A collation device includes a light source unit; a camera unit that receives light emitted from the light source unit and reflected in a collation area of an object to acquire a photographed image of the collation area; and a processor configured to, by executing a program: detect a positional relationship between the light source unit and the camera unit by using the photographed image, and notify of a collation result between the photographed image and a registered image prepared in advance by using the positional relationship.

A method for detecting a three-dimensional object in a two-dimensional image includes: inputting the two-dimensional image into an object detection model, and obtaining a resulting detection depth dataset; obtaining, based on the detection depth dataset, coordinate sets of a number of points-of-interest each associated with a to-be-detected object in a 3D camera centered coordinate system; and converting the coordinate sets of the number of points-of-interest in the 3D camera centered coordinate system into a number of coordinate sets in a 3D global coordinate system. Embodiments of this disclosure may be utilized in the field of self-driving cars with roadside traffic cameras.

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.

Embodiments of the present invention provide a system for determining a location of a tow hitch (280) mounted to a vehicle (250), comprising imaging means (260) disposed in relation to the vehicle to output image data corresponding to an image (105), and processing means (200, 210) arranged to receive the image data (270), wherein the processing means is arranged to select image data corresponding to a region (310) of the image and to search within the selected data for image data corresponding to the tow hitch (280) to determine the location of the tow hitch within the image.

A medical support apparatus has one processor or more, and the processor is configured to: acquire a medical image; and based on the medical image, generate a guidance display that indicates a boundary between segments whose recommendation levels for medical treatment are different in the medical image and that is to be superimposed on the medical image.

An edge detection method, an edge detection device, an electronic apparatus, and a storage medium are provided. The method includes: processing an input image to obtain a line drawing of grayscale contours, where the input image includes an object with edges, and the line drawing includes lines; merging the lines to obtain reference boundary lines; processing the input image to obtain boundary regions corresponding to the object; for each of the reference boundary lines, comparing the reference boundary line with the boundary regions, calculating a number of pixels on the reference boundary line belonging to the boundary regions to serve as a score of the reference boundary line to determine a plurality of scores corresponding to the reference boundary lines one-to-one; determining target boundary lines according to the reference boundary lines, the scores, and the boundary regions; and determining edges of the object according to the target boundary lines.

An inspection apparatus (100) detects an inspection object (90) from first image data (10) in which the inspection object (90) is included. The inspection apparatus (100) generates second image data (20) by performing a geometric transform on the first image data (10) in such a way that a view of the detected inspection object (90) becomes a view satisfying a predetermined reference. In an inference phase, the inspection apparatus (100) detects, by using an identification model for detecting an abnormality of the inspection object (90), an abnormality of the inspection object (90) included in the second image data (20). Further, in a learning phase, the inspection apparatus (100) learns, by using the second image data (20), an identification model for detecting an abnormality of the inspection object (90).

A method for restoring video data of a pipe based on computer vision is provided. The method includes: performing gray stretching on pipe image/video collected by a pipe robot; processing noise interference by smoothing filtering; extracting an iron chain from the center of a video image as a template for location; performing target recognition on the center of video data by an SIFT corner detection algorithm; detecting ropes on left and right sides of a target by Hough transform; performing gray covering on the iron chain at the center of the video image and the ropes on two sides; and restoring data by an FMM image restoration algorithm.

Example methods and systems train an end-to-end neural network machine to analyze images of lateral flow assay test strips by learning non-linear interactions among lighting variations, test strip reflections, bi-directional reflectance distribution functions, angles of imaging, response curves of smartphone cameras, or any suitable combination thereof. Such example methods and systems improve the limit of detection, the limit of quantification, and the coefficient of variation in the precision of quantitative test results, under ambient light settings.

The techniques and device provided herein relate to receiving, via a processor, image data representative of a borehole of a well. The technique may include generating dequantized image data based on the image data, such that the dequantized image data filters one or more artifacts present in a Hough transformed version of the image data. One or more dip orientations (inclination and azimuth) associated with one or more formation dips present in the image data may be determined based on the dequantized image data. The technique may also include performing an a-contration validation algorithm for for the one or more formation dips to verify whether at least a formation dip having the or one of the possible dip orientation is present at a predetermined measured depth in the image data..