14 results about "Warping function" patented technology

Methods and computer systems for methods, computer systems, and computer-readable memory media for determining a warp function. An as-designed watertight spline model of an object is received. A set of sweep points along a sweep trajectory for manufacturing the object are used to determine a sweep surface. Model points are sampled from the as-designed watertight spline model and projected onto nearest as-executed points on the sweep surface. The projected as-executed points are used to construct an as-executed model of the object. A warp function is determined based on a difference between the as-designed watertight spline model and the as-executed model. The warp function is a continuous function approximating differences between the as-designed model and the as-manufactured object. The warp function is stored in a non-transitory computer-readable memory medium.

An example computing device may include memory and one or more processors. The one or more processors may be configured to parallel entropy decode encoded video data from a received bitstream to generate entropy decoded data. The one or more processors may be configured to predict a motion vector based on the entropy decoded data. The one or more processors may be configured to decode a motion vector residual from the entropy decoded data. The one or more processors may be configured to add the motion vector residual and motion vector. The one or more processors may be configured to warp previous reconstructed video data with an overlapped block-based warp function using the motion vector to generate predicted current video data. The one or more processors may be configured to sum the predicted current video data with a residual block to generate current reconstructed video data.

Methods and computer systems for methods, computer systems, and computer-readable memory media for determining a warp function. An as-designed watertight spline model of an object is received. A set of sweep points along a sweep trajectory for manufacturing the object are used to determine a sweep surface. Model points are sampled from the as-designed watertight spline model and projected onto nearest as-executed points on the sweep surface. The projected as-executed points are used to construct an as-executed model of the object. A warp function is determined based on a difference between the as-designed watertight spline model and the as-executed model. The warp function is a continuous function approximating differences between the as-designed model and the as-manufactured object. The warp function is stored in a non-transitory computer-readable memory medium.

The present application provides a method for predicting phase using a parallel estimation architecture network trained with anti-warping loss. The method includes, during the training process, simulating the process of calculating the phase spectrum from the real and imaginary parts of the short-time complex spectrum through two parallel linear convolution layers and a phase calculation unit in the neural network to be trained, and limiting the predicted phase value to the main value interval to achieve the prediction of the warped phase spectrum, and the anti-warping loss used in the training includes the instantaneous phase error, group delay error and instantaneous angular frequency error activated by the anti-warping function to avoid the error expansion problem caused by phase warping. After the training is completed, the trained phase prediction neural network is used to process the logarithmic amplitude spectrum of the speech signal to be predicted to obtain the warped phase spectrum. This solution directly predicts the warped phase spectrum of the speech signal through a neural network, and solves the error expansion problem caused by phase warping during training by introducing an anti-warping function when calculating the loss, and has high efficiency and accuracy.

The invention discloses a DTWICA-based thought typewriting character signal template construction method, and relates to construction of a thought typewriting character signal template, which comprises the following steps of: firstly, constructing preprocessing, DTWICA and template construction on equipment, and visualizing and classifying aligned data; the electroencephalogram signals are sent into a DTWICA model, the model constructs a distortion function for the EEG signals of each trial through FastDTW, and signal decomposition is carried out based on FastICA to obtain time factors and neuron factors; carrying out mapping deformation on the decomposed time factor through a distortion function, and combining the time factor with a neuron factor to form a new EEG signal; then, a sliding time window is used for carrying out self-adaptive processing on the new EEG signal, and a deformed EEG signal is obtained; finally, averaging the deformed EEG signals of different test times to obtain each type of character signal template; according to the method, the performance of the template is verified in the imaginary sentence data set, and technical support is provided for real-time imaginary handwriting typing.

Methods and computer systems for methods, computer systems, and computer-readable memory media for determining a warp function. An as-designed watertight spline model of an object is received. A set of sweep points along a sweep trajectory for manufacturing the object are used to determine a sweep surface. Model points are sampled from the as-designed watertight spline model and projected onto nearest as-executed points on the sweep surface. The projected as-executed points are used to construct an as-executed model of the object. A warp function is determined based on a difference between the as-designed watertight spline model and the as-executed model. The warp function is a continuous function approximating differences between the as-designed model and the as-manufactured object. The warp function is stored in a non-transitory computer-readable memory medium.

The invention relates to the technical field of computer vision, and provides an asynchronous space warping function evaluation method and device, equipment, a medium and a product. The method comprises the following steps: collecting a plurality of evaluation parameters in a first dynamic scene; determining a plurality of evaluation indexes based on the plurality of evaluation parameters, wherein the plurality of evaluation indexes at least comprise a motion vector residual error and a geometric distortion residual error; and evaluating the asynchronous spatial warping function according to the plurality of evaluation indexes to obtain a first evaluation result. According to the method, the motion prediction precision and the geometric deformation matching degree of the asynchronous spatial warping function are reflected through the residual error generated by the motion vector field and geometric warping, and the accuracy of the asynchronous spatial warping function is evaluated through the key performance of the asynchronous spatial warping function.

A method includes obtaining an image that includes a plurality of pixels. The method includes identifying a first subset of the plurality of pixels that defines a particular geometry that a user of the electronic device is currently focusing on. The method includes warping the first subset of the plurality of pixels according to a first warping function that is based on a movement of the electronic device. The method includes warping a second subset of the plurality of pixels, that is different from the first subset, according to a second warping function that is different from the first warping function.

An example computing device may include a memory and one or more processors. The one or more processors may be configured to perform parallel entropy decoding of the encoded video data from the received bitstream to generate entropy decoded data. The one or more processors may be configured to predict a motion vector based on the entropy decoded data. The one or more processors may be configured to decode a motion vector residual from the entropy decoded data. The one or more processors may be configured to add the motion vector residual to the motion vector. The one or more processors may be configured to warp previously reconstructed video data using the motion vector with a warp function based on overlapping blocks to generate predicted current video data. The one or more processors may be configured to sum the predicted current video data with the residual block to generate current reconstructed video data.

The present invention discloses an image processing method, device and electronic device. The method comprises: obtaining a first group of positions of a first group of feature points in a first image to be deformed, and moving the first group of feature points to obtain a second group of positions; determining radial basis fitting parameters according to the offset between each position in the first group of positions and each position in the third group of positions, the first group of positions and the second group of position values, and determining a target deformation function according to the radial basis parameters; according to the target deformation function, moving each pixel point in the first image to a corresponding pixel point in the second image to obtain a deformed second image, thereby achieving a deformation field of an image based on a radial basis function, and generating a deformed image by transmitting the deformation field to a graphics processor, thereby solving the technical problems in the prior art that the spatial structure of points is determined due to the grid, arbitrary large-scale deformation cannot be performed, and the smoothness is low in image deformation.

Determining a warp function between two watertight spline models. A first watertight spline model of a first object and a set of points and associated metadata from a second object are received. A second watertight spline model of the second object is constructed based on the set of points, the metadata, and the first watertight spline model. A warp function is determined based on a difference between the first watertight spline model and the second watertight spline model. The warp function is a continuous function approximating differences between the first object and the second object. The warp function is stored in a non-transitory computer-readable memory medium.

A network device (110) generates (810) video data representing a viewing frustrum of a three-dimensional scene. A plurality of virtual objects are within the viewing frustrum. The network device (110) transmits (820) pose information and the video data to a computing device (120) over a first transport channel (130a) and a second transport channel (130b), respectively. The first transport channel (130a) has lower latency characteristics than the second transport channel (130b) and the pose information comprises a pose of a virtual object within the viewing frustrum. The computing device (120) receives (860), from the network device (110), the pose information and video data over the first transport channel (130a) and the second transport channel (130b), respectively. The computing device (120) predicts (870) a newer pose of the virtual object from the pose information and generates (880) a two-dimensional image using the predicted pose and the video data as inputs to a warping function.

A method of decoding an image according to a present disclosure, the method includes decoding a Gaussian parameter and Gaussian embedding for a first Gaussian in a canonical space; obtaining a variation of a Gaussian parameter for the first Gaussian by inputting the Gaussian embedding into a deformation function; and reconstructing a second Gaussian at a target timestamp by applying the variation to the Gaussian parameter for the first Gaussian, wherein the Gaussian embedding is derived individually for each Gaussian.