An ophthalmic apparatus of an aspect example includes an image acquiring unit, a corneal shape estimating processor, and a first image correcting processor. The image acquiring unit is configured to acquire an anterior segment image constructed based on data collected from an anterior segment of a subject's eye by optical coherence tomography (OCT) scanning. The anterior segment image includes a missing part corresponding to a part of a cornea. The corneal shape estimating processor is configured to estimate a shape of the missing part of a cornea image by analyzing the anterior segment image acquired by the image acquiring unit. The first image correcting processor is configured to correct distortion of the anterior segment image based at least on the shape of the missing part estimated by the corneal shape estimating processor.

The present disclosure provides a method for optimizing map data, a device and a storage medium, and in particular relates to artificial intelligence, intelligent transportation, smart cities and smart cockpits. A specific implementation is: performing a completeness detection of a lane line on received local map data comprising road condition information to obtain a detection result, the detection result comprising the lane line being complete or is the lane line having a missing area; in a case where the detection result is the lane line having the missing area, performing completion processing on the lane line having the missing area to obtain completed local map data; and performing a rationality detection on the completed local map data, and in a case of passing the detection, synthesizing the completed local map data with global map data to obtain an optimization result of map data.

A method for determining wire regions of a circuit includes steps of: obtaining an original image containing multiple stick regions; processing the original image to obtain a first processed image containing multiple line segments; grouping the line segments into multiple groups corresponding respectively to the stick regions; generating a second processed image including multiple complete lines corresponding respectively to the groups; and generating a third processed image including multiple extended lines by extending the complete lines; and determining, for each of the extended lines in the third processed image, a rectangular region based on a stick region in the original image corresponding thereto.

An information processing device (100) is provided with a generation unit that acquires an input image serving as an intraoperative image and generates an output image based on whether the input image includes an intraoperatively generated matter or not.

The image restoration device has an initialization block that initializes the luminance value of each pixel coordinate to an intermediate value in the luminance array list that stores one of a pair of polarity values and intermediate values as the luminance value for each pixel coordinate. The image restoration device also has an update block that updates the initialized luminance array list according to the pixel coordinates and polarity values for each event, and an output block that outputs the luminance array list updated by the update block over the shooting period as a binary image. By the update performed in the update block, the luminance values of the firing coordinates where the event fired in the luminance array list are overwritten by the polarity values of the event. In addition, the update preserves the luminance values of the non-firing coordinates in the luminance array list, excluding the firing coordinates.

This machine-learning device is provided with: a detection unit which detects a loss of consistency with a lapse of time in a determination result for unit data, the determination result being output from a determination unit that generates a learning model to be used when performing prescribed determination for one or more pieces of the unit data that form time series data; and a selection unit which selects, on the basis of the result of detection by the detection unit, unit data to be used as teacher data when the determination unit updates the learning model, thereby efficiently raising the accuracy of the learning model when machine learning is performed on the basis of the time series data.

A hyperspectral infrared imaging system includes optical components, multi-color focal plane array or arrays, readout electronics, control electronics, and a computing system. The system measures a limited number of spatial and spectral points during image capture and the full dataset is computationally generated.

An image processing method includes obtaining a to-be-processed image, performing an image processing operation on the to-be-processed image by inputting the to-be-processed image into a corresponding image processing model to obtain a processed image, and obtaining an output image according to the processed image of the corresponding image processing model. The image processing operation includes at least one of color deviation removal processing or ghost effect removal processing. The image processing model corresponding to the color deviation removal processing is a first image processing model, and the image processing model corresponding to the ghost effect removal processing is a second image processing model.

A color image inpainting method includes: obtaining a color image of an object to be recognized, the color image including a missing portion where at least part of image information is missing; obtaining an infrared image of the object; identifying the missing portion in the color image; and inpainting the missing portion in the color image identified in the identifying. The inpainting includes inpainting the missing portion by using information which is obtained from the infrared image and corresponds to the missing portion to obtain an inpainted color image of the object.

A method for removing extraneous content in a first plurality of images, captured at a corresponding plurality of poses and a corresponding first plurality of times, by a first drone, of a scene in which a second drone is present includes the following steps, for each of the first plurality of captured images. The first drone predicts a 3D position of the second drone at a time of capture of that image. The first drone defines, in an image plane corresponding to that captured image, a region of interest (ROI) including a projection of the predicted 3D position of the second drone at a time of capture of that image. A drone mask for the second drone is generated, and then applied to the defined ROI, to generate an output image free of extraneous content contributed by the second drone.