate a plurality of tomographic planes of an object, respectively, and have a first resolution, as a target region to be set to a second resolution higher than the first resolution, a resolution enhancement process of increasing the resolution of the target region to the second resolution to generate a high-resolution partial image, and a composite two-dimensional image generation process of generating a high-resolution composite two-dimensional image having the second resolution, using the high-resolution partial image.

Systems and methods are disclosed for generating synthetic medical images, including images presenting rare conditions or morphologies for which sufficient data may be unavailable. In one aspect, style transfer methods may be used. For example, a target medical image, a segmentation mask identifying style(s) to be transferred to area(s) of the target, and source medical image(s) including the style(s) may be received. Using the mask, the target may be divided into tile(s) corresponding to the area(s) and input to a trained machine learning system. For each tile, gradients associated with a content and style of the tile may be output by the system. Pixel(s) of at least one tile of the target may be altered based on the gradients to maintain content of the target while transferring the style(s) of the source(s) to the target. The synthetic medical image may be generated from the target based on the altering.

A vehicle control system including a spatial monitoring system includes on-vehicle cameras that capture images, from which are recovered a plurality of three-dimensional (3D) points. A left ground plane normal vector is determined for a left image, a center ground plane normal vector is determined for a front image, and a right ground plane normal vector is determined for a right image. A first angle difference between the left ground plane normal vector and the center ground plane normal vector is determined, and a second angle difference between the right ground plane normal vector and the center ground plane normal vector is determined. An uneven ground surface is determined based upon one of the first angle difference or the second angle difference, and an alignment compensation factor for the left camera or the right camera is determined. A bird's eye view image is determined based upon the alignment compensation factor.

There is provided with an image processing apparatus. An obtaining unit obtains a first image serving as a read image of an inspection target medium having undergone printing, and a second image serving as a read image of a reference medium representing a target print result. An inspection unit inspects a defect on the inspection target medium based on the first image and the second image by performing inspection at inspection settings different between a print region and a peripheral region of the inspection target medium.

A method of detecting damages in a freight container, comprising capturing a first image of a part of the freight container at an angle deviating from a perpendicular direction in respect of the part of the container, capturing a second image of the same part of the container at an angle substantially perpendicular in respect of the same part of the container, analysing the first and second images for detecting a damage in the part of the container, and in response to detecting the damage in the part of the container providing a damage information image regarding to the part of the container, the damage information image being an image based on the second image regarding to the respective part of the container and including damages detected in at least one of the respective first image or this second image.

Also, a system for detecting damages in a freight container.

Methods and apparatuses for performing automated detection of lung slide using a computing device (e.g., an ultrasound system, etc.) are disclosed. In some embodiments, the techniques determine lung sliding using one or more neural networks. In some embodiments, the neural networks are part of a process that determines probabilities of the lung sliding at one or more M-lines. In some embodiments, the techniques display one or more probabilities of lung sliding in a B-mode ultrasound image.

Three-dimensional (3D) point cloud generation using a stationary laser scanner and a mobile scanner. The method includes scanning a first part of a surrounding with the stationary laser scanner, obtaining a first 3D point cloud, scanning a second part of the surrounding with the mobile scanner, obtaining a second 3D point cloud, whereby there is an overlap region of the first part and the second part, and aligning the second 3D point cloud to the first 3D point cloud to form a combined 3D point cloud. The positional accuracy of points of the second 3D point cloud is increased by automatically referencing second scanner data of the overlap region, generated by the mobile scanner, to first scanner data of the overlap region, generated by the stationary laser scanner. Therewith, deformations of the second 3D point cloud and its alignment with the first 3D point cloud are corrected.