A quality control method for remote fundus screening includes acquiring a fundus image of an examinee through a fundus camera by terminal application institution and transmitting the fundus image as well as personal information and inquiry data which are carried by the examinee to a remote interpretation and consultation center. The remote interpretation consultation center determines whether the fundus image, personal information, and consultation data are qualified; if the fundus image, personal information, and consultation data are unqualified, the remote interpretation consultation center terminates the interpretation. Thus clear and usable qualified fundus image is obtained during follow-up or screening of a patient and the examinee is not needed to go back and forth or repeat the examination multiple times, unnecessary expenses or time waste is saved, and the progress or screening rate of fundus screening is not affected, guaranteeing for the effective and smooth progress of remote fundus medical screening.

The present invention relates to fundus image processing field, especially a preprocessing method for quantitative analysis of fundus image, and storage device. A preprocessing method for quantitative analysis of fundus image, wherein the step includes, acquiring a to-be-processed fundus image; performing optic disk positioning on the to-be-processed fundus image; performing macular fovea positioning on the to-be-processed image; and calculating a quantization parameter of a distance between a center of the macular fovea and a bitamporal edge of the optic disk. Through this method, the data obtained is converted from absolute representation to relative representation, and through normalization, a meaningful and comparable quantification is formed between people, between different people, and even between inspection results of different instruments. analyze data. It ensures that fundus images from different sources can form meaningful and comparable quantitative indicators.

Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map.

A system for detection and identification of objects and obstacles near, between or on railway comprise several forward-looking imagers adapted to cover each different range forward and preferably to be sensitive each to different wavelength of radiation, including visible light, LWIR, and SWIR. The substantially homogeneous temperature along the rail the image of which is included in an imager frame assists in identifying and distinguishing the rail from the background Image processing is applied to define living creature in the image frame and to distinguish from a man-made object based on temperature of the body. Electro optic sensors (e.g. thermal infrared imaging sensor and visible band imaging sensor) are used to survey and monitor railway scenes in real time.

Method for determining at least one optimal insertion segment (810a, 810b, 810c, 810d) in a limb of a patient for inserting a needle into a vein of the patient, said segment (810a, 810b, 810c, 810d) being representative of an insertion point (820a, 820b, 820c, 820d), an insertion direction and a maximum insertion length, comprising a step of near-infrared illumination of the limb of the patient, a step of acquiring near-infrared images of the limb of the patient, a step of pre-processing the acquired images to obtain an image of the veins, a step of applying a linear structure detection filter to said image of the veins to obtain a vascular profile map, a step of binarizing the vascular profile map, a step of skeletonising the veins, a step of defining insertion segments from the skeletons of the veins, a step of classifying the insertion segments according to predetermined classification parameters.

Disclosed are a perspective method for a physical whiteboard and a generation method for a virtual whiteboard. A Hoffman straight-line detection method is used, statistics are taken on a quantity of overlapping times of a straight line, and a determining dimension of a whiteboard-related straight line is increased. On a basis of generating a high-precision virtual whiteboard, purity of a whiteboard color is improved through color enhancement, and a virtual whiteboard corresponding to each frame of a physical whiteboard image is processed based on a preset algorithm to obtain a background-color image, a motion map, and a chromatic aberration map, so as to obtain a foreground mask. A character is perspective and smoothed based on a foreground mask of a current frame, a color-enhanced image of the current frame, and a fully perspective image of a previous frame.

It is provided a recognition device for analyzing a starvation extent of a whiteleg shrimp based on underwater imaging, including a bracket, a camera mounted on a top of the bracket, a plurality of light sources for illumination, and a processor connected with the camera. The processor receives a shrimp image collected by the camera, extracts an edge image of the shrimp after the collected shrimp image is preprocessed, and calculates a movement speed of the shrimp and a quantity of baits after a head and a tail are recognized, to recognize starvation extent of the shrimp. It is also provided a recognition method for analyzing starvation extent of the whiteleg shrimp based on underwater imaging. The present disclosure may guide feeders to feed and realize reasonable culture for the shrimp, by capturing images of the shrimp in time and determining starvation the extent of shrimp.

The present disclosure relates to systems, methods, and non-transitory computer readable media for efficiently, quickly, and flexibly applying median filters to digital image utilizing a separable sorting network approach for computation sharing. For example, the disclosed systems generate a modified digital image by determining pixel values for a number of output tiles in applying a median filter. To generate output tiles, in some implementations, the disclosed systems utilize different forms of separability to precompute sorted columns of pixels to reduce the size of per-pixel tasks required to generate output pixels and to share computations among nearby pixels of an input tile captured from a digital image in generating output tiles. In some implementations, the disclosed systems utilize an interpreter to generate and apply a median filter to a digital image at runtime based on a user-selected filter size.

A circuitry for image demosaicing and contrast enhancement and an image-processing method thereof are provided. The circuitry includes a storage device that is used to temporarily store an image and is jointly used by circuits that perform color restoration and brightness reconstruction. The circuitry includes a color restoration circuit for performing image interpolation and a global mapping circuit that performs mapping to obtain brightness of an image according to restored red, green and blue information of every pixel. Further, an edge texture feature decision circuit is provided to obtain each pixel's directionality for color restoration. A brightness estimation circuit utilizes green information of the pixels as the brightness for an area. After that, a color image with the color restoration and brightness reconstruction is outputted.

Provided are a method and apparatus of embedding an image in video, and a method and apparatus of acquiring a plane prediction model, relating to the field of image processing. The method includes inputting a video frame image of a video into a plane prediction model to obtain a predicted plane mask of the video frame image, the plane prediction model being obtained by training a deep learning model using training images with labels having plane detection frames and plane masks; embedding the image to be embedded into the predicted plane mask of the video frame image.