Technologies are provided to supply image assets for presentation at a client device. Some embodiments include a computing device that can determine that multiple image assets to be presented in a user interface are unavailable within a non-volatile storage device of the computing device. The computing device can then generate a request for an image sprite containing the multiple image assets, and can send the request to a content source platform. The request identifies the multiple image assets. The computing device can receive, from the content source platform, the image sprite and metadata corresponding to the image sprite. The metadata defines attributes of the multiple image assets. The computing device can store the metadata in the non-volatile storage device, and can extract, using the metadata, the multiple image assets from the image sprite. The computing device can present the multiple assets during presentation of the user interface.

An electronic device is provided. The electronic device includes a communication module, a camera module, a memory configured to store capability-related information about an external electronic device and information related to content, and a processor configured to be operatively connected to the communication module, the camera module, and the memory. The processor may generate at least one first content corresponding to a user using the camera module, may convert the at least one first content into at least one second content, based on the capability-related information about the external electronic device and the information related to the content, and may transmit the converted at least one second content to the external electronic device through the communication module.

A topographical identification and comparison system which converts other examples of the same kind or with parts of the examples of topographical structures having fixed references such as circular, rectangular and square, regular polygon to a digital data such as trace and wavy structural conditions of the surface areas and which carries out similarity comparison of this data by soft mathematical calculations.

A computer-implemented method of generating metadata from an image may comprise sending the image to an object detection service, which generates detections metadata from the image. The image may also be sent to a visual features extractor, which extracts visual features metadata from the image. The generated detections metadata may then be sent to an uncertainty score calculator, which computes an uncertainty score from the detections metadata. The uncertainty score may be related to a level of uncertainty within the detections metadata. The image, the visual features metadata, the detections metadata and the uncertainty score may then be stored in a database accessible over a computer network.

The Loupe system defines Loupe Visual Art DNA for art images to be presented to a user so as to maximize and customize the user experience in viewing art images delivered onto digital displays, TVs and other screens facilitating the artwork transition with and without human interaction. The Loupe system recommendations engine utilizes both human and machine curated data to determine factors of art images that will appeal to a user viewing the images. The Loupe system gathers data about visual perception, historical and academic provenance, and emotion or intention represented in an image. The gathered data is analyzed through deep learning and AI algorithms to inform recommendations and select art images to be presented to a user. The user may purchase fine art prints or select originals of the artwork image displayed, if the artist elects to make it available for sale, presented from the Loupe integrated electronic marketplace.

The texture data acquired by the first acquisition unit is processed so as to accord with a size of the region set by the setting unit, and the illumination data acquired by the second acquisition unit is processed so as to accord with the size of the region set by the setting unit. The decoration data is generated from the texture data processed by the first processing unit and the illumination data processed by the second processing unit, and the decoration data is applied to the region set by the setting unit are comprised.

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.

A process for locating real estate parcels for a user comprises accessing a library of parceled real estate image data to identify objects and features in a plurality of parcels identified by the user as having a feature of interest. A predictive model is constructed and applied to a geographic region selected by the user to generate a customized output of real estate parcels predicted to have the feature of interest.

A process for locating real estate parcels for a user comprises accessing a library of parceled real estate image data to identify objects and features in a plurality of parcels identified by the user as having a feature of interest. A predictive model is constructed and applied to a geographic region selected by the user to generate a customized output of real estate parcels predicted to have the feature of interest.

Methods and systems for automatically creating item records for physical items. The method may include receiving an image obtained using an image sensor; detecting a physical item in the image; extracting item data regarding the physical item by applying image analysis to the image; determining, using the extracted item data, whether a memory contains an item record for the physical item; and, when no item record for the physical item exists in the memory, generating and storing in the memory a new item record for the physical item in association with the extracted item data.