An electronic device and a method for tracking an object thereof are provided. The electronic device identifies whether there is a first object being tracked, when obtaining an image and rotation information of a camera of the electronic device, corrects state information of the first object using the rotation information, when there is the first object, detects a second object matched to the first object from the image based on the corrected state information, and tracks a position of the second object using an object tracking algorithm.

Disclosed is a system and associated methods for generating and rendering a polyhedral point cloud that represents a scene with multi-faceted primitives. Each multi-faceted primitive stores multiple sets of values that represent different non-positional characteristics that are associated with a particular point in the scene from different angles. For instance, the system generates a multi-faceted primitive for a particular point of the scene that is captured in first capture from a first position and a second capture from a different second position. Generating the multi-faceted primitive includes defining a first facet with a first surface normal oriented towards the first position and first non-positional values based on descriptive characteristics of the particular point in the first capture, and defining a second facet with a second surface normal orientated towards the second position and second non-positional values based on different descriptive characteristics of the particular point in the second capture.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

Embodiments relate to in-situ process monitoring of a part being made via additive manufacturing. The process can involve capturing computed tomography (CT) scans of a post-built part. A neural network (NN) can be used during the build of a new part to process multi-modal sensor data. Spatial and temporal registration techniques can be used to align the data to x,y,z coordinates on the build plate. During the build of the part, the multi-modal sensor data can be superimposed on the build plate. Machine learning can be used to train the NN to correlate the sensor data to a defect label or a non-defect label by looking to certain patterns in the sensor data at the x,y,z location to identify a defect in the CT scan at x,y,z. The NN can then be used to predict where defects are or will occur during an actual build of a part.

Disclosed in some examples are devices, methods, systems, and machine-readable mediums for enhanced meeting room solutions to provide increased inclusiveness for both remote and in-room participants for network-based communication sessions, such as hybrid network-based communication sessions. Content of a first type is placed in a location exclusive of a discontinuity in a display device and content of a second type is placed in a location inclusive of the discontinuity of the display device.

Disclosed in some examples are devices, methods, systems, and machine-readable mediums for enhanced meeting room solutions to provide increased inclusiveness for both remote and in-room participants for network-based communication sessions, such as hybrid network-based communication sessions. Content of a first type is placed in a location exclusive of a discontinuity in a display device and content of a second type is placed in a location inclusive of the discontinuity of the display device.

The coordinate system fixing unit uses the displacement of an object under measurement between photographed images in chronological order to generate fixed-coordinate chronological images. The displacement calculation unit uses the fixed-coordinate chronological images to calculate a two-dimensional spatial distribution of the displacement of the surface of the object under measurement. The displacement difference calculation unit calculates a two-dimensional displacement difference distribution by removing an error component from the two-dimensional spatial distribution. The depth movement amount calculation unit calculates a depth movement amount from the two-dimensional displacement difference distribution. The displacement separation unit calculates in-plane displacement from the two-dimensional displacement difference distribution. The determination unit uses the in-plane displacement and/or the depth movement amount to determine whether there is an abnormality in the object under measurement.

The coordinate system fixing unit uses the displacement of an object under measurement between photographed images in chronological order to generate fixed-coordinate chronological images. The displacement calculation unit uses the fixed-coordinate chronological images to calculate a two-dimensional spatial distribution of the displacement of the surface of the object under measurement. The displacement difference calculation unit calculates a two-dimensional displacement difference distribution by removing an error component from the two-dimensional spatial distribution. The depth movement amount calculation unit calculates a depth movement amount from the two-dimensional displacement difference distribution. The displacement separation unit calculates in-plane displacement from the two-dimensional displacement difference distribution. The determination unit uses the in-plane displacement and/or the depth movement amount to determine whether there is an abnormality in the object under measurement.

A pose determining method, which may be applied to the field of photographing and image processing, includes: obtaining a target image, where the target image includes a target parking space mark and a target parking space line, and a target parking space corresponding to the target parking space mark includes the target parking space line; and determining pose information based on the target parking space mark and the target parking space line. The pose information indicates a corresponding pose of a terminal during photographing of the target image. According to the pose determining method, the pose information may be determined based on the target parking space mark and the target parking space line, to implement positioning.

An image inspection device includes: an image acquisition unit to acquire an inspection target image; a geometric transformation processing unit to estimate a geometric transformation parameter for aligning a position of an inspection target in the inspection target image with a first reference image in which a position of the inspection target is known, and geometrically transform the inspection target image using the estimated geometric transformation parameter, thereby generating an aligned image in which the position of the inspection target is aligned with the first reference image; an image restoration processing unit to restore the aligned image, using an image generation network to receive an input image generated using the inspection target image and infer the aligned image as a correct image; and an abnormality determination unit to determine an abnormality of the inspection target using a difference image between the aligned image and the restored aligned image.