A method of generating congruous metadata is provided. The method includes receiving a similarity measure between at least two multimedia objects. Each multimedia object has associated metadata. If the at least two multimedia objects are similar based on the similarity measure and a similarity threshold, the associated metadata of each of the multimedia objects are compared. Then, based on the comparison of the associated metadata of each of the at least two multimedia objects, the method further includes generating congruous metadata. Metadata may be tags, for example.

A method of generating congruous metadata is provided. The method includes receiving a similarity measure between at least two multimedia objects. Each multimedia object has associated metadata. If the at least two multimedia objects are similar based on the similarity measure and a similarity threshold, the associated metadata of each of the multimedia objects are compared. Then, based on the comparison of the associated metadata of each of the at least two multimedia objects, the method further includes generating congruous metadata. Metadata may be tags, for example.

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.

System and computer-implemented image search engine of analyzing tags associated with a sequence of images presented to a user to present a current object of interest of the user is disclosed. An image from among a plurality of images is presented on an electronic display. The image is associated with a set of tags. An input is received indicating a user's preference for the image. A plurality of tags is processed based on the preference and the set of tags to determine a next set of tags from the plurality of tags. A next image is determined from the plurality of images based on the next set of tags. The next image represents a physical object, different from a physical object represented by the previous image. A sequence of images is generated by repeating the above process with the next image in place of the previous image for present a user's current object of interest.

System and computer-implemented image search engine of analyzing tags associated with a sequence of images presented to a user to present a current object of interest of the user is disclosed. An image from among a plurality of images is presented on an electronic display. The image is associated with a set of tags. An input is received indicating a user's preference for the image. A plurality of tags is processed based on the preference and the set of tags to determine a next set of tags from the plurality of tags. A next image is determined from the plurality of images based on the next set of tags. The next image represents a physical object, different from a physical object represented by the previous image. A sequence of images is generated by repeating the above process with the next image in place of the previous image for present a user's current object of interest.

Computerized systems and methods are disclosed, including a computer system that executes software that may receive a geographic location having one or more coordinates of a structure, receive a validation of the structure location, and generate unmanned aircraft information based on the one or more coordinates of the validated location. The unmanned aircraft information may include an offset from the walls of the structure to direct an unmanned aircraft to fly an autonomous flight path offset from the walls, and camera control information to direct a camera of the unmanned aircraft to capture images of the walls at a predetermined time interval while the unmanned aircraft is flying the flight path. The computer system may receive images of the walls captured by the camera while the unmanned aircraft is flying the autonomous flight path and generate a structure report based at least in part on the images.

An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

A system for analyzing screenshots can include a computing device including a processor coupled to a memory and a display screen configured to display content. The system can include an application stored on the memory and executable by the processor. The application can include a screenshot receiver configured to access, from storage to which a screenshot of the content displayed on the display screen captured using a screenshot function of the computing device is stored, the screenshot including an image and a predetermined marker. The application can include a marker detector configured to detect the predetermined marker included in the screenshot. The application can include a link identifier configured to identify, using the predetermined marker, a link to a resource mapped to the image included in the screenshot, the resource accessible by the computing device via the link.

The present disclosure provides computerized, automated real estate valuation systems and methods that take into account subjective factors of the property like curb appeal and property condition. In preferred embodiments, the valuation system uses image recognition of images (photos and/or videos) associated with the property to automatically identify which comparable properties to include and to exclude in determining the value of the property.

The present disclosure provides computerized, automated real estate valuation systems and methods that take into account subjective factors of the property like curb appeal and property condition. In preferred embodiments, the valuation system uses image recognition of images (photos and/or videos) associated with the property to automatically identify which comparable properties to include and to exclude in determining the value of the property.