The present disclosure relates to methods of analyzing works of art for purposes of authentication or attribution. Such methods may be implemented by receiving digital image data associated with a work of art, identifying a plurality of artist's strokes formed along a surface of the work of art, segmenting the plurality of strokes into a plurality of individual strokes, analyzing the plurality of individual strokes to determine stroke characteristics, and comparing the stroke characteristics to stroke characteristics derived from one or more computational models based on known works of art.

Pattern recognition by receiving a set multi-variable data records, each record including a plurality of variables, representing at least two of the plurality of variables as geometric shapes, defining a boundary enclosing the geometric shapes, configuring at least one geometric shape to move within the boundary, capturing a location of each of the geometric shapes within the boundary as a system state, one or more times, combining one or more system states as a system signature, providing a model trained to recognize patterns in system signatures, and recognizing a pattern in the system signature.

A method for providing virtual contents in a virtual space based on a common coordinate system includes: detecting a base marker for identifying a fixed point of an actual operation space, and a target marker for identifying an actual operation object, from initial image data indicating an initial state of the actual operation object in the actual operation space; calculating an initial model matrix of a target marker expressing an initial position and orientation of the target marker, and a model matrix of the base marker expressing a position and an orientation of the detected base marker in a virtual operation space having a common coordinate system which has the detected base marker as a reference; and calculating a current model matrix of the target marker expressing a current position and orientation of the target marker by using the calculated model matrix of the base marker.

A method of producing an avatar video, the method comprising the steps of: providing a reference image of a person's face; providing a plurality of characteristic features representative of a facial model X0 of the person's face, the characteristic features defining a facial pose dependent on the person speaking; providing a target phrase to be rendered over a predetermined time period during the avatar video and providing a plurality of time intervals t within the predetermined time period; generating, for each of said times intervals t, speech features from the target phrase, to provide a sequence of speech features; and generating, using the plurality of characteristic features and sequence of speech features, a sequence of facial models Xt for each of said time intervals t.

A method in an imaging controller of detecting obstructions on a front of a support structure includes: obtaining (i) a point cloud of the support structure and an obstruction, and (ii) a support structure plane corresponding to the front of the support structure; for each of a plurality of selection depths: selecting a subset of points from the point cloud based on the selection depth; detecting obstruction candidates from the subset of points and, for each obstruction candidate: responsive to a dimensional criterion being met, determining whether the obstruction candidate meets a confirmation criterion; when the obstruction candidate meets the confirmation criterion, identifying the obstruction candidate as a confirmed obstruction; and presenting obstruction detection output data including the confirmed obstructions.

Generating visual data by defining a first action into a first set of objects and corresponding first set of motions, and defining a second action into a second set of objects and corresponding second set of motions. A relationship is then determined for the second action to the first action in terms of relationships between corresponding constituent objects and motions. Objects and motions are detected from visual data of first action. Visual data is composed for the second action from the data by transforming the constituent objects and motions detected in first action based on the corresponding determined relationships.

Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.

Method for receiving an input onto a graphical user interface at a client device, capturing an image frame at the client device, the image frame comprising a depiction of an object, identifying the object within the image frame, accessing media content associated with the object within a media repository in response to identifying the object, and causing presentation of the media content within the image frame at the client device.

Provided are a method, an apparatus, and a device for identifying human body, including: acquiring a first original picture captured; adjusting a resolution according to the acquired picture to obtain a target picture; processing the target picture based on a preset model for human body feature point detection to determine whether the target picture includes human body information; if the target picture includes the human body information, determining human body area information in the original picture according to the human body information and inputting the human body area information into a filter, enabling that the filter determines target human body area information according to the human body area information; acquiring a next original picture captured; and determining a possible human body area in the next original picture according to the target human body area information, and performing the step of adjusting the resolution according to the possible human body area.

Methods and systems for videoconferencing include recognition of emotions related to one videoconference participant such as a customer. This ultimately enables another videoconference participant, such as a service provider or supervisor, to handle angry, annoyed, or distressed customers. One example method includes the steps of receiving a video that includes a sequence of images, detecting at least one object of interest (e.g., a face), locating feature reference points of the at least one object of interest, aligning a virtual face mesh to the at least one object of interest based on the feature reference points, finding over the sequence of images at least one deformation of the virtual face mesh that reflect face mimics, determining that the at least one deformation refers to a facial emotion selected from a plurality of reference facial emotions, and generating a communication bearing data associated with the facial emotion.