A method includes obtaining multiple input images of a scene based on image data captured using multiple imaging sensors. The method also includes generating a gain map identifying relative gains of the imaging sensors. The gain map is generated using the input images and translational and rotational offsets between one or more pairs of the input images. Generating the gain map may include using, for each pair of the input images, a rotation matrix based on a rotation angle between the pair of the input images. The method may further include using the gain map to process additional image data captured using the imaging sensors.

A method for generating a synthesized medical image by receiving a normal image includes generating first data based on a random selection, generating second data, and, based at least in part on the first and second data, modifying the normal image to form the synthesized medical image. Modifying the normal image comprises combining the first data and the second data. The first data characterizes an image that represents a lesion and the second data characterizes a transformation of that image as well as a location of the lesion.

A computing system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include determining that a portion of an existing map is to be updated; obtaining a point cloud acquired by one or more Lidar sensors corresponding to a location of the portion; converting the portion into an equivalent point cloud; performing a point cloud registration based on the equivalent point cloud and the point cloud; and updating the existing map based on the point cloud registration.

Surgical instruments and methods for performing an osteotomy are disclosed herein. A surgical instrument includes a body with a distal end, a proximal end, a first surface, and a second surface. The surgical instrument can include cutting burrs positioned on the first surface and/or the second surface. The surgical instrument can also include cutting burrs positioned on the first surface and cutting blades positioned on the second surface.

When reviewing digital pathology tissue specimens, multiple slides may be created from thin, sequential slices of tissue. These slices may then be prepared with various stains and digitized to generate a Whole Slide Image (WSI). Review of multiple WSIs is challenging because of the lack of homogeneity across the images. In embodiments, to facilitate review, WSIs are aligned with a multi-resolution registration algorithm, normalized for improved processing, annotated by an expert user, and divided into image patches. The image patches may be used to train a Machine Learning model to identify features useful for detection and classification of regions of interest (ROIs) in images. The trained model may be applied to other images to detect and classify ROIs in the other images, which can aid in navigating the WSIs. When the resulting ROIs are presented to the user, the user may easily navigate and provide feedback through a display layer.

Examples disclosed herein may involve a computing system that is operable to (i) identify a source map and a target map for transferring map data, where the source map and the target map have different respective coordinate frames and respective coverage areas that at least partially overlap, (ii) select a real-world element for which to transfer previously-created map data from the source map to the target map, (iii) select a source image associated with the source map in which the selected real-world element appears and has been labeled, (iv) select a target image associated with the target map in which the selected real-world element appears, (v) derive a geometric relationship between the source image and the target image, and (vi) use the derived geometric relationship between the source image and the target image to determine a position of the real-world element within the respective coordinate frame of the target map.

There is provided an automatic correction method for an onboard camera and an onboard camera device. The automatic correction method includes the following steps: obtaining a lane image with the onboard camera and a current extrinsic parameter matrix, and identifying two lane lines in the lane image; converting the lane image into a top-view lane image, and obtaining two projected lane lines in the top-view lane image for the two lane lines; calculating a plurality of correction parameter matrices corresponding to the current extrinsic parameter matrix according to the two projected lane lines; and correcting the current extrinsic parameter matrix according to the plurality of correction parameter matrices. This can be applied in situations where the vehicle is stationary or travelling for automatic correction on the extrinsic parameter matrix of the onboard camera.

The present disclosure discloses a method and apparatus for generating an image, a device, a storage medium and a program product, relates to the field of artificial intelligence, and particularly to computer vision and deep learning technologies, and may be applied in smart cloud and power grid inspection scenarios. A particular implementation of the method comprises: acquiring an original insulator image; performing an image transformation on the original insulator image to obtain a composite insulator image; and inputting the original insulator image and the composite insulator image into a pre-trained generative adversarial network to generate a target insulator image. According to the implementation, the image transformation is performed on the original insulator image, and then, massive target insulator images are generated through the generative adversarial network.

The present invention provides a method for quantitatively identifying the defects of large-size composite material based on infrared image sequence, firstly obtaining the overlap area of an infrared splicing image, and dividing the infrared splicing image into three parts according to overlap area: overlap area, reference image area and registration image area, then extracting the defect areas from the infrared splicing image to obtain P defect areas, then obtaining the conversion coordinates of pixels of defect areas according to the three parts of the infrared splicing image, and further obtaining the transient thermal response curves of centroid coordinate and edge point coordinates, finding out the thermal diffusion points from the edge points of defect areas according to a created weight sequence and dynamic distance threshold ε.sub.ttr×d.sub.p_max, finally, based on the thermal diffusion points, the accurate identification of quantitative size of defects are completed.

Disclosed is an image processing method. The method includes: determining that an original image is to be displayed on a dividing line between two display screens; acquiring a complete display picture of the original image, and calculating distances from boundaries of the original image to the dividing line; and adjusting a display position of the original image on the two display screens according to the distances, and displaying the complete display picture of the original image according to the adjusted display position. Further disclosed are an image processing apparatus, a storage medium and a processor.