A front-end image acquisition component acquires photographs and/or videos of the exterior of a vehicle traveling on a path between a first predefined location and a second predefined location. In one aspect, an image acquisition system comprises a first camera support structure, a second camera support structure, an image processor, and a memory. Cameras are affixed to each of the camera support structures for acquiring image data of a subject vehicle. Image data is controlled by the image processor and the memory. In another aspect, an image acquisition system comprises a first camera tower, a second camera tower, a camera boom. Cameras are affixed to each of the camera towers. In another aspect, a method of identifying features of the exterior of a vehicle includes acquiring a series of images from a front-end component, accessing previously acquired images and associated metadata stored in a database, executing machine learning algorithms using the previously acquired information to identify at least one exterior vehicle feature, and executing machine learning algorithms using the presently acquired series of images to identify at least one exterior feature of the vehicle.

A front-end image acquisition component acquires photographs and/or videos of the exterior of a vehicle traveling on a path between a first predefined location and a second predefined location. In one aspect, an image acquisition system comprises a first camera support structure, a second camera support structure, an image processor, and a memory. Cameras are affixed to each of the camera support structures for acquiring image data of a subject vehicle. Image data is controlled by the image processor and the memory. In another aspect, an image acquisition system comprises a first camera tower, a second camera tower, a camera boom. Cameras are affixed to each of the camera towers. In another aspect, a method of identifying features of the exterior of a vehicle includes acquiring a series of images from a front-end component, accessing previously acquired images and associated metadata stored in a database, executing machine learning algorithms using the previously acquired information to identify at least one exterior vehicle feature, and executing machine learning algorithms using the presently acquired series of images to identify at least one exterior feature of the vehicle.

A system for automated conversion and delivery of medical images. In an example implementation, a server is configured to retrieve a medical image file including medical data and metadata, in a medical data format, determine an output destination type based on the metadata, identify standardized format specifications based on the determined output destination type from the retrieved metadata, convert the medical data into a format compatible with the identified standardized format, and transmit an output message of the converted medical data to at least a recipient delivery address.

Systems, methods, devices, media, and computer readable instructions are described for local image tagging in a resource constrained environment. One embodiment involves processing image data using a deep convolutional neural network (DCNN) comprising at least a first subgraph and a second subgraph, the first subgraph comprising at least a first layer and a second layer, processing, the image data using at least the first layer of the first subgraph to generate first intermediate output data; processing, by the mobile device, the first intermediate output data using at least the second layer of the first subgraph to generate first subgraph output data, and in response to a determination that each layer reliant on the first intermediate data have completed processing, deleting the first intermediate data from the mobile device. Additional embodiments involve convolving entire pixel resolutions of the image data against kernels in different layers if the DCNN.

Aspects of the disclosure relate to validating map data using challenge questions. For instance, an attributes to be validated may be identified from the map data. At least one challenge question may be selected from a plurality of predetermined challenge questions based on the attribute. An image may be retrieved based on image information associated with the at least one challenge question. The image and the at least one challenge question may be provided for display. In response to the providing, operator input identifying an answer to the at least one challenge question may be received. This answer may be then used to validate the attribute.

Aspects of the disclosure relate to validating map data using challenge questions. For instance, an attributes to be validated may be identified from the map data. At least one challenge question may be selected from a plurality of predetermined challenge questions based on the attribute. An image may be retrieved based on image information associated with the at least one challenge question. The image and the at least one challenge question may be provided for display. In response to the providing, operator input identifying an answer to the at least one challenge question may be received. This answer may be then used to validate the attribute.

Techniques for formulating queries and retrieving relevant results for 3D components in a virtual or augmented reality system. In an aspect, a user works with a 3D component using a workflow, and views the 3D component from one or more selected perspective views. Data associated with the workflow and the selected perspective views are transmitted to an online engine. The online engine may include a query formulation module for automatically forming a query based on the received workflow data and selected perspective views. The formulated query may be supplied to a search engine to retrieve online results based on relevance to the formulated queries. One or more most relevant online results may be seamlessly served to the user as part of the workflow.

In an example implementation according to aspects of the present disclosure, a system, method, and storage medium for processing digital image file metadata characteristics. The system includes a processor and memory. The processor executes machine readable instructions to receive a set of digital image files from a first client application. The processor extracts a set of metadata from the set of digital image files. The processor identifies a set of common characteristics within the set of metadata. The processor groups the set of digital image files based on the set of common characteristics. The processor evaluates an association between the set of common characteristics and a second user. Responsive to the evaluation, the processor sends the grouped set of digital image files to a second client application.

In some embodiments, image input data is received from multiple sources. The received image input data may then be aggregated and mapped to create a set of image input data. An event in the set of image input data may be automatically detected, such as by being triggered by a rule and an associated tag. An image mining result database may be updated by adding an entry to the database identifying each detected event and the triggering rule. An indication associated with the image mining result database may then be transmitted to a plurality of risk applications.

In some embodiments, image input data is received from multiple sources. The received image input data may then be aggregated and mapped to create a set of image input data. An event in the set of image input data may be automatically detected, such as by being triggered by a rule and an associated tag. An image mining result database may be updated by adding an entry to the database identifying each detected event and the triggering rule. An indication associated with the image mining result database may then be transmitted to a plurality of risk applications.