The present disclosure provides geological linear body extraction method based on tensor voting coupled with Hough transformation, including pre-processing a remote sensing image to obtain a pre-processed remote sensing image; selecting three optimal wavebands from N multi-spectral wavebands of the pre-processed remote sensing image, so as to obtain a remote sensing image combined by the optimal wavebands, N being a natural number greater than or equal to 3; using Gaussian high-pass filtering to perform sharpening processing on the remote sensing image combined by the optimal wavebands, so as to enhance linearized edge information; performing edge detection on the remote sensing image having enhanced linearized edge information, so as to obtain all edge points in the remote sensing image; and converting all the edge points in the remote sensing image from an image coordinate system to a parameter coordinate system, and extracting a geological linear body from the parameter coordinate system.

The present invention provides a system and a method for assisting in the diagnosis of diseases from otolaryngology images that comprises: an apparatus for the acquisition of images of otolaryngologic endoscopy; a processor, operatively connected to said apparatus for the acquisition of otolaryngologic endoscopy images; and a user interface comprising a screen, said user interface operatively connected to said processor; wherein said processor is configured to: recognize a type of otolaryngologic endoscopic examination apparatus to which said apparatus belongs; obtain a plurality of images of otolaryngologic endoscopy from said apparatus; display said plurality of images on said screen; and identify, from said plurality of images, whether the same corresponds to any disease or to a healthy patient.

A method of determining a steering angle of an aircraft landing gear including: obtaining an image of the aircraft landing gear; performing edge detection on the image; determining, based at least in part on an edge obtained by the edge detection, a relative position of a component of the aircraft landing gear; and determining, based at least in part on the relative position of the component, the steering angle of the aircraft landing gear.

A method for detecting defects on a sample based on a defect inspection apparatus is provided. In the method, an image data set that includes defect data and non-defect data is organized. A convolutional neural network (CNN) model is defined. The CNN model is trained based on the image data set. The defects on the sample are detected based on inspection data of the defect inspection apparatus and the CNN model. The sample includes uniformly repeating structures, and the inspection data of the defect inspection apparatus is generated by filtering out signals of the uniformly repeating structures of the sample.

A method of identifying a rip current that includes receiving an image comprising a body of water, and identifying, in the image an estimated location of a rip current associated with the body of water, wherein the identification comprises a plurality of displayed vertices based on least one of a rip length or a rip width. The method may include digitizing the estimated location of the rip current, the estimated location having highest, lower, and no confidence boundaries. The method may include generating, based on the digitized estimated location, a set of fuzzy-set scheme labels for one or more pixels in the image based on a respective confidence boundary, where the fuzzy-set scheme labels are based on a pixel interpolation associated with the lower confidence boundary, and generating, based on the generated fuzzy-set scheme labels, a refined digitizing of the estimated location of the rip current.

Provided is a border detection method, server, and storage medium. The method including detecting a plurality of first straight line segments in a to-be-detected image, the to-be-detected image comprising a target region of a to-be-determined border; generating a plurality of first candidate borders of the target region according to the plurality of first straight line segments; obtaining a plurality of second candidate borders of the target region from the plurality of first candidate borders; extracting border features of the plurality of second candidate borders; and obtaining an actual border of the target region from the plurality of second candidate borders according to the border features of the plurality of second candidate borders and a border detection model, the border detection model being used to determine a detected value of each candidate border, and the detected value representing a similarity between each candidate border and the actual border.

An object is to make it possible to precisely infer a geometric transformation matrix for transformation between an image and a reference image representing a plane region even if correspondence to the reference image cannot be obtained. A first line segment group extraction unit 120 extracts, out of a line segment group in an image, line segments that correspond to a direction that is parallel or perpendicular to a side of a rectangle included in the image, from the inside of the rectangle, takes the extracted line segments to be a first line segment group, and extracts a plurality of line segments different from the first line segment group out of the line segment group. An endpoint detection unit 150 detects four intersection points between ends of the image and two line segments that are selected from line segments that correspond to a direction that is parallel or perpendicular to a side of the rectangle and are extracted from a plurality of line segments obtained by transforming the different line segments using an affine transformation matrix in which an angle of the first line segment group relative to a reference direction of the image is used as a rotation angle. A homography matrix inferring unit 160 computes a geometric transformation matrix based on the affine transformation matrix and a homography matrix computed based on correspondence between the four intersection points and the four vertexes of the rectangle in a reference image.

A method for detecting defects on a sample based on a defect inspection apparatus is provided. In the method, an image data set that includes defect data and non-defect data is organized. A convolutional neural network (CNN) model is defined. The CNN model is trained based on the image data set. The defects on the sample are detected based on inspection data of the defect inspection apparatus and the CNN model. The sample includes uniformly repeating structures, and the inspection data of the defect inspection apparatus is generated by filtering out signals of the uniformly repeating structures of the sample.

The present invention relates to the field of intelligent driving. Disclosed is a method for detecting the position relation between a vehicle and a lane line. The method for detecting the position relation between a vehicle and a lane line comprises: obtaining a vehicle model, the vehicle model being represented by a plurality of first coordinates in a world coordinate system; obtaining a lane line image, the lane line image being captured by a camera disposed on a vehicle; obtaining a calibration parameter of the camera; determining, according to the lane line image and the calibration parameter, a first line segment of a lane line mapped into the world coordinate system; and determining the position relation between the lane line and the vehicle according to the position relation between the first line segment and the plurality of first coordinates in the world coordinate system. According to the detection method, the position relation between the lane line and the vehicle can be determined without using a positioning system, so that the construction cost of intelligent driving is reduced.

Some examples herein describe time-series recognition and analysis techniques with computer vision. In one example, a system can access an image depicting data lines representing time series datasets. The system can execute a clustering process to assign pixels in the image to pixel clusters. The system can generate image masks based on attributes of the pixel clusters, and identify a respective set of line segments defining the respective data line associated with each image mask. The system can determine pixel sets associated with the time series datasets based on the respective set of line segments associated with each image mask, and provide one or more pixel sets as input for a computing operation that processes the pixel sets and returns a processing result. The system may then display the processing result on a display device or perform another task based on the processing result.