293 results about "Space time domain" patented technology

The invention discloses a method for partial reference evaluation of wireless videos based on space-time domain feature extraction, which relates to a method for evaluating video quality. On the basis of performing profound understanding and detailed analysis on the existing video objective quality evaluation model, by combining visual characteristics of human eyes, the invention provides the method for the partial reference evaluation of the wireless videos based on the space-time domain feature extraction to improve the conventional SSIM model. The method takes the fluency of the time domain and the structural similarity and the definition of the space domain of the videos as main evaluation indexes, and under the condition of ensuring the evaluation accuracy, the method extracts characteristic parameters of ST domains (the space domain and the time domain), establishes a new evaluation model, reduces the reference data needed by the evaluation and lowers the computational complexity to ensure that the evaluation model is suitable for evaluating the quality of wireless transmission videos in real time.

Seismic data are obtained for each seismic source activation in a marine streamer and for each frequency, after being transformed to a spectral domain. An iterative conjugate gradient scheme, using a physically-based preconditioner, is applied to the transformed seismic data, to provide a least squares solution to a normal set of equations for a deghosting system of equations. The solution is inverse-transformed back to a space-time domain to provide deghosted seismic data.

The invention belongs to the field of infrared image processing, and mainly relates to an infrared weak and small target detection method based on time-space domain background suppression. The infrared weak and small target detection method is used for achieving the aim of infrared movement weak and small target detection in a complicated background and includes the steps that firstly, stable background noise waves in a space domain are suppressed through guiding filtering; secondly, slowly-changed backgrounds in a time domain are suppressed with a gradient weight filtering method on the time domain through target movement information in an infrared image sequence; thirdly, the time domain background suppression result and the space domain background suppression result are fused to obtain a background-suppressed weak and small target image; finally, the image is split through a self-adaptation threshold value, and a weak and small target is detected. By means of the infrared weak and small target detection method, during target detection, space grey information of the infrared weak and small target is used, time domain movement information of the target is further sufficiently used, the background noise waves are suppressed in the time domain and the space domain, and therefore the movement weak and small target detection performance in the complex background is greatly improved.

Methods to identify and risk stratify disease states, cardiac structural defects, functional cardiac deficiencies induced by teratogens and other toxic agents, pathological substrates, conduction delays and defects, and ejection fraction using single channel biological data obtained from the subject. A modified Matching Pursuit (MP) algorithm may be used to find a noiseless model of the data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to characterize the cardiac system. In another method, space-time domain is divided into a number of regions (which is largely determined by the signal length), the density of the signal is computed in each region and input to a learning algorithm to associate them to the desired cardiac dysfunction indicator target.

The Space-Time Mapping Analysis (STMA) method and system provides an engineering method and/or system for modeling and/or analyzing and/or designing and/or building and/or operating complex physical processes, components, devices, and phenomena. STMA can be used in a way for modifying and/or improving the design of many different products, components, processes, and devices, for example. Any physical system, whether existing or proposed, which exhibits, for example, unsteady flow phenomena, might be modeled by the STMA. Thus, STMA can be implemented as a part of an engineering system for design improvements and/or modifications and/or evaluations. The STMA system and/or method uses a space-time mapping technique wherein the space and time directions are treated in an equivalent way, such that, rather than solving a three-dimensional unsteady problem by sweeping in the time direction from an initial point in time to a final point in time, the problem is solved as a four-dimensional problem in space-time.

The invention provides a nonlinear optimization based time-space domain staggered grid finite difference method and device. The method includes: determining finite difference coefficients; optimizing the finite difference coefficients on the basis of time-space domain dispersion relation and a nonlinear inversion algorithm; utilizing the optimized finite difference coefficients to perform elastic wave forward modeling. By the method and device, the technical problems of high middle-high frequency dispersion and low simulation precision caused by the fact that a finite difference method of taylor series expansion and space domain dispersion relation is utilized to acquire the finite difference coefficients so as to perform elastic wave forward modeling are solved, dispersion of middle and high frequency is achieved, and technical effect of simulation precision is improved.

The invention discloses a segmentation method for the space-time consistency of a video sequence of the parameter and depth information of a known video camera, comprising the following steps of: (1) segmenting videos by using a Mean-shift method; (2) counting Mean-shift segmentation boundaries according to the parameter and depth information of the video camera, and computing a probability boundary diagram of each frame; (3) segmenting the probability boundary diagram by utilizing a Watershed and an energy optimization method to obtain a more consistent segmentation result; (4) matching and connecting segmentation blocks among different frames to generate segmentation blocks in a space-time domain in regard to initialized segmentation obtained by utilizing the Watershed and the energy optimization method; (5) counting the probability of each pixel belonging to each space-time domain segmentation block by utilizing the parameters and the depth of the video camera, and iteratively optimizing frame by frame by using the energy optimization method to obtain a space-time consistent video segmentation result. The segmentation result can well keep object boundaries and the high space-time consistency of the segmentation blocks among multiple frames of the videos without flashing and skipping.

The invention puts forward the predicting method by combining correlation of motion vector between time domain and space domain as well as the method for describing movement correlation between adjacent macro blocks. The method includes following steps. A video camera converts the state of a target object to video frequency signal. The said video signal through an acquisition card is converted to a digital video frequency sequence stored in a flash memory to be used as input from system for further compression. The computer runs motion compensation/DCT mixed coded subroutine for original video frequency frames for carrying out video frequency compression and creating code stream file. The main point of the invention is the subrouting for predicting motion vector.

The invention discloses a target tracking method based on correlation of space-time-domain edge and color feature. The target tracking method based on correlation of space-time-domain edge and color feature comprises the following steps: (1) selecting a tracked target area; (2) extracting the edge outline of the target and calculating the direction angle of the edge; (3) along the two orthogonal directions of horizontal direction and vertical direction, conducting statistics of edge-color symbiosis character pairs, and building a target edge-color correlation centroid model; (4) selecting the centroids of the edge-color pairs with high confidence coefficient to conduct probability weighting, so as to gain a transfer vector of a target centroids in a current frame; (5) conducting statistics of histograms of target edge distances between adjacent frames, conducting probability weighting of the successfully matched distance change rates between the adjacent frames so as to gain a target dimension scaling parameter. By means of the target tracking method based on correlation of space-time-domain edge and color feature, a target tracking in a crowded scene, a shelter, and a condition that the target dimension changes is achieved, and robustness, accuracy and instantaneity of the tracking are improved. The target tracking method based on correlation of space-time-domain edge and color feature has a wide application prospect in the video image processing field, and can be applied to the fields such as intelligent video monitoring, enterprise production automation and intelligent robot.

This invention relates to a network video encoder multidimensional code rate control method. The image control unit of an input video sequence containing macroblock strips, image frames or image set is divided into different dimensions of set spaces according to the time domain, space domain, time domain transformation, space domain, time domain transformation, space domain transformation and space-time domain transformation, a code rate control unit transmits the time-varying state of band-width, the occupation degree of a buffer zone and resource and process ability of multi-access terminal based on the multiple element isomeric network channels to carry out variable bit rate encode of the divided set space to match with the current video encoder resource, or network transmission code rate, the process ability of the receive end and the decoder, the video decoder reconstructs related multidimensional images to display image sequence based on the grammar ID plugged by received stream.

Seismic data recorded in a marine streamer are obtained, with the seismic data being representative of characteristics of subsurface earth formations and acquired by deployment of a plurality of seismic receivers overlying an area of the subsurface earth formations to be evaluated, the seismic receivers generating at least one of an electrical and optical signal in response to seismic energy. A complex Laplace frequency parameter is used to transform the seismic data from a space-time domain to a spectral domain. An iterative conjugate gradient scheme, using a physically-based preconditioner, is applied to the transformed seismic data, to provide a least squares solution to a normal set of equations for a deghosting system of equations. The solution is inverse-transformed back to a space-time domain to provide deghosted seismic data, which is useful for imaging the earth's subsurface.

Seismic data recorded in a marine streamer are obtained, sorted as a common receiver gather. A complex Laplace frequency parameter is used to transform the seismic data from a space-time domain to a spectral domain. An iterative conjugate gradient scheme, using a physically-based preconditioner, is applied to the transformed seismic data, to provide a least squares solution to a normal set of equations for a source deghosting system of equations. The solution is inverse-transformed back to a space-time domain to provide source deghosted seismic data, which is useful for imaging the earth's subsurface.

The embodiment of the invention discloses an expression recognition method, device and equipment for a video. The method comprises the steps: extracting a corresponding image sequence from any video comprising a face; extracting a face image in the video through face recognition, and carrying out a corresponding alignment operation as the input; carrying out the feature extraction comprising spatial features and time features through a pre-trained 3D convolution neural network, and generating a feature vector. The method integrates the prediction of the time-space domain of an image sequence,and achieves the more accurate expression recognition according to the feature vector.

Objective video quality evaluation is one of the important research points for QoE service in the further. The invention provides a video quality evaluation method based on no-reference natural scene statistics (NSS). Firstly, through analyzing a video sequence, corresponding statistical values of each pixel point and the adjacent point are calculated and space domain statistics characteristics of the video are thus obtained. A predication image of an n+1 frame is obtained according to a motion vector and in combination with a reference frame n, a motion residual image is obtained, and statistical distribution after DCT transformation is carried out on the residual image is observed. Values obtained in the former two steps are used for respectively calculating a mahalanobis distance between the space domain characteristics and the natural video characteristics and a mahalanobis distance between the time domain characteristics and the natural video characteristics so as to obtain statistical differences between a distorted video and the natural video, and the quality of a single-frame image is obtained when the time domain information and the space domain information are converged. Finally, a time domain aggregation strategy on the basis of visual hysteresis effects is adopted to obtain the objective quality of the final video sequence.

The invention discloses a multiuser-oriented relay satellite space-time frequency domain resource dynamic scheduling method. The method comprises the following steps: I, receiving different paths of user data streams; II, calculating weight values of the paths of user data streams; and III, allocating time-domain resource blocks to the user data streams according to the weight values. Through adoption of the multiuser-oriented relay satellite space-time frequency domain resource dynamic scheduling method, comprehensive consideration is given to the factors of task priority, time delay requirement, packet loss rate, throughput and application time sequence, and reasonable satellite resources are allocated to a plurality of users on a space-time domain. Specific to a relay satellite control and management center, the spectrum utilization rate and network throughput of relay satellite resources are increased. From the aspect of the users, the service quality of different services of the users is ensured, and the user demands are met.

The invention discloses a weak target detection method by use of time-space domain filtering adaptive to a pipe diameter. The weak target detection method comprises steps of: carrying out background detection on a to-be-processed image by use of anisotropic differential algorithm so as to improving subsequent target detection ability; segmenting a difference graph by use of a local maximum value so as to acquire a binary image; initializing a time domain parameter (an accumulation frame length) and a space domain parameter (the size of the pipe diameter), and successively inputting a series of binary images with the accumulation frame length of N; and finally, by use of the time-space domain filtering adaptive to the pipe diameter, detecting multiple frames of images so as to acquire a real target point, and superposing the detection results so as to output a target movement track. Compared with a traditional pipeline filtering target detection method with a fixed pipe diameter, the weak target detection method is advantageous in that based on the correlation between a target and the time-space domain multi-frame movement, the size of the pipe diameter is adaptively modified according to the change of a target scale, so a detection problem caused by the fact that the target becomes small/big due to unchanged pipe diameter is effectively solved and target detection precision is greatly improved.

The invention discloses a convex optimization method for a three-dimensional (3D)-video-based time-space domain motion segmentation and estimation model. The method is implemented by the following steps of: 1) establishing the 3D-video-based time-space domain motion segmentation and estimation model according to an active contour theory and a mapping relationship between a background three-dimensional motion parameter and a two-dimensional light stream; 2) converting the model into a corresponding horizontal set description equation, calculating a corresponding gradient descent equation, calculating an equivalent equation of the gradient descent equation, calculating an energy function corresponding to the equivalent equation, and performing convex relaxation on the energy function to obtain a convexly-optimized time-space domain motion segmentation and estimation model; and 3) introducing a cost variable into the further relaxation of the convexly-optimized time-space domain motion segmentation and estimation model, minimizing the convexly-optimized time-space domain motion segmentation and estimation model by adopting a multi-variable alternate iteration algorithm, and performing iterative convergence to obtain a final split surface according to a selected threshold function. The method has the advantages of high adaptability to changes in a target number, independence of a segmentation result on an initialized contour, and high operation efficiency.

The invention discloses a human body behavior recognition method based on a depth video sequence. The method comprises the steps that four-dimensional normal vectors of all pixel points in the video sequence are calculated; low-level features of the pixel points in different layers are extracted by establishing behavior sequence space-time pyramid models in different space-time domains; a group sparse dictionary is learned based on the low-level features, so that sparse codes of the low-level features are obtained; the codes are integrated through a space average pool and a time maximum pool, so that high-level features are obtained and serve as descriptors of a final behavior sequence. The descriptors can effectively preserve space-time multi-resolution information of human body behaviors; meanwhile, the sparse dictionary with stronger expressive power is obtained by eliminating similar contents contained in different behavior categories, so that the behavior recognition rate is effectively increased.

The invention relates to a denoising device and a denoising method for sequence image. The denoising device comprises an input image unit, a motion estimation unit, a time-space domain federated filtering unit and a noise estimation module, wherein the input image unit inputs the sequence image to the motion estimation unit, and the sequence image is processed by the motion estimation unit and then input to the time-space domain federated filtering unit for processing; the noise estimation module outputs data to the motion estimation unit for processing; the motion estimation unit comprises a space domain filtering module, a differential module, a differential image fusion module, a morphological processing module and a motion estimation module; the space domain filtering module carries out filtering processing on the image and then transmits the image to the differential module for difference operation; and the differential image fusion module carries out weighting fusion processing on the image of the previous module, and the image is further processed by the morphological processing module and then transmitted to the motion estimation module for motion estimation operation after processing. The denoising device and the denoising method effectively complete the separation of a moving object and noise in the sequence image, effectively eliminate the noise, and improve the image quality.

The invention relates to the field of video information, and discloses a video clustering method, an ordering method, a video searching method and corresponding devices. The video clustering method comprises a clustering step used for clustering multiple pixels of which the similarity is higher than a predetermined threshold value together by aiming at each frame in a video, so as to obtain super pixels capable of imitating the minimum entity of human vision, wherein the similarity is calculated according to the colors, the positions and/or the motion characteristics of the pixels. Through conducting time-space domain self-correlation and cross-correlation analysis on pixel areas in the video and ordering the pixel areas in the video on the basis of the super pixels capable of imitating the minimum entity of the human vision, the invention has the advantage that videos can be accurately searched in a real-time manner even in a massive video database.

The invention discloses a no-reference video quality evaluation method based on three-dimensional spatial-temporal feature decomposition, which comprises the following steps of: constructing a qualityprediction network consisting of a spatial-temporal distortion feature learning module and a quality regression module, generating a no-reference training data set and a no-reference test data set, training the spatial-temporal distortion feature learning module and the quality regression module, and outputting the quality evaluation score value of each distorted video in the test set. The invention is used for accurately and efficiently extracting the quality perception characteristics of the time-space domain content from the input distorted video, the corresponding prediction quality scoreis obtained at the output end of the network, and the method has the advantages that the result is more accurate and the application is wider when the quality of the non-reference video is evaluated.

The invention discloses a video steganography analysis method based on a space-time domain local binary pattern. The video steganography analysis method includes step 1, classifying each frame in a video sequence to be analyzed by taking 8x8 blocks as a unit according to regional activity classifying standards; step 2, respectively establishing three-dimensional orthogonal planes, containing time-space coordinate axis, corresponding to classifying result, and making statistic analysis on LBP histograms of different three-dimensional orthogonal planes corresponding to each area in the classifying result; step 3, introducing a concept of activity factor, and enabling the activity factor and the corresponding LBP histograms to be in operation to acquire ST_LBP features; step 4, utilizing a Fisher Ratio method for feature selection, and realizing classifying recognition of steganographic information. Compared with the prior art, the video steganography analysis method has the advantages that by introducing the activity factor, interference, on detection result, caused by movement of an activity area object can be lowered, so that detection effectiveness is improved effectively; limitation of feature dimension is avoided, and valuing of radius R and field point number P in an LBP definition can be further expanded, so that relevance of space-time domain of the video sequence is utilized more effectively.

The invention discloses a method for detecting human faces on the basis of video. The method comprises: concerning foreground areas in video frames, using the space-time domain characteristics between the video frames to detect the human faces of the foreground areas through preprocessing, dividing the human face detecting process into two different modes of an inspection mode and a tracking mode, switching a detecting flow to the inspection mode if the human faces can not be detected in the tracking mode or the tracking mode forecasts that the human faces which are tracked will escape a monitoring area in next frame, searching the foreground area wholly, and recollecting human face information of the monitoring area. The invention can reduce the region area which needs searching in the detecting process, can reduce areas which need searching in the detecting process, can avoid the defect that a traditional AdaBoost algorism searches full frames, can greatly lower the computational complexity, and can improve the real-time performance of human face detection in the intelligent video monitoring application.

The invention relates to a virtual viewpoint video quality evaluation method. According to the invention, time domain blink distortion of virtual viewpoint video is calculated by taking all pixels in a space-time domain unit as a unit, so that erroneous estimation which is brought about by a pixel-to-pixel time domain blink distortion calculation mode for virtual viewpoint video human eye perception distortion is avoided. The virtual viewpoint video quality evaluation method not only considers distortion brought about by a depth image error but also considers distortion introduced by left and right viewpoint texture images when performing calculation on the time domain blink distortion of the virtual viewpoint video. The virtual viewpoint video quality evaluation method can effectively evaluate the time domain blink distortion which imposes great influences on the subjective quality in the virtual viewpoint video, thereby more conforming to a result of human eye subjective perception when the virtual viewpoint video quality is evaluated, and enabling virtual viewpoint video quality evaluation to be more accurate and more comprehensive.