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148results about How to "Avoid excessive computation" patented technology

Method and Apparatus for Parameter Free Regularized Partially Parallel Imaging Using Magnetic Resonance Imaging

Embodiments of the invention are directed to a method and apparatus for parameter free regularized partially parallel imaging (PPI). Specific embodiments relate to a method and apparatus for high pass GRAPPA (hp-GRAPPA), doubly calibrated GRAPPA (db-GRAPPA), and / or image ratio constrained reconstruction (IRCR). The subject techniques can be applied individually or in combination. In a specific application of an embodiment of the subject method, hp-GRAPPA is used to reconstruct high frequency information, and db-GRAPPA is used reconstruct low frequency information regularized with prior information. In another specific application of an embodiment of the subject method, the result of IRCR a regularization term for db-GRAPPA. Experiments demonstrate that the results obtained by implementing embodiments of the subject method have significantly higher SNR than results obtained utilizing un-regularized techniques and have higher spatial resolution and / or lower error than results obtained using regularized SENSE. The subject double calibration technique lessens the motion problem of the pre-scan even when significant structure change occurs. High quality images generated by a specific embodiment of the subject double calibration technique are demonstrated with a net reduction factor as high as 4.8.
Owner:KONINKLIJKE PHILIPS ELECTRONICS NV

End-to-end behavior recognition method and system based on self-adaptive space-time attention mechanism

ActiveCN111401177AAvoid excessive computationImprove behavior recognition speed and recognition accuracyCharacter and pattern recognitionNeural architecturesSelf adaptiveConvolution
The invention belongs to the field of behavior recognition, and provides an end-to-end behavior recognition method and system based on a self-adaptive space-time attention mechanism. In order to solvethe problem of poor behavior recognition precision, the behavior recognition method comprises the steps of receiving an image sequence of a video; processing the image sequence of the video by usinga behavior recognition model and outputting a behavior recognition result, wherein the behavior recognition model comprises a time attention module and a main convolutional neural network, and a spaceattention module is embedded in the main convolutional neural network; adaptively allocating a weight to each frame of image in the image sequence of the video according to the criticality of each frame of image by using a time attention module, and inputting an output result of the time attention module into a main convolutional neural network for behavior recognition; in the behavior recognition process of the main convolutional neural network, using the spatial attention module for focusing the behavior recognition of the main convolutional neural network on a motion related region, so asto quickly and accurately obtain a behavior recognition result.
Owner:SHANDONG UNIV
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