Apparatuses, systems, methods, and medium are disclosed for precise geospatial structure geometry extraction from multi-view imagery, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: receive an image of a structure having an outline, the image having pixels with first pixel values depicting the structure and second pixel values outside of the structure depicting a background of a geographic area surrounding the structure, and image metadata including first geolocation data; and generate a synthetic shape image of the structure from the image using a machine learning algorithm, the synthetic shape image including pixels having pixel values forming a synthetic shape of the outline, the synthetic shape image having second geolocation data derived from the first geolocation data.

A display apparatus for a vehicle includes: a controller configured to create map information; and a display device configured to display the map information created by the controller, wherein the controller controls the display device to display a path guidance texture based on a road shape when guiding a path among the map information.

In implementations for free form radius editing, a computing device implements a radius editing system, such as may be integrated with an image editing application. The radius editing system can determine the edge segments for outlines of image objects depicted in a digital image, where the edge segments include corner segments of the image objects. The radius editing system can also determine the radius values of the corner segments of the image objects, and the radius values of the corner segments are maintained in a cache as part of object data corresponding to the image objects depicted in the digital image. The radius editing system can also identify one or more similar corner segments of the image objects that have an equivalent radius value as a selected corner segment responsive to an editing input of a radius of the selected corner segment of an image object.

A machine tool that visualizes the state of a ball screw in an easy-to-understand way includes a detector that detects at least one sensed value among vibrations, sound, and a current, heat, light, and power value applied to drive a ball screw during warming-up, a feature amount extractor that extracts a first feature amount and a second feature amount from the sensed value obtained by the detector, and a display that displays a point plotting the sensed value, and at least two boundaries laid out like contour lines to represent the possibility of generation of an anomaly in the ball screw, on a plane having a first axis defined by numerical values regarding the first feature amount and a second axis defined by numerical values regarding the second feature amount.

A method for operating a medical imaging device includes obtaining lesion information on at least one lesion detected from a medical image, determining a shape and a position of at least one contour corresponding to the at least one lesion based on the obtained lesion information, determining a position of at least one text region that includes a text indicating the lesion information on the at least one lesion in the medical image, and displaying the at least one contour and the text included in the at least one text region on the medical image, based on the determined shape and position of the at least one contour and the determined position of the at least one text region.

The present disclosure realizes a configuration capable of accurately displaying a flight path of a drone on an actually captured image of the drone. The configuration includes a data processing unit that displays a moving path of a moving device such as a drone on a display unit that displays a camera-capturing image of the moving device. The data processing unit generates a coordinate conversion matrix for performing coordinate conversion processing of converting position information according to a first coordinate system, for example, the NED coordinate system indicating the moving path of the moving device into a second coordinate system, for example, the camera coordinate system capable of specifying a pixel position of a display image on the display unit and outputs, to the display unit, the moving path having position information according to the camera coordinate system generated by coordinate conversion processing to which the generated coordinate conversion matrix is applied.

The medical image processing device includes a processor, in which the processor acquires a medical image obtained by imaging a subject with an endoscope, identifies a tumor region and a non-tumor region from the medical image, generates a demarcation line that is a boundary between the tumor region and the non-tumor region, generates a virtual incision line at a position separated from the demarcation line by a designated distance, and performs control for superimposing the demarcation line and the virtual incision line on the medical image to be displayed.

A localized evaluation network incorporates a discriminator acting as classifier, which may be included within a generative adversarial network (GAN). GAN may include a generative network such as U-NET for creating segmentations. The localized evaluation network is trained on image pairs including medical images of organs of interest and segmentation (mask) images. The network is trained to distinguish whether an image pair does or does not represent the ground truth. GAN examines interior layers of the discriminator and evaluates how much each localized image region contributes to the final classification. The discriminator may analyze regions of the image pair that contribute to a classification by analyzing layer weights of the machine learning model. Disclosed embodiments include a visual attribute, such as a heat map, that represents contributions of localized regions of a contour to an overall confidence score. These localized regions may be highlighted and reported for quality assurance review.

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating scalable and semantically editable font representations utilizing a machine learning approach. For example, the disclosed systems generate a font representation code from a glyph utilizing a particular neural network architecture. For example, the disclosed systems utilize a glyph appearance propagation model and perform an iterative process to generate a font representation code from an initial glyph. Additionally, using a glyph appearance propagation model, the disclosed systems automatically propagate the appearance of the initial glyph from the font representation code to generate additional glyphs corresponding to respective glyph labels. In some embodiments, the disclosed systems propagate edits or other changes in appearance of a glyph to other glyphs within a glyph set (e.g., to match the appearance of the edited glyph).

A medical image processing device a reference image that is a medical image with which boundary line information related to a boundary line that is a boundary between an abnormal region and a normal region and landmark information related to a landmark that is a characteristic structure of the subject are associated and a captured image that is the medical image captured in real time, detects the landmark from the captured image, calculates a ratio of match between the landmark included in the reference image and the landmark included in the captured image, estimates a correspondence relationship between the reference image and the captured image on the basis of the ratio of match and information regarding the landmarks included in the reference image and the captured image, and generates a superimposition image in which the boundary line associated with the reference image is superimposed on the captured image on the basis of the correspondence relationship.