Abstract: Techniques for optimizing vehicle license plate recognition in images and their decoding include training a set of convolutional neural networks (CNNs) by using images in which license plates are identified or labeled as a whole, rather than by license plate parts or key points, and rather than by the individual, segmented characters represented thereon. The trained CNNs may operate on target images of environments to localize images of license plates included therein and determine the issuing jurisdiction and/or ordered set of characters represented on detected license plates without utilizing character segmentation and/or per-character recognition techniques. As such, license plates depicted within target images are able to be detected and decoded with greater tolerances for lighting conditions, deformations or damages, occlusions, differing image resolutions, differing angles of capture, variations of other objects depicted within the images (such as dense or changing signage), etc.

Abstract: Intelligent vehicle repair estimating techniques include an image processing component that extracts image attributes from one or more images of a damaged vehicle, and utilizes the attributes to predict an initial set of parts that are globally-identified. Based on a jurisdiction associated with the damaged vehicle, the initial set of parts is transformed into a set of jurisdictionally-based repairs (e.g., parts, labor operations, time intervals, costs, etc.), which may be included in a draft vehicle repair estimate. An estimate refinement component iteratively modifies/refines the draft estimate using a machine-only loop nested within a larger human-machine loop, where system-generated modifications are incrementally incorporated into the draft within the smaller loop, and user-generated modifications are incrementally incorporated into the draft within the larger loop.

Abstract: Intelligent vehicle repair estimating techniques include an image processing component that extracts image attributes from one or more images of a damaged vehicle, and utilizes the attributes to predict an initial set of parts that are globally-identified. Based on a jurisdiction associated with the damaged vehicle, the initial set of parts is transformed into a set of jurisdictionally-based repairs (e.g., parts, labor operations, time intervals, costs, etc.), which may be included in a draft vehicle repair estimate. An estimate refinement component iteratively modifies/refines the draft estimate using a machine-only loop nested within a larger human-machine loop, where system-generated modifications are incrementally incorporated into the draft within the smaller loop, and user-generated modifications are incrementally incorporated into the draft within the larger loop.

Abstract: A method and system for analyzing images of a target vehicle according to a base image model corresponding to the target vehicle. According to certain aspects, the method and system receive a set of images from an electronic device and analyze the set of images to identify the target vehicle and a set of information associated with the target vehicle. The method and system may determine that the set of images does not meet a threshold criteria for further image analysis using a base image model corresponding to the target vehicle. In response, the method and system may generate and transmit a notification to the electronic device indicating that the set of images does not meet the threshold criteria.

Abstract: A driver assist design analysis system includes a processing system and a database that stores vehicle data, vehicle operational data, vehicle accident data, and environmental data related to the configuration and operation of a plurality of vehicles with driver assist systems or features. The driver assist design analysis system also includes one or more analysis engines that execute on the processing system to determine one or more driving anomalies (e.g., accidents or poor driving operation) based on the vehicle operational data, and that correlate or determine a statistical relationship between the driving anomalies and the operation of the driver assist systems or features.

Abstract: An image processing system that may be used to detect changes in objects, such as damage to automobiles, compares a base object model, which depicts the object in an expected condition, to one or more target object images of the object in the changed condition. To do so, the image processing system first processes a target object image to detect one or more predefined landmarks in the target object image using one or more correlation filters. The image processing system then uses the detected landmarks to determine a camera model for the target object image and uses the camera model to correct for camera distortions and to align the target object depicted in a target object image with the object in the base object model to put these objects in a common frame of reference for use in subsequent image processing.

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: An image processing system detects changes in an object, such as damage to an automobile, by comparing a base object model, which depicts the object in an expected condition, to one or more target object images depicting the object in the changed condition. The image processing system aligns the object, as depicted in the base object model, with the object as depicted in a target object image. The image processing system then determines contours of the target object within the target object image by overlaying the aligned base object model with the target object image, and removes background pixels or other extraneous information based on this comparison. The image processing system may also determine various different components, such as body panels of an automobile, based on this overlay.

Abstract: An image processing system automatically processes a plurality of images of a damaged vehicle and determines replacements parts that are needed to repair the vehicle therefrom. The system includes a network interface via which the images are received, an image extraction component that generates a set of image attributes from the received images, a parts identifier component that generates indications of the needed replacement parts based on the image attributes and an information identification model, and an output interface via which the generated indications are provided. Additionally, the system includes a data storage entity storing data from multiple historical vehicle insurance claims, and a model generation component that generates the information identification model based on the historical claim data.

Abstract: An image processing system includes a first image processor that creates a composite 3-D image or model of a damaged vehicle from a plurality of 2-D images of the damaged vehicle. The first image processor or another image processor of the system generates deformation data that is indicative or descriptive of points/areas of damage on the vehicle based on a comparison of the composite 3-D image/model with a 3-D image/model of a pristine vehicle. Based on the deformation data, a deformation model component generates indications of replacement parts and/or other items corresponding to repairing the vehicle by utilizing a model generated from historical claim data. Aside from receiving the initial set of images of the damaged vehicle, the system does not require any user input, and may generate the indications of replacement parts and/or other items corresponding to repairing the vehicle in real-time, such as at FNOL.

Abstract: An image processing system automatically processes a plurality of images of a damaged vehicle and determines replacements parts that are needed to repair the vehicle therefrom. The system includes a network interface via which the images are received, an image extraction component that generates a set of image attributes from the received images, a parts identifier component that generates indications of the needed replacement parts based on the image attributes and an information identification model, and an output interface via which the generated indications are provided. Additionally, the system includes a data storage entity storing data from multiple historical vehicle insurance claims, and a model generation component that generates the information identification model based on the historical claim data.

Abstract: A system for analyzing images of objects such as vehicles. According to certain aspects, the system includes a user interface device configured to capture a set of images depicting a target vehicle, and transfer the set of images to a server that stores a set of base image models. The server analyzes the set of images using a base image model corresponding to the target vehicle, a set of correlational filters, and a set of convolutional neural networks (CNNs) to determine a set of changes to the target vehicle as depicted in the set of images. The server further transmits, to the user interface device, information indicative of the set of changes for a user to view or otherwise access.

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: An image processing system and/or method obtains source images in which a damaged vehicle is represented, and performs image processing techniques to determine, predict, estimate, and/or detect damage that has occurred at various locations on the vehicle. The image processing techniques may include generating a composite image of the damaged vehicle, aligning and/or isolating the image, applying convolutional neural network techniques to the image to generate damage parameter values, where each value corresponds to damage in a particular location of vehicle, and/or other techniques. Based on the damage values, the image processing system/method generates and displays a heat map for the vehicle, where each color and/or color gradation corresponds to respective damage at a respective location on the vehicle. The heat map may be manipulatable by the user, and may include user controls for displaying additional information corresponding to the damage at a particular location on the vehicle.

Abstract: A system includes a parts prediction model module. The parts prediction model module causes the system to, based on information obtained via a network interface and indicative of damage to a specific vehicle, filter vehicle parts data to obtain filtered vehicle parts data that is indicative of a subset of a plurality of parts that were utilized to repair a subset of a plurality of vehicles. The parts prediction model module further causes the system to obtain an indication of at least one specific part determined as being needed to repair the damage to the specific vehicle, and analyze the filtered vehicle parts data to determine one or more recommended parts based on the indication. Still further, the parts prediction model module causes an indication of the one or more recommended parts to be transmitted to at least one of a remote computing device or a user interface.

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.

Abstract: A system for preparing vehicle repair estimates and sourcing parts for vehicles includes a processor, a display in communication with the processor, and an input device in communication with the processor. The display is configured to display information generated by the processor, while the input device is configured to receive inputs from a user and transmit the inputs to the processor. The processor is configured to identify records in a first electronic database that relate to a particular vehicle, identify records in a second {or multiple} electronic database relating to a source of parts for the particular vehicle, display a repair estimate illustrating recommended parts and part sources on the display, and receive user input identifying parts other than recommended, or identifying a different source for a recommended part, and automatically updating the estimate and notification of guideline exceptions.