Provided are an illegal building identification method and apparatus, a device, and a storage medium, which relate to the field of cloud computing. The specific implementation scheme is: acquiring a target image and a reference image associated with the target image; extracting a target building feature of the target image and a reference building feature of the reference image, respectively; and determining, according to the target building feature and the reference building feature, an illegal building identification result of the target image.

An object detection device includes a processor. The processor is configured to apply a Hough transform to coordinate points on a UD map to detect a straight line having a predetermined length, and detect a target disparity corresponding to the detected straight line having the predetermined length as a disparity corresponding to an object parallel to a direction of travel of a stereo camera. In the UD map, a target disparity satisfying a predetermined condition among disparities acquired from a captured image is associated with coordinate points, each of the coordinate points having two-dimensional coordinates formed by a first direction and a direction corresponding to a magnitude of a disparity. The processor is configured to convert, in the Hough transform, a straight line passing through coordinate points associated with the target disparity and a predetermined range based on a vanishing point into a Hough space.

A method for extracting a fingerprint of a video having a plurality of frames includes obtaining a plurality of pixel value matrices from each of the plurality of frames, calculating maximum values of average pixel values in each axis of the plurality of pixel value matrices for each of the plurality of frames, and calculating the fingerprint of the video based on a temporal correlation of the maximum values calculated for the plurality of frames.

An object detection device that detects a specific object included in an input image includes a first candidate region specifying unit that specifies a first candidate region in which an object candidate is included from a first input image obtained by imaging a subject in a first posture, a second candidate region specifying unit that specifies a second candidate region in which an object candidate is included from a second input image obtained by imaging the subject in a second posture different from the first posture, a deformation displacement field generation unit that generates a deformation displacement field between the first input image and the second input image, a coordinate transformation unit that transforms a coordinate of the second candidate region to a coordinate of the first posture based on the deformation displacement field, an association unit that associates the first candidate region with the transformed second candidate region that is close to the first candidate region, and a same object determination unit that determines that the object candidates included in the candidate regions associated with each other by the association unit are the same object and are the specific object.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for extrinsic camera calibration using a calibration object. One of the methods includes: determining physical locations of interest points of a calibration object in a calibration object centered coordinate system; determining pixel locations of the interest points in an image of the calibration object captured by a camera; determining, using the pixel locations and the physical locations, a transformation from the calibration object centered coordinate system to a camera centered coordinate system; and determining, using the transformation, a camera tilt angle and a camera mount height of the camera for use in analyzing images captured by the camera.

A system and a method of classifying data and providing an accuracy of classification are described. The method includes determining values of statistical features associated with data packets present in a data stream. The values of statistical features are provided to a data model for producing a classification output including the data packets classified into one or more categories. While producing the classification output, the data model extracts heuristics for each of the values of statistical features, compares the heuristics with one or more conditional checks defined at each node within the data model, and determines a cumulative score based on results of the comparing. The cumulative score is determined by aggregating a score assigned to successful clearance of each conditional check. The cumulative score indicates an accuracy of the classification output.

A method is provided for machine vision and image analysis for recognizing an object in an electronic image, which is captured with the aid of an optical sensor. A reference image of the object to be recognized is trained during a learning phase and compared with the image of the scene during a working phase, the pattern comparison between the object and the scene takes place with the aid of a modified census transform, using a determination of maximum and which must exceed a threshold value for a positive statement on a degree of correspondence.

Provided are a device for a digital assay of targets according to an exemplary embodiment of the present disclosure and a method using the same. The digital assay method of targets according to the exemplary embodiment of the present disclosure includes acquiring an image for a plurality of microdroplets, predicting at least one region based on the image for the plurality of microdroplets using an artificial neural network-based prediction model configured to segment at least one region among positive microdroplets, negative microdroplets, and atypical microdroplets, with the image for the plurality of microdroplets as an input, determining a number for the plurality of microdroplets based on the at least one region, and providing quantitative data of targets based on the number for the plurality of microdroplets.

Systems and methods for assessing plotting device accuracy in aid of a process for qualifying plotter systems. The system and process involve an ideal (virtual) test pattern consisting of digital data defining a nominal grid augmented by geometric features (e.g., closed two-dimensional geometric shapes which respectively surround intersections or vertices of the nominal grid). The plotting device under test is commanded to print a test plot having a pattern that matches the test pattern, but the test plot may deviate from the test pattern. To measure the amount of deviation, first an image of the test plot on the printed medium is captured using an accurate optical scanner. Then a mathematical measurement process, implemented in a computer vision application (e.g., a software package), is employed to detect deviations of the test plot from the test pattern. A statistical analysis is then performed to determine whether the deviations are within specifications.

A three-dimensional data augmentation method includes that an original two-dimensional image and truth value annotation data matching the original two-dimensional image are acquired, that the original two-dimensional image and two-dimensional truth value annotation data are transformed according to a target transformation element to obtain a transformed two-dimensional image and transformed two-dimensional truth value annotation data, that an original intrinsic matrix is transformed according to the target transformation element to obtain a transformed intrinsic matrix, that a two-dimensional projection is performed on three-dimensional truth value annotation data according to the transformed intrinsic matrix to obtain projected truth value annotation data, and that three-dimensional augmentation image data is generated according to the transformed two-dimensional image, the transformed two-dimensional truth value annotation data, and the projected truth value annotation data.