An image pickup apparatus includes a lens, an image pickup device, and a processor. The processor acquires a plurality of pieces of first image data and second image data with an exposure time period longer than an exposure time period of the first image data from the image pickup device, detects a motion region by using the first image data and the second image data, and acquires motion detection information by using the plurality of pieces of first image data. The processor changes a synthesis method in accordance with whether the motion region is detected and generates one piece of synthesized image data.

An image pickup apparatus includes a lens, an image pickup device, and a processor. The processor acquires a plurality of pieces of first image data and second image data with an exposure time period longer than an exposure time period of the first image data from the image pickup device, detects a motion region by using the first image data and the second image data, and acquires motion detection information by using the plurality of pieces of first image data. The processor changes a synthesis method in accordance with whether the motion region is detected and generates one piece of synthesized image data.

The present disclosure discloses a method and an apparatus for acquiring motion information. A frequency domain transformation is performed on a detection signal of a vibration propagating in a medium to obtain a frequency domain signal, then a signal that is outside of a defined vibration velocity range is removed from the frequency domain signal, that is, only a vibration signal is retained, and then a position-time diagram is obtained along a defined vibration propagation direction. It is not necessary to perform motion estimation on propagation of the vibration by a complicated calculation, and it is only necessary to determine the presence or absence of the vibration by processing in the frequency domain, and then the position-time diagram is obtained, which is a highly efficient method for acquiring motion information.

Images of optical codes are mapped to an overview image to localize optical codes within a space. By localizing optical codes, information about locations of various products can be ascertained. One or more techniques can be used to map the images of optical codes to the overview image. The overview image can be a composite image formed by stitching together several images.

Training a neural network to correct motion-induced artifacts in magnetic resonance images includes acquiring motion-free magnetic resonance image (MRI) data of a target object and applying a spatial transformation matrix to the motion-free MRI data. Multiple frames of MRI data are produced having respective motion states. A Non-uniform Fast Fourier Transform (NUFFT) can be applied to generate respective k-space data sets corresponding to each of the multiple frames of MRI; the respective k-space data sets can be combined to produce a motion-corrupted k-space data set and an adjoint NUFFT can be applied to the motion-corrupted k-space data set. Updated frames of motion-corrupted MRI data can be formed. Using the updated frames of motion corrupted MRI data, a neural network can be trained that generates output frames of motion free MRI data; and the neural network can be saved.

The present invention relates to the field of panoramic cameras, and provides a method for tracking a target in a panoramic video, and a panoramic camera. The method is used to track a target in a panoramic video on the basis of a multi-scale correlation filter, and employs automatic electronic pan-tilt-zoom technology. The present invention provides more robust tracking, faster processing speeds, a greater range of application in different tracking scenarios, and can be used to ensure that a tracked target is always at the center of the screen.

Provided are robotic systems and methods for navigation of luminal network that detect physiological noise. In one aspect, the system includes a set of one or more processors configured to receive first and second image data from an image sensor located on an instrument, detect a set of one or more points of interest the first image data, and identify a set of first locations and a set of second location respectively corresponding to the set of points in the first and second image data. The set of processors are further configured to, based on the set of first locations and the set of second locations, detect a change of location of the instrument within a luminal network caused by movement of the luminal network relative to the instrument based on the set of first locations and the set of second locations.

Provided are robotic systems and methods for navigation of luminal network that detect physiological noise. In one aspect, the system includes a set of one or more processors configured to receive first and second image data from an image sensor located on an instrument, detect a set of one or more points of interest the first image data, and identify a set of first locations and a set of second location respectively corresponding to the set of points in the first and second image data. The set of processors are further configured to, based on the set of first locations and the set of second locations, detect a change of location of the instrument within a luminal network caused by movement of the luminal network relative to the instrument based on the set of first locations and the set of second locations.

This specification includes a method that includes receiving, at one or more processing devices at one or more locations, one or more image frames; receiving a set of signals representing outputs of one or more sensors of a device; estimating, based on the one or more image frames, a first set of one or more motion values; estimating, based on the set of signals, a second set of one or more motion values; determining that a degree of correlation between (i) a first motion represented by the first set of one or more motion values and (ii) a second motion represented by the second set of one or more motion values fails to satisfy a threshold condition; and in response to determining that the degree of correlation fails to satisfy the threshold condition, determining presence of an adverse condition associated with the device.

This specification includes a method that includes receiving, at one or more processing devices at one or more locations, one or more image frames; receiving a set of signals representing outputs of one or more sensors of a device; estimating, based on the one or more image frames, a first set of one or more motion values; estimating, based on the set of signals, a second set of one or more motion values; determining that a degree of correlation between (i) a first motion represented by the first set of one or more motion values and (ii) a second motion represented by the second set of one or more motion values fails to satisfy a threshold condition; and in response to determining that the degree of correlation fails to satisfy the threshold condition, determining presence of an adverse condition associated with the device.