A hub image retrieval method and a device thereof for improving the retrieval accuracy of an image containing a hub. The method includes: performing feature extraction on a hub image to be processed containing a target hub, and obtaining a hub feature to be processed containing N sub-hub features, wherein the N sub-hub features at least include information characterizing the features of a hub window of the target hub; determining similarities between candidate hub features of each candidate hub image and the hub features to be processed; and selecting a candidate hub image matching the hub image to be processed from the plurality of candidate hub images based on the determined respective similarities.

Embodiments relate to a method for updating query information for tracing a target object from multi-camera including receiving a query information update command including query information for tracing a target object from multi-camera, searching for at least one image displaying the target object among a plurality of images captured by the multiple cameras, and updating the query information of a query image based on the at least one found image, and a multi-camera system performing the same.

In one embodiment, a method includes accessing an image of a damaged object. The method further includes determining, using a plurality of image segmentation models, a plurality of objects in the image. The method further includes determining, using a plurality of visual inference models and the determined plurality of objects from the image segmentation models, a repair-relevant property vector for the damaged object in the image. The repair-relevant property vector includes a plurality of damaged object properties. The method further includes generating a repair report using the repair-relevant property vector and a price catalogue. The repair report includes an indication of the damaged object and a price associated with the repair or replacement of the damaged object. The method further includes providing the generated report for display on an electronic display device.

A method for controlling a near field communication between a device and a transaction card is disclosed. The method includes the steps of capturing, by a front-facing camera of the device, a series of images of the transaction card and processing each image of the series of images to identify a darkness level associated with a distance of the transaction card from the front of the device. The method includes comparing each identified darkness level to a predetermined darkness level associated with a preferred distance for a near field communication read operation and automatically triggering a near field communication read operation between the device and the transaction card for the communication of a cryptogram from an applet of the transaction card to the device in response to the identified darkness level corresponding to the predetermined darkness level associated with the preferred distance for the near field communication read operation.

A machine may be configured to execute a machine-learning process for identifying and understanding fine properties of various items of various types by using images and associated corresponding annotations, such as titles, captions, tags, keywords, or other textual information applied to these images. By use of a machine-learning process, the machine may perform property identification accurately and without human intervention. These item properties may be used as annotations for other images that have similar features. Accordingly, the machine may answer user-submitted questions, such as “What do rustic items look like?,” and items or images depicting items that are deemed to be rustic can be readily identified, classified, ranked, or any suitable combination thereof.

Image identification, retrieval, transformation and arrangement systems, methods, and computer-program products are configured to access a family tree of a user in a family tree database, identify one or more additional persons of interest in the family tree, determine whether the one or more persons of interest is associated with an image, retrieve the image, and transform the image of the one or more additional persons of interest with an image of the user or other person such as in an image arrangement template. Whether an image pertains to a person is determined using a machine learning classifier. A plurality of candidate lineages from a root or self node may be evaluated based on the number and/or quality of images associated therewith and/or based on filtering the one or more characteristics of the nodes in the candidate lineages.

A method comprising identifying a plurality of non-overlapping coarse domains of a region of interest; selecting a subset of the plurality of coarse domains based on a plurality of first diversity metrics determined for the plurality of coarse domains; identifying a plurality of non-overlapping fine domains of the region of interest, wherein each of the fine domains is a portion of one of the coarse domains of the selected subset of the plurality of coarse domains; selecting a subset of the plurality of fine domains based on a plurality of second diversity metrics determined for the plurality of coarse domains; and providing an indication of the selected subset of the plurality of fine domains.

Digital image data including discrete photographic images of a variety of different subjects, times, and so forth, are collected and analyzed to identify specific features in the photographs. In an embodiment of the invention, distinctive markers are distributed to aid in the identification of particular subject matter. Facial recognition may also be employed. The digital image data is maintained in a database and quarried in response to search requests. The search requests include criteria specifying any feature category or other identifying information, such as date, time, and location that each photograph was taken, associated with each photograph. Candidate images are provided for review by requesters, who may select desired images for purchase or downloading.

Various embodiments of systems and methods for image matching and retrieval process are disclosed.

In some implementations, a computing device may create a semantic mapping that includes identified features that appear in a particular percentage of assets in a subset of assets of a media library. Also, the computing device may analyze assets of the media library using the semantic mapping to generate semantic scores, which may be used to determine a first tier of assets from the media library that rate highest for semantic score out of all assets. The computing device may present at least one of the first tier assets prominently in a user interface when viewing assets of the media library.