A method for identifying an alteration of an object in the vicinity of a train in operation travelling on a rail track along a route. The train is operative to transport passengers or cargo. The method includes capturing image data of an object located at a point of interest along said route when said train is operational on said rail track. Furthermore, said image data is analyzed to find matching reference image data stored in a data storage device. Based on said matching reference image data, said object is located at a position within said point of interest. Further, said image data is analyzed to find an alteration of said object compared to said matching reference image data. If an alteration is found, an action depending may be taken.

Technology for matching images (for example, video images, still images) of an identical infrastructure object (for example, a tower component of a tower supporting power lines) for purposes of comparing the infrastructure object to itself at different points in time to detect a potential anomaly and the potential need for maintenance of the infrastructure object. In some embodiments, this matching of images is done using creation of a three dimensional (# D) computer model of the infrastructure object and by tagging captured images with location on the 3D model across multiple videos taken at different points in time.

Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.

The various embodiments described herein include methods, devices, and systems for coupling wireless devices. In one aspect, a method includes receiving, at a server, an image captured by a first electronic device; obtaining, based at least in part on the received image, connection information for facilitating a wireless connection between the first electronic device and a second electronic device; and transmitting to the first electronic device the obtained connection information for facilitating a wireless connection between the first electronic device and a second electronic device.

An automated process to determine whether an image has been modified includes receiving an image (e.g., via a web portal), requesting an image validation service to analyze the image to determine whether the image and/or a subject depicted in the image, has been modified from its original form and, based on the analysis of the image validation service, outputting a likelihood that the image has been modified. The image validation service may analyze the image using one or more operations to determine a likelihood that the image has been modified, and provide an indication of the likelihood that the image has been modified to the web portal. The indication of the likelihood that the image has been modified may be presented on a display via the web portal, and various actions may be suggested or taken based on the likelihood that the image has been modified.

A method for selecting important digital images in a collection of digital images, comprising: analyzing the digital images in the collection of digital images to identify one or more sets of similar digital images; identifying one or more sets of similar digital images having the largest number of similar digital images; selecting one or more digital images from the identified largest sets of similar digital images to be important digital images; and storing an indication of the selected important digital image in a processor accessible memory.

Methods, systems, and computer readable media are provided for identifying photos such as selfies in which the individuals in the photo are in imminent danger. One or more profiles are generated, each profile associated with a corresponding first user of a networked system and pertaining to problematic conditions for image capture. An image of a second user is captured via an image capture device. The captured image of the second user is uploaded to the networked system. The captured image of the second user is compared to the generated one or more profiles to determine a score value for the captured image of the second user. A score value may be generated based upon a combination of scores from one or more of geolocation information, metadata analysis, and image processing and machine learning. The upload of the captured image of the second user is rejected in response to the score value satisfying a threshold indicating a presence of problematic conditions during capturing of the image of the second user.

The disclosure relates to a method, apparatus and computer-readable medium for fingerprint identification. The method includes detecting a contact of an object with a fingerprint identification area of a terminal, wherein the contact of the object covers a contact area on the fingerprint identification area; acquiring characteristic information of the contact area; determining whether the contact area includes a fingerprint based on the characteristic information; performing fingerprint identification when it is determined that the contact area includes the fingerprint; and maintaining a sleep state when it is determined that the contact area does not include the fingerprint.

An augmented reality (AR) client terminal identifies initial location information for a location of a user. A scene picture associated with the location of the user is captured. Image matching is performed between the captured scene picture and scene pictures in a scene picture set. The scene picture set is generated based on the initial location information. Each scene picture in the scene picture set is associated with detailed location information. In response to the captured scene picture matching a particular scene picture in the scene picture set, refined location information for the location of the user is determined based on the detailed location information of the particular scene picture.

A first and second image of a scene are captured. Each of a plurality of pixels in the first image is associated with a disparity value. An image patch associated with each of the plurality of pixels of the first image and the second image is mapped into a binary vector. Thus, values of pixels in an image are mapped to a binary space using a function that preserves characteristics of values of the pixels. The difference between the binary vector associated with each of the plurality of pixels of the first image and its corresponding binary vector in the second image designated by the disparity value associated with each of the plurality of pixels of the first image is determined. Based on the determined difference between binary vectors, correspondence between the plurality of pixels of the first image and the second image is established.