The present disclosure provides a method and an apparatus for processing a lane line, and relates to the field of data processing and, in particular, to the fields of intelligent transportation, Internet of Vehicles and intelligent cockpit. A specific implementation scheme is: obtaining a lane edge line of a road and a lane dividing line of the road according to point cloud data and image information of the road; acquiring breakpoints of the lane edge line, and acquiring breakpoints of the lane dividing line; completing the lane edge line according to the breakpoints of the lane edge line, to obtain a continuous lane edge line; completing the lane dividing line according to the breakpoints of the lane dividing line and the continuous lane edge line, to obtain a continuous lane dividing line.

A method, system, and computer program product for classifying input data of a machine learning model. The method includes obtaining a dataset. The method also includes determining pairwise correlations of the set of features using their values in the dataset. The method also includes selecting one or more pairs of features that are highly correlated. The method also includes creating a density map that contains a set of points. The method also includes determining a low-density area on the density map having a low-density of points from the density analysis. The method also includes identifying records of the dataset that belong to the determined low-density areas. The method also includes labeling the identified records as low-density and labeling the remaining records of the dataset as high-density. The method also includes training a classifier to classify an input record having the set of features as a low-density or high-density record.

Technologies are provided for assigning developers to source code issues using machine learning. A machine learning model can be generated based on multiple versions of source code objects (such as source code files, classes, modules, packages, etc.), such as those that are managed by a version control system. The versions of the source code objects can reflect changes that are made to the source code objects over time. Associations between developers and source code object versions can be analyzed and used to train the machine learning model. Patterns of similar changes to various source code objects can be detected and can also be used to train the machine learning model. When an issue is detected in a version of a source code object, the model can be used to identify a developer to assign to the issue. Feedback data regarding the developer assignment can be used to re-train the model.

A computer that identifies the video. The computer annotates the video using a deep learning tool. The computer analyzes the annotated video to highlight a dangerous condition. The computer identifies a video from a repository with the dangerous condition. The computer analyzes the video and the video from the repository using a similarity analysis. The computer determines a score based on the annotated video and based on comparing the video to the video from the repository and based on determining the score is above a threshold value, the computer generates an action.

Wafer-to-wafer and within-wafer image contrast variations can be identified and mitigated by extracting an image frame during recipe setup and then during runtime at the same location. Image contrast is determined for the two image frames. A ratio of the contrast for the two image frames can be used to determine contrast variations and focus variation.

A method to perform a well maintenance operation is disclosed. The method includes obtaining, using a downhole camera, a downhole image comprising a pixel-based dimension of an object inside the wellbore, generating, using a laser distance pointer, a distance measurement of the object with respect to the downhole camera, determining, based on the distance measurement, a physical dimension of the object from the pixel-based dimension of the object, and performing the well maintenance operation based at least on the physical dimension of the object.

An image processing system comprises a template matching engine (TME). The TME reads an image from the memory; and as each pixel of the image is being read, calculates a respective feature value of a plurality of feature maps as a function of the pixel value. A pre-filter is responsive to a current pixel location comprising a node within a limited detector cascade to be applied to a window within the image to: compare a feature value from a selected one of the plurality of feature maps corresponding to the pixel location to a threshold value; and responsive to pixels for all nodes within a limited detector cascade to be applied to the window having been read, determine a score for the window. A classifier, responsive to the pre-filter indicating that a score for a window is below a window threshold, does not apply a longer detector cascade to the window before indicating that the window does not comprise an object to be detected.

A face verification method and apparatus is disclosed. The face verification method includes selecting a current verification mode, from among plural verification modes, to be implemented for the verifying of the face, determining one or more recognizers, from among plural recognizers, based on the selected current verification mode, extracting feature information from information of the face using at least one of the determined one or more recognizers, and indicating whether a verification is successful based on the extracted feature information.

The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium. The method comprises: acquiring an iris image group comprising at least two iris images to be compared; detecting iris locations in the iris images and segmentation results of iris areas in the iris images; performing multi-scale feature extraction and multi-scale feature fusion on an image area corresponding to the iris locations, to obtain iris feature maps corresponding to the iris images; performing comparison using the segmentation results and the iris feature maps respectively corresponding to the at least two iris images, and determining whether the at least two iris images correspond to the same object based on a comparison result of the comparison. Embodiments of the present disclosure realize accurate comparison of iris images.

A memory device is provided including instructions that, when executed, cause one or more processors to perform the steps including receiving a plurality of images acquired by a camera, the plurality of images including a plurality of optical patterns, wherein an optical pattern of the plurality of optical patterns encodes an object identifier. The steps include presenting the plurality of images comprising the plurality of optical patterns on a display, and presenting a plurality of visual indications overlying the plurality of optical patterns in the plurality of images. The steps also include identifying a selected optical pattern of the plurality of optical patterns based on a user action and a position of the selected optical pattern in one or more of the plurality of images. The steps also include decoding the selected optical pattern to generate the object identifier and storing the object identifier in a second memory device.