Abstract: An image processing system detects changes in objects, such as damage to automobiles, by comparing a base object model, which depicts the object in an expected condition, to one or more target images of the object in the changed condition. The image processing system first processes a target object image to detect one or more predefined landmarks in the target object image and corrects for camera and positional distortions by determining a camera model for the target object image based on the detected landmarks.

Abstract: A vehicle image capture and labeling system operates to enable a user to capture vehicle photos, pictures, and/or images that are automatically labeled, i.e., annotated. In particular, the captured vehicle photos, pictures, or images are automatically labeled with certain vehicle identifier information, like a vehicle identification number (VIN), and pose information, that identifies a portion or view of the vehicle depicted within the image of the vehicle. The vehicle images may also be automatically labeled with one or more other image attributes or indicia, such as geospatial information corresponding to a location at which the photo or image was captured (e.g., global positioning system (GPS) data), time and date of image capture data, etc. The captured vehicle image and its label(s) may then be stored and used by other applications such as vehicle insurance claim applications, automobile repair estimate applications, etc.

Abstract: Techniques for determining or predicting re-inspection of a vehicle insurance claim are disclosed. The probability of an occurrence of a re-inspection of a claim (e.g., a re-inspection score) is determined by using a predictive re-inspection model generated based on a data analysis of historical claim data from a plurality of sources. The re-inspection score may be determined prior to a repair facility initially reviewing the claim or viewing the damage to the vehicle, e.g., at FNOL. Inputs to the predictive re-inspection model may include a settlement estimate, and optionally one or more other claim attributes that are strongly correlated to re-inspection. Other re-inspection information may be additionally or alternatively predicted by using the predictive re-inspection model. Candidate claims for re-inspection may be identified by ranking re-inspection scores and/or other re-inspection information.

Abstract: A vehicle image capture and labeling system operates to enable a user to capture vehicle photos, pictures, and/or images that are automatically labeled, i.e., annotated. In particular, the captured vehicle photos, pictures, or images are automatically labeled with certain vehicle identifier information, like a vehicle identification number (VIN), and pose information, that identifies a portion or view of the vehicle depicted within the image of the vehicle. The vehicle images may also be automatically labeled with one or more other image attributes or indicia, such as geospatial information corresponding to a location at which the photo or image was captured (e.g., global positioning system (GPS) data), time and date of image capture data, etc. The captured vehicle image and its label(s) may then be stored and used by other applications such as vehicle insurance claim applications, automobile repair estimate applications, etc.