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.

A method for generating data representing the volume of part of a body, the method comprising generating a point distribution model “PDM” based on an input dataset comprising data representing at least one surface of part of a body, the PDM defining a surface model dataset based on an average dataset and one or more weight-eigenvector pairs, generating a first surface model dataset based on the PDM by modifying at least one weight of the one or more weight-eigenvector pairs, wherein the first surface model dataset is different from the average dataset, and generating an output volume dataset based on the first surface model dataset and a first reference dataset, the first reference dataset comprising data representing the volume of a corresponding part of a body, the output volume dataset comprising data representing a deformed volume of the corresponding part of the body.

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, a structure shape of a structure at a first instance of time to pixels within an aerial image depicting the structure captured at a second instance of time; assess a degree of alignment between the structure shape and the pixels, so as to classify similarities between the structure depicted within the pixels and the structure shape 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 structure shape and the pixels within the aerial image are aligned.

An image processing apparatus includes first and second acquisition units, a determination unit, and an estimation unit. The first acquisition unit is configured to acquire shape information of an object. The second acquisition unit is configured to acquire a plurality of pieces of image data. The determination unit is configured to determine a pixel position corresponding to a position at which an orientation of a surface is the same as or similar to an orientation of a surface at a position of interest on the object, as a pixel position for estimating a reflection characteristic of the object at the position of interest. The estimation unit is configured to estimate the reflection characteristic of the object at the position of interest by using a pixel value at the pixel position determined by the determination unit.

Apparatus and methods automate selection of patient interface(s) according to their size, such as with processing in a processor(s) or in a server(s). Image data captured by an image sensor may be received. The captured image data may contain facial feature(s) of an intended user of the patient interface. The facial features may be captured in association with a predetermined reference feature of known dimension(s). The user's facial feature(s) and the reference feature may be detected in the captured image data. Image pixel data of the image may be processed to measure an aspect of the detected facial feature(s) based on the reference feature. A patient interface size may be detected from standard patient interface sizes based on a comparison between the measured aspect of the facial feature(s) and a data record relating sizing information of the standard patient interface sizes and the measured aspect of the facial feature(s).

Aspects of the present disclosure provide an image-based face detection and recognition system that processes and/or analyzes portions of an image using “image strips” and cascading classifiers to detect faces and/or various facial features, such an eye, nose, mouth, cheekbone, jaw line, etc.

Disclosed are computer-readable devices, systems and methods for generating a model of a clothed body. The method includes generating a model of an unclothed human body, the model capturing a shape or a pose of the unclothed human body, determining two-dimensional contours associated with the model, and computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body. Based on the two-dimensional contours and the deformations, the method includes generating a first two-dimensional model of the unclothed human body, the first two-dimensional model factoring the deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation and learning an eigen-clothing model using principal component analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing, to yield a second two-dimensional model of a clothed human body.

The present disclosure provides a method and a device for generating a 3D scene map, a related apparatus and a storage medium. The method includes the following. At least two frames of point cloud data collected by a collection device is obtained. Data registration is performed on the at least two frames of point cloud data. First type of point cloud data corresponding to a movable obstacle is deleted from each frame of point cloud data and each frame of point cloud data is merged to obtain an initial scene map. Second type of point cloud data corresponding to a regularly shaped object is replaced with model data of a geometry model matching with the regularly object for the initial scene map to obtain the 3D scene map.

Disclosed are a method and apparatus for image processing, a device for image processing, and a storage medium. The method for image processing includes: acquiring a first replacement image of a target part at a first posture; determining a posture parameter of the target part at a second posture in a first image; transforming the first replacement image into a second replacement image corresponding to the second posture according to the posture parameter; and fusing the second replacement image to the target part in the first image to obtain a second image.

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.