66 results about "Temporal consistency" patented technology

In accordance with the preferred embodiment of the present invention, a method of and system for improving temporal consistency of an enhanced signal representative of at least one frame using a sharpness enhancement algorithm with an enhancement gain are provided. The method comprises the steps of: receiving the enhanced signal comprising at least one frame, obtaining an enhancement gain for each pixel in the frame, retrieving an enhancement gain value of each pixel in a reference frame from a gain memory using motion vectors, identifying if the frame is an I, P or B frame type and determining an updated enhancement gain for an I frame type by calculating a gain map for use in the sharpness enhancement algorithm. The updated enhancement gain of each pixel is equal to enhancement gain previously determined for use in the sharpness enhancement algorithm. In addition, the method includes storing the updated enhancement gain to gain memory, and applying the updated enhancement gain to the sharpness enhancement algorithm to improve temporal consistency of the enhanced signal. The method may further comprise the step of further improving the updated enhancement gain by applying a motion adaptive temporal IIR filter on the updated enhancement gain.

A method for generating spatial-temporal consistency depth map sequences based on convolutional neural networks for 2D-3D conversion of television works includes steps of: 1) collecting a training set, wherein each training sample thereof includes a sequence of continuous RGB images, and a corresponding depth map sequence; 2) processing each image sequence in the training set with spatial-temporal consistency superpixel segmentation, and establishing a spatial similarity matrix and a temporal similarity matrix; 3) establishing the convolution neural network including a single superpixel depth regression network and a spatial-temporal consistency condition random field loss layer; 4) training the convolution neural network; and 5) recovering a depth maps of a RGB image sequence of unknown depth through forward propagation with the trained convolution neural network; which avoids that clue-based depth recovery method is greatly depended on scenario assumptions, and inter-frame discontinuity between depth maps generated by conventional neural networks.

An object-oriented software application is provided for receiving updates that change state of an object model and reporting those updates to requesting users. The application includes a database application for storing data; an object model driver for writing updates into the database; a notification system for notifying about the updates; and, a plurality of external monitors for reading the updates. In a preferred embodiment the object model produces multiple temporal snapshots of itself in co-currency with received events, each snapshot containing associated update information from an associated event and whereupon at the time of occurrence of each snapshot coinciding with an event the notification system notifies the appropriate external monitor or monitors, which in turn access the appropriate snapshot, performs calculations thereupon if required and renders the information accessible to the users.

The invention discloses a method for generating a spatial-temporal consistency depth map sequence based on a convolution neural network, which can be used in a film and television work 2D-to-3D technology. The method comprises the following steps: (1) collecting a training set, wherein each training sample in the training set is composed of a continuous RGB image sequence and a corresponding depth map sequence; (2) carrying out spatial-temporal consistency super pixel segmentation on each image sequence in the training set, and constructing a spatial similarity matrix and a temporal similarity matrix; (3) constructing a convolution neural network composed of a single super pixel depth regression network and a spatial-temporal consistency conditional random field loss layer; (4) training the convolution neural network; and (5) for an RGB image sequence of unknown depth, using the trained neural network to recover a corresponding depth map sequence through forward propagation. The problem that a depth recovery method based on clues relies too much on the scene hypothesis and the problem that the frames of a depth map generated by the existing depth recovery method based on a convolution neural network are discontinuous are avoided.

A distributed time reversal mirror array (DTRMA) system includes a plurality of independent, sparsely distributed time reversal mirrors (TRMs). Each of the TRMs includes an antenna; a transceiver connected to the antenna for transmitting a signal toward a target, for receiving a return, reflected signal from the target, and for retransmitting a time-reversed signal toward the target; means for phase-locking and for maintaining spatial and temporal coherences between the TRMs; and a computer including a machine-readable storage media having programmed instructions stored thereon for computing and generating the time-reversed retransmitted signal, thereby providing a phased array functionality for the DTRMA while minimizing distortion from external sources. The DTRMA is capable of operating in an autonomous, unattended, and passive state, owing to the time-reversal's self-focusing feature. The beam may be sharply focused on the target due to the coherently synthesized extended aperture over the entire array.

The invention provides a region-based multi-scale spatial-temporal visual saliency detection method that includes the following steps of: multi-scale video segmentation and spatial saliency entity construction the process of which comprises: firstly executing a time super pixel model in order to divide a video into spatial-temporal regions in various scale levels; extracting the motion informationin each scale level and the characteristics of each frame, creating characteristic mapping, combing the characteristic mapping to generate a spatially salient entity for each scale-level region; thenindividually using an adaptive time window smoothness saliency value for each region, and bringing in time consistency to form a spatial-temporal saliency entity trans frame; and finally, generatinga spatial-temporal saliency map for each frame by fusing multi-scale spatial-temporal saliency entities. The method overcomes the limitation of using a fixed number of reference frames, and introduces a new metric of the adaptive time window, can maintain the time consistency between consecutive frames of each entity in the video and reduce the fluctuation of the target between frames.

The invention discloses a weak supervision abnormal behavior detection method based on time sequence consistency. The method comprises the following steps: constructing a behavior detection system, importing a behavior video pair into the behavior detection system, and forming a time sequence matrix constructed by the behavior video pair after processing. Based on the temporal consistency and smoothing characteristic of human behavior, a behavior unit pair is formed to obtain relevant behavior segments, and the start and end frames of relevant abnormal actions in the behavior segments are located. Based on weak supervision, the obtained behavior segments are learned to obtain the operation classifier which can judge various behavior characteristics. Based on dictionary learning and solvingthe detection criteria of judging behavior features in sparse coding reconstruction operation classifier, a new behavior judgment logic is formed to detect various abnormal behavior classes. The weakly supervised abnormal behavior detection method provided by the invention is easy to learn the characteristics of the related behavior classification without manually labeling the boundary of the frame to establish the related behavior model.

The invention discloses a web temporal object model-based outdated webpage information automatic discovering method, belongs to the field of research of data quality, and relates to the technical fields of temporal Web, network information quality estimation, semantic comprehension and extraction of temporal information, constraint and reasoning of temporal information, automatic screening of consistency of webpage information and the like. Aiming at the phenomenon of low web data quality caused by temporal inconsistency, a Web temporal object model is established on the basis of different temporal sensitivities of different web-pages, network data temporal consistency constraint relation, reasoning mechanism and an algebraic operation rule are constructed, automatic screening is performed and outdated information in the Web webpage is promoted, and information with timeliness and relevancy is provided for a network user. The method can be widely applied to the aspects such as quality ordering of similar websites and search ordering of time perception, and the Web information quality is improved to a certain extent.

The invention discloses a three-phase standard ground electric signal generating method and device and a primary and secondary fusion switch ground fault detecting function testing method and system.The three-phase standard ground electric signal generating method comprises performing modulation and error compensation on ground fault typical electric signals to obtain a corresponding three-phasewaveform sequence; performing digital-to-analogue conversion, amplification and PI (proportional-integral) correction on the three-sequence waveform sequence to obtain three-phase standard ground electric signals. The primary and secondary fusion switch ground fault detecting function testing method comprises loading the three-phase standard ground electric signals onto the primary side of the mutual inductor of a primary and secondary fusion switch to be tested; transmitting electric signals output from the secondary side of the mutual inductor to a supported intelligent terminal; according to SOE (state of energy) data uploaded by the intelligent terminal, determining accuracy of a ground fault detecting function. The three-phase standard ground electric signal generating method and device and a primary and secondary fusion switch ground fault detecting function testing method and system can help generate high-precision, high-stability and high-temporal-consistency three-phase standard ground electric signals and improve detecting accuracy of primary and secondary fusion switch ground fault detecting functions.

An image segmentation system for performing image segmentation on an image data includes an image preprocessing module, a motion analyzing module, a detection module, a classification module, and a multi-dimensional detection module. The image data has a plurality of image stacks ordered chronologically that respectively have a plurality of images sequentially ordered according to spatial levels, wherein one spatial level is designated as a first stack. The image preprocessing module transforms the images into binary images while the motion analyzing module finds a repeating pattern in the binary images in the first stack and accordingly generates a repeating motion result. The classification module generates a classification result based on a spatial and an anatomical assumption to classify objects. The multi-dimensional detection module generates segmentation results for stacks above and below the first stack using spatial and temporal consistency of geometric layouts of object structures.

A video signal processor enhancing video information evaluates candidate vectors of enhancement algorithms utilizing an error function biased towards spatio-temporal consistency with a penalty function. The penalty function increases with the distance—both spatial and temporal—of the subject block from the block for which the candidate vector was optimal. Enhancements are therefore gradual across both space and time and the enhanced video information is intrinsically free of perceptible spatio-temporal varying artifacts.

A computer implemented system for generating one or more data structures is described, the one or more data structures representing an unseen composition based on a first category and a second category observed individually in a training data set. During training of a generator, a proposed framework utilizes at least one of the following discriminators—three pixel-centric discriminators, namely, frame discriminator, gradient discriminator, video discriminator; and one object-centric relational discriminator. The three pixel-centric discriminators ensure spatial and temporal consistency across the frames, and the relational discriminator leverages spatio-temporal scene graphs to reason over the object layouts in videos ensuring the right interactions among objects.

The invention provides a control method based on a long short-term memory neural network incremental model, and belongs to the technical field of intelligent control of information physical systems. According to the method, virtual models are established for a to-be-assembled product, an assembly robot and an assembly task entity which are on an assembly line through modeling software, and the movement pose of the assembly robot is accurately controlled on the basis of the dynamics principle; a feasible assembly scheme is planned through virtual assembly, real-time accurate mapping between thevirtual models and actual models is achieved, the actual assembly meets the error requirements of virtual assembly, so that the spatio-temporal consistency of the virtual models and equipment entityduring the movement process is maintained. According to the control method, the accuracy of a virtual assembly result can be improved, and it is ensured that the actual assembly is successfully completed.

The invention discloses a method for analyzing distribution and transformation daily load typical curves on the basis of structure features. The method focuses on the peak / valley temporal consistency of similar daily load curves when the distribution and transformation daily load typical curves are acquired. The method includes selecting curves without abnormal data from distribution and transformation historical daily load curve databases and utilizing the curves without the abnormal data as to-be-analyzed objects; utilizing categories with high duty cycles as daily load curve typical categories by the aid of a classification process; solving the distribution and transformation daily load typical curves. The method has the advantages that peak / valley typical periods which are acquired statically are utilized as main bases in daily load curve classification procedures, and accordingly distribution and transformation daily load peak / valley occurrence time can be controlled advantageously.

A video sequence having a plurality of frames is received. A feature in a first frame from the plurality of frames and a first position of the feature in the first frame are detected. The position of the feature in a second frame from the plurality of frames is estimated to determine a second position. A displacement vector between the first position and the second position is also computed. A plurality of content characteristics is determined for the first frame and the second frame. The displacement vector is spatially diffused with a spatial filter over a frame from the plurality of frames to generate a spatially diffused displacement vector field. The spatial filter utilizes the plurality of content characteristics. A temporal filter temporally diffuses over a video volume the spatially diffused displacement vector field to generate a spatiotemporal vector field. The temporal filter utilizes the plurality of content characteristics.

The invention discloses a visual loopback detection method based on an auto-encoding network. The visual loopback detection method comprises the following steps: 1, acquiring an image; 2, calculatinga memorability score of the image, comparing the memorability score with a set memorability score threshold value, determining whether to reserve the image or not, and determining a key frame; 3, inputting the screened key frames into a trained convolutional self-encoding network, and obtaining a GIST global feature f after noise reduction; 4, taking out a feature fpre from the feature database, calculating cosine similarity of two feature vectors fpre and f, comparing the cosine similarity with a set similarity threshold, determining whether the frame is a candidate frame or not, and performing loop-back verification; and 5, in a loopback verification stage, on the premise of completing space consistency verification, carrying out time consistency verification, enabling one image to meetloopback conditions and become loopback candidate frames in a continuous motion process, enabling the obtained key frames to become candidate frames within a certain time range, and finally determining loopback only when the conditions are met.

A method and an apparatus for generating superpixels for a sequence of frames. A feature space of the sequence of frames is separated into a color subspace and a spatial subspace. A clustering is then performed in the spatial subspace on a frame basis. In the color subspace a clustering is performed on stacked frames. An erroneous temporal consistency of a superpixel associated to a frame is detected by performing a similarity check between a corresponding superpixel in one or more past frames and one or more future frames using two or more metrics. The affected superpixels is future frames are corrected accordingly.

The invention provides a fast video defogging method based on space-time consistency constraint. The method comprises: dividing the input image by using a quadtree to obtain an instantaneous atmospheric light value; and performing spatial-temporal consistency constraint on the instantaneous atmospheric light value, solving a global atmospheric light value, solving an initial transmissivity according to the global atmospheric light value and the input image, performing edge maintaining filtering on the initial transmissivity, solving a fine transmissivity, and performing defogging restoration according to the input image, the global atmospheric light value and the fine transmissivity. According to the method, the global atmospheric light value is subjected to space-time consistency constraint, so that the flickering phenomenon possibly generated when a video image is processed by a single-frame defogging algorithm is avoided. In the transmissivity estimation, the fog concentration of the image is judged, and the minimum value of the transmissivity is constrained by using the judgment result, so that the self-adaptive capability of the method under different fog conditions is ensured. A comparison algorithm is mostly adopted in transmissivity estimation, and hardware logic implementation and transplantation are very convenient.

The invention relates to the field of intelligent video monitoring and deep learning, in particular to an action normalization detection method and device based on time consistency comparative learning, and the method comprises the steps: firstly constructing a data set for videos which are acquired through a camera and are marked by a first number and are not marked by a second number, and the first number is smaller than the second number; secondly, performing strong and weak data enhancement on an unlabeled video, extracting features, inputting the features into a time consistency behavior alignment network, outputting a feature map and a similar action starting and ending frame set between different samples, mapping sub-feature maps corresponding to the sets on the feature map, and constructing similar and different types of sub-feature map samples; sending to a comparative learning network to extract space-time discriminant features; the first number of labeled videos are sent to a pre-trained network for transfer learning, and behavior categories are output; and finally, the behavior normalization is judged according to the inter-frame behavior category change, and if the behavior normalization is not standard, early warning is given out.

The invention discloses a physical action attribute analysis and recognition method for intelligent teaching space-time correlation. The method comprises the following steps: tracking and positioningthe position of a learner in each frame according to a real-time visual target tracking mode of multi-scale correlation filtering; And based on a multi-frame image joint segmentation mode, establishing a deformable part model of the learner, and realizing space-time correlation estimation of single-frame overall image action attribute perception. The scheme provided by the invention can improve the spatial-temporal structure prediction and recognition capability of the high-quality physical action education resource visual media of the education resource public service platform, and the structural, continuous and spatial-temporal consistency of physical actions and the like are analyzed to realize the analysis and recognition of the physical actions and the accurate prediction of the change trend of the physical actions.

A VSR approach with temporal consistency using generative adversarial networks (VistGAN) that requires only the training HR video sequence to generate the HR / LR video frame pairs, instead of the pre-artificial-synthesized HR / LR video frame pairs, for training. By this unsupervised learning method, the encoder degrades the input HR video frames of a training HR video sequence to their LR counterparts, and the decoder seeks to recover the original HR video frames from the LR video frames. To improve the temporal consistency the unsupervised learning method provides a sliding window that explores the temporal correlation in both HR and LR domains. It keeps the temporal consistent and also fully utilizes high-frequency details from the last-generated reconstructed HR video frame.

The invention discloses a three-dimensional signal processing method based on space-time adversarial. A space-time adversarial network comprises a cycle generator, an optical flow estimation network and a spatio-temporal discriminator. The cycle generator is used for recursively generating a high resolution video frame from the low resolution input; the optical flow estimation network learns motion compensation between frames; the spatio-temporal discriminator may take into account spatial and temporal aspects and penalize unreal temporal discontinuities in the result without excessively smoothing the image content. According to the method, the problem that the visual effect is remarkably reduced under diverse and fuzzy motions in overclocking and super-resolution is solved, meanwhile, time information in the video is fully utilized, and the spatio-temporal consistency of the video after super-resolution is guaranteed.