The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle including preserving the quality of the input images of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle, including preserving the quality and/or resolution of the images of the damaged vehicle.

A method of identifying a bale of material includes capturing an initial image of a region of an initial bale with an initial image sensor and identifying a unique band pattern of at least one multi-colored band of the initial bale. The identified unique band pattern is associated with the initial bale. Data related to the initial bale is associated with the identified unique band pattern. The identified unique band pattern of the initial bale may then be used as an identifier of the initial bale. A second image of the bale is captured and analyzed to determine if the second image includes the unique band pattern of the at least one multi-colored band, thereby identifying the bale in the second image as the initial bale. The data associated with the initial bale may then be retrieved by an operator.

One variation of a method for automatically registering and distributing modular computer component information and services through a network includes: accessing a device inventory identifying a set of modular computer components installed in a computing device; identifying a first modular computer component for installation in the first computing device, acquired from an online marketplace; identifying a second modular computer component within the first device, substitutable by the first modular computer component. In response to detecting substitution of the second modular computer component by the first modular computer component, in the computing device, appending the first modular computer component to the first device inventory. Additionally, identifying a third modular computer component combinable with the second modular computer component to complete a second computing device and generating a prompt to acquire the third modular computer component, or, alternatively, generating a prompt to list the second component for sale on the online marketplace.

The present disclosure relates generally to the processing of machine-readable visual encodings in view of contextual information. One embodiment of aspects of the present disclosure comprises obtaining image data descriptive of a scene that includes a machine-readable visual encoding; processing the image data with a first recognition system configured to recognize the machine-readable visual encoding; processing the image data with a second, different recognition system configured to recognize a surrounding portion of the scene that surrounds the machine-readable visual encoding; identifying a stored reference associated with the machine-readable visual encoding based at least in part on one or more first outputs generated by the first recognition system based on the image data and based at least in part on one or more second outputs generated by the second recognition system based on the image data; and performing one or more actions responsive to identification of the stored reference.

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

Provided are a method and an apparatus for restoring an object image, capable of restoring an image naturally by detecting positions of landmarks of an object in a bounding-box detected from an input image, performing warping to align the object at a central position or a reference position on the basis of the landmarks, improving the image using a learning model learned from the aligned object image, performing inverse warping for rotating the improved object image in an original direction or at an original angle, and inserting the inversely-warped object image into the input image. In addition, provided are a method and an apparatus for restoring an object image, capable of detecting positions of landmarks of an object in a bounding-box detected from an input image, performing pose estimation for a side object on the basis of the landmarks, and improving an image using a learning model learned from a side object image corresponding to the pose estimation result.

Provided are a method and a device for restoring a facial image, capable of restoring an image naturally by detecting positions of landmarks of a face in a bounding-box detected from an input image, improving an image using a learning model learned from a front facial image after performing warping for aligning a front face to be positioned at a central position or a reference position on the basis of the landmarks, performing inverse warping for rotating the improved image in an original direction or at an original angle, and inserting the inversely-warped image into the input image. In addition, provided are a method and a device for restoring a facial image, capable of performing pose estimation for a face in a bounding-box detected from an input image, and improving the image using a learning model learned from a side facial image corresponding to a result of the pose estimation.

A fingerprint image display means displays an input fingerprint image being a fingerprint image which is input and a registered fingerprint image being a fingerprint image which has been registered. A marker acquisition means acquires a position of an input marker which is input by a user with respect to the input fingerprint image being displayed. A marker applying means applies a correspondence marker at a position corresponding to an input marker in the registered fingerprint image. A matching means matches the input fingerprint image to which the input marker is input with the registered fingerprint image to which the correspondence marker has been applied.

Systems, methods and techniques for automatically recognizing two or 3-dimensional real world objects with an augmented reality display device (smartphone or glasses etc.), and augmenting or enhancing the display of such real world objects by superimposing virtual images such as a still or video advertisement, an opportunity to buy, a story or other virtual image presentation. In non-limiting embodiments, the real world object includes visible features including visible security features and a recognition process takes the visible security features into account when recognizing the object and/or displaying superimposed virtual images.

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; a display unit; and a processor configured to, by executing a program: display a guide of a bright spot portion of the light source unit on the photographed image on the display unit so that an angle formed by the light source unit, the camera unit, and the collation area of the object is a predetermined angle.