Methods and apparatuses for training a magnetic resonance imaging model, electronic devices and computer readable storage media are provided. A method may include: acquiring a magnetic resonance image data set; constructing a ring deep neural network to be trained; inputting an under-sampled magnetic resonance image and a full-sampled magnetic resonance image respectively to two neural networks included in the ring deep neural network, to generate respective simulated magnetic resonance images; inputting a first simulated full-sampled magnetic resonance image and the full-sampled magnetic resonance image to a pre-constructed first simulated magnetic resonance image class discrimination model, to obtain a first discrimination result indicating whether or not the first simulated full-sampled magnetic resonance image is of a simulated magnetic resonance image class; and adjusting a network parameter of the ring deep neural network based on a preset loss function, to obtain a trained magnetic resonance imaging model.

Methods and apparatuses for training a magnetic resonance imaging model, electronic devices and computer readable storage media are provided. A method may include: acquiring a magnetic resonance image data set; constructing a ring deep neural network to be trained; inputting an under-sampled magnetic resonance image and a full-sampled magnetic resonance image respectively to two neural networks included in the ring deep neural network, to generate respective simulated magnetic resonance images; inputting a first simulated full-sampled magnetic resonance image and the full-sampled magnetic resonance image to a pre-constructed first simulated magnetic resonance image class discrimination model, to obtain a first discrimination result indicating whether or not the first simulated full-sampled magnetic resonance image is of a simulated magnetic resonance image class; and adjusting a network parameter of the ring deep neural network based on a preset loss function, to obtain a trained magnetic resonance imaging model.

An individual object identification system includes: an image acquisition processor configured to perform an image acquisition process to acquire an image of a subject acquired using imaging equipment; a feature point extraction processor that extracts a feature point from the image; a local feature amount calculation processor that calculates a local feature amount of the feature point; a local feature amount group classification processor that performs classification into a predetermined number of local feature amount groups; a global feature amount calculation processor that calculates a global feature amount based on each of the local feature amount groups; a searching target image registration processor that registers a plurality of images that are searching targets; a global feature amount registration processor that registers the global feature amount related to each of the registered images in a global feature amount registration unit; a narrowing processor that narrows down the plurality of registered images to registered images each having the global feature amount highly correlated with a global feature amount of an identification image; and a determination processor that compares the registered images as candidates with the identification image to determine the registered image having the largest number of corresponding points.

An individual object identification system includes: an image acquisition processor configured to perform an image acquisition process to acquire an image of a subject acquired using imaging equipment; a feature point extraction processor that extracts a feature point from the image; a local feature amount calculation processor that calculates a local feature amount of the feature point; a local feature amount group classification processor that performs classification into a predetermined number of local feature amount groups; a global feature amount calculation processor that calculates a global feature amount based on each of the local feature amount groups; a searching target image registration processor that registers a plurality of images that are searching targets; a global feature amount registration processor that registers the global feature amount related to each of the registered images in a global feature amount registration unit; a narrowing processor that narrows down the plurality of registered images to registered images each having the global feature amount highly correlated with a global feature amount of an identification image; and a determination processor that compares the registered images as candidates with the identification image to determine the registered image having the largest number of corresponding points.

Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image set selection can include comparing a determined image quality metric with an image quality threshold to identify an eye image passing an image quality threshold, and selecting, from a plurality of eye images, a set of eye images that passes the image quality threshold.

Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image set selection can include comparing a determined image quality metric with an image quality threshold to identify an eye image passing an image quality threshold, and selecting, from a plurality of eye images, a set of eye images that passes the image quality threshold.

A radar system is provided that includes transmission signal generation circuitry, a transmit channel coupled to the transmission generation circuitry to receive a continuous wave test signal, the transmit channel configurable to output a test signal based on the continuous wave signal in which a phase angle of the test signal is changed in discrete steps within a phase angle range, a receive channel coupled to the transmit channel via a feedback loop to receive the test signal, the receive channel including an in-phase (I) channel and a quadrature (Q) channel, a statistics collection module configured to collect energy measurements of the test signal output by the I channel and the test signal output by the Q channel at each phase angle, and a processor configured to estimate phase and gain imbalance of the I channel and the Q channel based on the collected energy measurements.

Provided are an apparatus and method for sensing a moving ball, which extract a feature portion such as a trademark, a logo, etc. indicated on a ball from consecutive images of a moving ball, acquired by an image acquisition unit embodied by a predetermined camera device, and calculate a spin axis and spin amount of rotation the moving ball based on the feature portion and thus spin of the ball is simply, rapidly, and accurately calculated with low computational load, thereby achieving rapid and stable calculation of the ball in a relatively low performance system. The sensing apparatus includes an image acquisition unit for acquiring consecutive images, an image processing unit for extracting a feature portion from the acquired image, and a spin calculation unit for calculating spin using the extracted feature portion.

Provided are an apparatus and method for sensing a moving ball, which extract a feature portion such as a trademark, a logo, etc. indicated on a ball from consecutive images of a moving ball, acquired by an image acquisition unit embodied by a predetermined camera device, and calculate a spin axis and spin amount of rotation the moving ball based on the feature portion and thus spin of the ball is simply, rapidly, and accurately calculated with low computational load, thereby achieving rapid and stable calculation of the ball in a relatively low performance system. The sensing apparatus includes an image acquisition unit for acquiring consecutive images, an image processing unit for extracting a feature portion from the acquired image, and a spin calculation unit for calculating spin using the extracted feature portion.

A target detection method and apparatus. The method comprises: acquiring an input image, and sending same to a candidate region generation network to generate a plurality of regions of interest; formatting the plurality of regions of interest, and then sending same to a target key point network to generate a thermodynamic diagram; using a global feature map of the input image to perform convolution on the thermodynamic diagram, so as to generate a local depth feature map; and fusing the global feature map and the local depth feature map, and detecting a target therefrom by means of a detector. The present invention can be applied to target detection at different scales, improves the detection accuracy and robustness of a target detection technique for an occluded target in complex scenarios, and achieves, by means of making full use of local key point information of the target, target positioning under occlusion.