Entities such as hotels, restaurants, resorts, houses, vehicles, and other places and things, are associated with images of each entity. The images are tagged using machine learning to identify what aspects of the associated entity are captured by each image. When a user is requested to select an entity from a set of entities, a user preference model is used to determine what tags the user is interested in. The tags are used to select images associated with the entities from the set of entities, and the selected images are displayed to the user. The user can then provide indicators that show which of the displayed images the user likes or dislikes. Based on the indicators, one or more entities from the set of entities is presented to the user. The model may also be updated based on the indicators.

Augmented reality, computer vision, and digital ticketing system techniques are described that employ a location determination system. In one example, the location determination system is configured to receiving at least one digital image as part of a live camera feed, identify an object included in the at least one digital image using object recognition, determine a location of the object in relation to a digital map of a physical environment, generate augmented reality digital content indicating the determined location in relation to the digital map, and render the augmented reality digital content as part of the live camera feed for display by a display device.

Disclosed are a method and device for archive application, and a storage medium. The method for archive application includes: acquiring target time and space range information; determining, based on the target time and space range information, at least one target clustered archive in a clustered archive library, wherein the at least one target clustered archive matches the target time and space range information, each of the at least one target clustered archive includes archive information of a respective acquisition object, and the archive information includes activity time and space range information of the respective acquisition object; and performing a predetermined operation based on the at least one target clustered archive.

An information processing apparatus determines whether or not one or more image groups for which it is necessary to perform predetermined display in a case where display is performed on a display unit are stored in an image file, selects, in a case where it is determined that one or more such image groups are stored in the image file, (i) a representative image or (ii) an image group of the stored one or more image group based on a user operation, and causes the display unit to display the selected representative image or images included in the selected image group.

Techniques described herein provide intelligent context-based testing. One or more implementations receive an image that includes content. In turn, some implementations process the image to extract test information from the content, such as questions, answers, learning material, and so forth. By analyzing the test information, various implementations determine one or more characteristics associated with the test information, and dynamically generate new test information based on the determined one or more characteristics. As one example, some implementations obtain new content by searching for content that includes the one or more characteristics and generate new test information based on the new content and the extracted test information.

Techniques described herein provide intelligent context-based testing. One or more implementations receive an image that includes content. In turn, some implementations process the image to extract test information from the content, such as questions, answers, learning material, and so forth. By analyzing the test information, various implementations determine one or more characteristics associated with the test information, and dynamically generate new test information based on the determined one or more characteristics. As one example, some implementations obtain new content by searching for content that includes the one or more characteristics and generate new test information based on the new content and the extracted test information.

Described herein are systems and methods for capturing images of a field and performing agricultural data analysis of the images. In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural image data including images of at least one stage of crop development that are captured with at least one of an apparatus and a remote sensor moving through a field. The computer includes at least one processing unit that is coupled to the database. The at least one processing unit is configured to execute instructions to analyze the captured images, to determine relevant images that indicate a change in at least one condition of the crop development, and to generate a localized view map layer for viewing the field at the at least one stage of crop development based on at least the relevant captured images.

Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically selecting the most definitive, precise and high-quality content files corresponding to POIs. The disclosed systems and methods utilize the performance of visual comparisons with a set of definitive content files of a given POI, and by incorporating visual aesthetic features as a factor of such comparisons, a search result is identified that down-weights imprecise and poor quality content files of a given POI, and ensures that only high quality, accurate content files are selected or identified.

In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.

A waterproof paint state determining method of a building using a satellite imagery according to an exemplary embodiment of the present disclosure includes receiving search information including information about a location in which a waterproof paint application state of a rooftop of a building is to be measured from a user; searching a search satellite imagery which is at least one satellite imagery corresponding to the search information from a satellite imagery DB including a plurality of satellite imagery data; detecting a rooftop area which is a waterproof paint application area of at least one building corresponding to the search information from the search satellite imagery, using a convolutional neural network (CNN) technique; and calculating a state index which is an index indicating a waterproof paint application state of at least one building, based on reference color information and color information of the rooftop area.