A method for comparing images, comprises: receiving images with the same subject matter that have been recorded at different times; establishing structures in the images and generating directed acyclical graphs based on the structures in the images, wherein each graph has specified points; registering a graph of at least one second image to the graph of a first image; establishing a correspondence between the points of the registered graphs, based on the spatial proximity of the points in conjunction with a link structure of the graphs; registering at least regions of the images that are specified by corresponding points, according to the registered graphs; and outputting at least the registered regions of the images.

A method for comparing images, comprises: receiving images with the same subject matter that have been recorded at different times; establishing structures in the images and generating directed acyclical graphs based on the structures in the images, wherein each graph has specified points; registering a graph of at least one second image to the graph of a first image; establishing a correspondence between the points of the registered graphs, based on the spatial proximity of the points in conjunction with a link structure of the graphs; registering at least regions of the images that are specified by corresponding points, according to the registered graphs; and outputting at least the registered regions of the images.

Provide is a novel mixed-noise removal method for HSI with large size. First, the underlying structure of the HSI is modeled by a two-layer architecture graph. The upper layer, called a skeleton graph, is a rough graph constructed by using the modified k-nearest-neighborhood algorithm and its nodes correspond to a series of superpixels formed by HSI segmentation, which can efficiently characterize the inter-correlations between superpixels, while preserving the boundary information and reducing the computational complexity. The lower layer, called detailed graph, consists of a series of local graphs which are constructed to model the similarities between pixels. Second, based on the two-layer graph architecture, the HSI restoration problem is formulated as a series of optimization problems each of which resides on a subgraph. Third, a novel distributed algorithm is tailored for the restoration problem, by using the information interaction between the nodes of skeleton graph and subgraphs.

Provide is a novel mixed-noise removal method for HSI with large size. First, the underlying structure of the HSI is modeled by a two-layer architecture graph. The upper layer, called a skeleton graph, is a rough graph constructed by using the modified k-nearest-neighborhood algorithm and its nodes correspond to a series of superpixels formed by HSI segmentation, which can efficiently characterize the inter-correlations between superpixels, while preserving the boundary information and reducing the computational complexity. The lower layer, called detailed graph, consists of a series of local graphs which are constructed to model the similarities between pixels. Second, based on the two-layer graph architecture, the HSI restoration problem is formulated as a series of optimization problems each of which resides on a subgraph. Third, a novel distributed algorithm is tailored for the restoration problem, by using the information interaction between the nodes of skeleton graph and subgraphs.

A computer implemented method of skin identification having scales, especially reptile skin identification, includes the steps of acquiring at least one image of a skin portion to be identified, detecting of features corresponding to borders of scales in the image, building a graph of the repetitive pattern scales positions of detected scales, determining the outline of the detected scales and representing the detected scales based on their outline, and determining recognition features data of detected scales for traceable identification of the skin comprising scales. The detection of scales is based on scan lines.

A computer implemented method of skin identification having scales, especially reptile skin identification, includes the steps of acquiring at least one image of a skin portion to be identified, detecting of features corresponding to borders of scales in the image, building a graph of the repetitive pattern scales positions of detected scales, determining the outline of the detected scales and representing the detected scales based on their outline, and determining recognition features data of detected scales for traceable identification of the skin comprising scales. The detection of scales is based on scan lines.

Implementations are directed to methods, systems, and computer-readable media for obtaining videos and extracting, from each video, a key frame for the video including a timestamp. For each key frame, a scene graph is generated. Generating the scene graph for the key frame includes identifying, objects in the image, and extracting a relationship feature defining a relationship between a first object and a second, different object of the objects in the key frame. The scene graph for the key frame is generated that includes a set of nodes and a set of edges. A natural language query request for a video is received, including terms defining a relationship between two or more particular objects. A query graph is generated for the natural language query request, and a set of videos corresponding to the set of scene graphs matching the query graph are provided for display on a user device.

A method includes acquiring digital video data that portrays an interacting event, extracting image data, audio data, and semantic text data from the video data, analyzing the extracted data to identify a plurality of video features, and analyzing the plurality of video features to create a relationship graph. The interacting event comprises a plurality of interactions between plurality of individuals and the relationship graph comprises a plurality of nodes and a plurality of edges. Each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents the plurality of interactions. The method further comprises determining whether a first key feature is present in the relationship graph, wherein presence of the first key feature is predictive of a positive outcome of the interacting event.

A method includes acquiring digital video data that portrays an interacting event, extracting image data, audio data, and semantic text data from the video data, analyzing the extracted data to identify a plurality of video features, and analyzing the plurality of video features to create a relationship graph. The interacting event comprises a plurality of interactions between plurality of individuals and the relationship graph comprises a plurality of nodes and a plurality of edges. Each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents the plurality of interactions. The method further comprises determining whether a first key feature is present in the relationship graph, wherein presence of the first key feature is predictive of a positive outcome of the interacting event.

Apparatuses, systems, and techniques to identify a location in which to place objects within a graphically rendered scene. In at least one embodiment, a location in which to place objects is identified using one or more neural networks, based, at least in part, on text or speech input to the one or more neural networks.