In one embodiment, a method includes recognizing an object in an image that is captured by a camera and presented in a region of a screen of the computing device, generating a 3-dimensional mesh representation for the object by recognizing visual components of the object, where the 3-dimensional mesh representation comprises a plurality of polygons, receiving one or more inputs from the user, where the inputs cause color information for at least a part of the region of the screen to be updated, identifying one of the plurality of polygons that corresponds to a first region of the screen, identifying an area of the texture layer that corresponds to the identified polygon, recording the updated color information in the identified area of the texture layer, generating an augmented reality effect associated with the object based on the updated color information recorded in the identified area of the texture layer.

Described is a system for extracting multi-scale hierarchical clustering on customer observables (COs) data in a vehicle. The system selects a parameter for a set of incident data of COs data. Simplicial complexes are generated from the COs data based on the selected parameter. Face networks are generated from the simplicial complexes. For each face network, a set of connected components is extracted. Each connected component is transformed to a cluster of related COs, resulting in a first extracted relation between COs. The first extracted relation is used to automatically generate an alert at a client device when a second extracted relation different from the first extracted relation results from the transformation.

Various embodiments disclosed herein relate to systems and methods for image matching involving the segmentation of images into regions and then defining and computing features of these regions to further identify geometrical relationships between the regions.

Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

An activity recognition system is disclosed. A plurality of temporal features is generated from a digital representation of an observed activity using a feature detection algorithm. An observed activity graph comprising one or more clusters of temporal features generated from the digital representation is established, wherein each one of the one or more clusters of temporal features defines a node of the observed activity graph. At least one contextually relevant scoring technique is selected from similarity scoring techniques for known activity graphs, the at least one contextually relevant scoring technique being associated with activity ingestion metadata that satisfies device context criteria defined based on device contextual attributes of the digital representation, and a similarity activity score is calculated for the observed activity graph as a function of the at least one contextually relevant scoring technique, the similarity activity score being relative to at least one known activity graph.

The purpose of the present invention is to provide a makeup support device capable of appropriately supporting the application of makeup even for users who have insufficient makeup skills. The makeup support device (100) has: a makeup style selection unit (250) that selects a makeup style being a makeup method, for each face to be made up; and a makeup style suggestion unit (260) that causes pigment corresponding to the selected makeup style to be supported on the surface of a sheet capable of being adhered for long periods to facial skin, on the opposite side to the side adhered to the skin.

An example method described herein involves receiving a first data stream and a second data stream; determining that a plurality of entities are present in the image data of the first data stream; analyzing the first data stream to determine that an entity, of the plurality of entities, is unrecognizable in the image data of the first data stream; obtaining, by the device, a common knowledge graph associated with the first data stream and the second data stream, wherein the common knowledge graph includes information regarding the plurality of entities; annotating the common knowledge graph with first corresponding recognizable characteristics of the plurality of entities in the first data stream to generate a first annotated knowledge graph; annotating the common knowledge graph with second corresponding recognizable characteristics of the plurality of entities in the second data stream to generate a second annotated knowledge graph; determining whether the entity is recognizable based on the first annotated knowledge graph and the second annotated knowledge graph; and/or performing an action associated with the first data stream based on whether the entity is recognizable.

Discovering repetitive structures in 3D models is a challenging task. A method for detecting repetitive structures in 3D models comprises sampling the 3D model using a current sampling step size, detecting repetitive structures and remaining portions of the model, determining a representative for each of the one or more repetitive structures, and as long as the detecting step yields one or more repetitive structures, reducing the current sampling step size and repeating the steps of sampling and detecting for each detected representative of a detected repetitive structure and for the remaining portions of the model, wherein the reduced sampling step size is used. The described method and device can e.g. be used for 3D model compression, 3D model repairing, or geometry synthesis.

The inventive subject matter is directed to an artificial intelligence intra-operative surgical guidance system and method of use. The artificial intelligence intra-operative surgical guidance system is made of a computer executing one or more automated artificial intelligence models trained on data layer datasets collections to calculate surgical decision risks and provide autonomously executing intra-operative surgical guidance; and a display configured to provide visual guidance to a user.

The inventive subject matter is directed to an artificial intelligence intra-operative surgical guidance system and method of use. The artificial intelligence intra-operative surgical guidance system is made of a computer executing one or more automated artificial intelligence models trained on data layer datasets collections to calculate surgical decision risks and provide autonomously executing intra-operative surgical guidance; and a display configured to provide visual guidance to a user.