A method of controlling an image acquisition device for tracking a target object includes: detecting an event in which tracking of a first object, which is a tracking target object, fails in a first image acquired by the image acquisition device; determining, in the first image, a reference object which is used as a reference for controlling the image acquisition device; controlling the image acquisition device such that at least one of an image capturing range and an image capturing direction of the image acquisition device is adjusted based on at least one of a size and a location of the reference object in the first image; and recognizing the first object in a second image acquired by the image acquisition device in a state in which at least one of the image capturing range and the image capturing direction is adjusted.

A method of controlling an image acquisition device for tracking a target object includes: detecting an event in which tracking of a first object, which is a tracking target object, fails in a first image acquired by the image acquisition device; determining, in the first image, a reference object which is used as a reference for controlling the image acquisition device; controlling the image acquisition device such that at least one of an image capturing range and an image capturing direction of the image acquisition device is adjusted based on at least one of a size and a location of the reference object in the first image; and recognizing the first object in a second image acquired by the image acquisition device in a state in which at least one of the image capturing range and the image capturing direction is adjusted.

An exemplary object masking system is configured to mask a recognized object during an application of a synthetic element to an original image. For example, the object masking system accesses a model of a recognized object depicted in an original image of a scene. The object masking system associates the model with the recognized object. The object masking system then generates presentation data for use by a presentation system to present an augmented version of the original image in which a synthetic element added to the original image is, based on the model as associated with the recognized object, prevented from occluding at least a portion of the recognized object. In this way, the synthetic element is made to appear as if located behind the recognized object. Corresponding systems and methods are also disclosed.

An exemplary object masking system is configured to mask a recognized object during an application of a synthetic element to an original image. For example, the object masking system accesses a model of a recognized object depicted in an original image of a scene. The object masking system associates the model with the recognized object. The object masking system then generates presentation data for use by a presentation system to present an augmented version of the original image in which a synthetic element added to the original image is, based on the model as associated with the recognized object, prevented from occluding at least a portion of the recognized object. In this way, the synthetic element is made to appear as if located behind the recognized object. Corresponding systems and methods are also disclosed.

A method of analyzing thoracic insufficiency syndrome (TIS) in a subject by performing quantitative dynamic magnetic resonance imaging (QdMRI) analysis. The QdMRI analysis includes performing four-dimensional (4D) image construction of a TIS subject's thoracic cavity. The 4D image includes a sequence of two dimensional (2D) images of the TIS subject's thoracic cavity over a respiratory cycle of the TIS subject. The QdMRI analysis also includes segmenting a region of interest (ROI) within the 4D image, determining TIS measurements within the ROI, comparing the TIS measurements to normal measurements determined from ROIs in 4D images of the thoracic cavities of normal subjects that are not afflicted by TIS, and outputting quantitative markers indicating deviation of the thoracic cavity of the TIS subject relative to the thoracic cavities of the normal subjects.

A method of analyzing thoracic insufficiency syndrome (TIS) in a subject by performing quantitative dynamic magnetic resonance imaging (QdMRI) analysis. The QdMRI analysis includes performing four-dimensional (4D) image construction of a TIS subject's thoracic cavity. The 4D image includes a sequence of two dimensional (2D) images of the TIS subject's thoracic cavity over a respiratory cycle of the TIS subject. The QdMRI analysis also includes segmenting a region of interest (ROI) within the 4D image, determining TIS measurements within the ROI, comparing the TIS measurements to normal measurements determined from ROIs in 4D images of the thoracic cavities of normal subjects that are not afflicted by TIS, and outputting quantitative markers indicating deviation of the thoracic cavity of the TIS subject relative to the thoracic cavities of the normal subjects.

A device and computer-implemented method for object tracking. The method comprises providing a sequence of digital images, determining a sequence of relational graph embeddings, wherein a first relational graph embedding of the sequence comprises a first object embedding representing a first object in a first digital image of the sequence of digital images, wherein the first relational graph embedding comprises a first relation embedding of a relation for the first object embedding, wherein the first relation embedding relates the first object embedding to embeddings representing other objects of the first digital image in the first relational graph embedding and to embeddings in a second relational graph embedding of the sequence that represent objects of a second digital image of the sequence of digital images.

A non-transitory computer-readable storage medium storing a determination program that causes at least one computer to execute a process, the process includes acquiring a group of captured images that includes images including a face to which markers are attached; selecting, from a plurality of patterns that indicates a transition of positions of the markers, a first pattern that corresponds to a time-series change in the positions of the markers included in consecutive images among the group of captured images; and determining occurrence intensity of an action based on a determination criterion of the action determined based on the first pattern and the positions of the markers included in a captured image included after the consecutive images among the group of captured images.

Systems, methods, and non-transitory media are provided for tracking operations using data received from a wearable device. An example method can include determining a first position of a wearable device in a physical space; receiving, from the wearable device, position information associated with the wearable device; determining a second position of the wearable device based on the received position information; and tracking, based on the first position and the second position, a movement of the wearable device relative to an electronic device.

Systems, methods, and non-transitory media are provided for tracking operations using data received from a wearable device. An example method can include determining a first position of a wearable device in a physical space; receiving, from the wearable device, position information associated with the wearable device; determining a second position of the wearable device based on the received position information; and tracking, based on the first position and the second position, a movement of the wearable device relative to an electronic device.