53 results about "Local consistency" patented technology

A system and method for extracting structure from stereo that represents the scene as a collection of planar layers. Each layer optimally has an explicit 3D plane equation, a colored image with per-pixel opacity, and a per-pixel depth value relative to the plane. Initial estimates of the layers are recovered using techniques from parametric motion estimation. The combination of a global model (the plane) with a local correction to it (the per-pixel relative depth value) imposes enough local consistency to allow the recovery of shape in both textured and untextured regions.

A system and method for performing a distributed consistency check of a clustered file system. File system functions for loading an inode and / or buffer tree are modified so that in response to either of these functions being invoked, a consistency check is performed. The consistency check verifies both local consistency on a node of the clustered file and a distributed check across the nodes of the storage system

There is provided a traffic placement method in a communications network, the communications network comprising a plurality of nodes, the nodes being connected to one another by links, the method comprising selecting a (possibly non-strict) subset from a given set of traffic flow demands and calculating a plurality of paths for the selected demands under consideration of a set of constraints using an algorithm hybridisation combining backtrack search with local consistency techniques (BT+CS) and guiding search by the use of one or more probe generators, that is, search techniques that solve a routing sub-problem or an arbitrary relaxation of the traffic placement problem. By using a hybrid algorithm that integrates other solvers (search techniques) into BT+CS through the use of probe generators, a more powerful search strategy can be achieved compared to BT+CS or the individual search techniques.

The invention relates to a text generation image method based on cross-modal similarity and a generative adversarial network. The method comprises the steps that S1, training a global consistency model, a local consistency model and a relation consistency model by using matched and unmatched data, wherein three models are used for obtaining global representation, local representation and relationrepresentation of a text and an image respectively; S2, obtaining global representation, local representation and relation representation of the to-be-processed text by utilizing the trained global consistency model, local consistency model and relation consistency model; S3, connecting the global representation, the local representation and the relation representation of the to-be-processed textin series to obtain text representation of the to-be-processed text; S4, converting the text representation of the to-be-processed text into a condition vector by utilizing an Fca condition enhancement module; and S5, inputting the condition vector into a generator to obtain a generated image. Compared with the prior art, the method has the advantages of considering local and relation informationand the like.

The present invention discloses an arithmetic of picking up magnetic resonance imaging cerebral active regions by sorting based on mode identification, which comprises the steps that cerebral active regions are extracted based on the multi-element mode distance between fine activity modes in partial cerebral regions for the pretreatment of an fMRI image; a partial consistent cerebral region is obtained by clustering; the combined activities of a plurality of tissues inside the partial consistent cerebral region are used for constructing the multi-element mode; the multi-element distance function is constructed by a mode sorting method to measure the separable characters of the partial cerebral region motion under different stimulation conditions, so as to judge whether the cerebral region is activated or not. The present invention indicates the cerebral motions under different stimulation conditions by multi-element mode information formed by multiple tissues inside the partial cerebral region directly, the multi-element mode can reflect the partial cerebral motion state over all, and multi-element statistical distance can be effectively integrated with the information in the partial cerebral region to measure the difference between different cerebral activate states, so the multi-element mode and the multi-element statistical distance ensure that the arithmetic of the present invention can detect the fine cerebral action mode more completely than the traditional fMRI analyzing technology.

The invention discloses a laser scanning SLAM indoor three-dimensional point cloud quality evaluation method based on deep learning. The method comprises the steps that S1, acquiring high-quality point cloud through a laser scanning SLAM device; S2, performing degradation on the high-quality point cloud to obtain a simulation point cloud; S3, carrying out track measurement analysis on the simulation point cloud; S4, extracting a plane from the high-quality point cloud and the simulation point cloud, performing local consistency noise analysis and geometric rule analysis on the plane, and quantifying the quality of the point cloud; S5, segmenting the high-quality point cloud and the simulation point cloud to obtain point cloud blocks; S6, normalizing the point cloud blocks and then inputting into a Point Net + + neural network for model training, and obtaining a network model; S7, performing point cloud quality analysis on the point cloud to be evaluated through the step S4 to obtain a point cloud quality level value; and S8, predicting the to-be-evaluated point cloud through the neural network model obtained in the step S6, and judging whether the point cloud belongs to a high-quality point cloud or a quality-reduced point cloud. The invention provides a method for quantifying the quality of point cloud, and establishes a classification standard and a framework for evaluating an indoor three-dimensional point cloud model under an SLAM system.

The invention discloses a time-space condition information based moving object detection method. The method comprises the following steps: building a target detection time-space domain model through considering the significance of human visual time-space domains; calculating a conditional probability that a detection image belongs to a time-space domain reference background; carrying out nonlinear transformation on the conditional probability through negative logarithm checking so as to extract time-space conditional information; carrying out weighted summation on the conditional information of image in an adjacent domain through considering the local consistency of image characteristics; and as characteristics, carrying out object detection by using a linear classifier. The conditional probability is rapidly calculated by using a color histogram, and an image block replacing a single pixel is adopted for carrying out modeling and detection, thereby reducing the algorithm complexity and the storage space requirements; and through combining with an image block difference pre-detection mechanism, the object detection speed is increased. The method disclosed by the invention is low in algorithm complexity, less in storage space requirements and high in algorithm instantaneity, and can effectively suppress the background disturbance interference and isolate the noise influence; and by using the method, the real-time detection of moving objects on the existing computers is realized, therefore, the method is applicable to embedded intelligent camera platforms.

The invention proposes a panoramic image tone consistency correction method and system. The method includes the following steps: extracting a content-consistent area from overlapped areas of all adjacent image pairs, and extracting a color corresponding relation between images according to a cumulative probability histogram of the content-consistent area at an equal probability interval; defining a tone transformation model with relatively high flexibility, and optimizing global tone consistency, image contrast and gradient details while designing a global energy function; and through solving model parameters with strict global optimality, applying respective tone correction models to all images to perform tone consistency correction, and outputting a processing result. The panoramic image tone consistency correction method and system utilize the tone corresponding relation of overlapped areas between adjacent images, and can effectively eliminate relatively large tone difference existing among images; the problems of tone overflow, dynamic range narrowing and gradient loss relative to an original image can be suppressed while tone consistency is corrected, a correction model of global joint optimization does not need to define a specific reference image, and the problem of an accumulative error can be effectively reduced.

A method includes selectively routing a physical address to an originating device instead of to a shared memory at controller that manages conversion of device virtual addresses to physical addresses. The physical address corresponds to a data access from a virtual device. The method may provide local coherency at a computing system that implements virtualized input / output.

The invention provides a method and a system for quickly matching images on the basis of feature states and global consistency. The method includes utilizing detection angular points as to-be-matched feature points and identifying feature neighborhood states of the feature points by the aid of state templates; utilizing the feature points as centers and computing main directions of descriptors; turning the main directions, describing feature neighborhoods of the feature points, combining binary texture features and statistic features with one another and creating RBT-OGMH feature descriptors; matching the images for two types of different descriptors by the aid of different types of similarity measurement; quickly determining transformation matrixes by the aid of consistent features of spatial domains on the basis of error fluctuation amplitude minimization clustering, and eliminating error matching point pairs to obtain ultimate correct matching point pairs. According to the technical scheme, the method and the system have the advantages that the method and the system have rotational invariance and certain scale invariance, and problems of image blurring, illumination change, low contrast and image deformation can be effectively solved; the method and the system are high in matching speed and precision as compared with the prior art.

The invention discloses a load transfer method and system in a metadata cluster, and relates to a metadata load transfer method among a plurality of servers in a metadata cluster. The method includes the steps that at least two metadata servers are established and used for establishing a load balancing module, a remote subdirectory pre-application module, a remote subdirectory establishing module, a remote subdirectory initialization module, a remote subdirectory deleting module, a remote subdirectory asynchronous releasing module and a remote subdirectory object recycling module; a client is established, a directory establishing request is sent to a metadata server where a parent directory is located, and a remote subdirectory object identifier is obtained and added into the directory entry of the parent directory; a directory deleting request is sent to the metadata server where the parent directory is located, and the remote subdirectory object identifier is deleted from the directory entry of the parent directory. The load transfer method and system in the metadata cluster only need a local consistency guarantee mechanism, do not need a distributed consistency guarantee mechanism, and effectively improve the remote subdirectory establishing and deleting performance.

The invention belongs to the field of image processing, computer vision and deep learning, and specifically discloses a nested loop consistency-based generative adversarial network image style transfer method. According to the method, mapping, from content images such as photos and the like to claborate-style painting, of style images is realized on the basis of a convolutional neural network, a generator, a discriminator in residual connection with the generator, and nested loop consistency and generative adversarial network-based loss function training. The method is capable of effectively solving image style transfer tasks which cover geometric changes; and moreover, the method does not need to pair data sets in a one-to-one correspondence manner, and is capable of learning mapping fromlearning content pictures to style pictures and the mapping from style pictures to content pictures at the same time.

The invention discloses a Bluetooth mesh gateway data aggregation reporting method of the invention, which comprises the following steps: step 1, the equipment status of the sub-equipment is reportedto the Bluetooth mesh gateway; step 2, the Bluetooth Mesh gateway caches the status data reported by all the sub-devices locally; 3, the Bluetooth mesh gateway compares the aggregated data to be updated of each sub-device; 4, the data is aggregated into a group by the Bluetooth mesh gateway and forwarded to the cloud server. One set of data includes heartbeat packets. The data format of heartbeatpacket is child device id + device online status + function data + product type. Child device id, device online status, function data, and product type are all represented in one byte. After the MeshGateway powers up, the child device status is synchronized to the cloud server in batches. The device status can be checked at the mobile terminal, such as on-line, on-off status and so on. Remote group control equipment, equipment status gradually synchronized, the final state and local consistency.

The present invention belongs to the field of signal and information processing technology, and relates to an abnormal tone rate evidence-obtaining method of fuzzy operation in falsification if image. It is characterized by that it provides a digital image evidence-obtaining method based on abnormal tone rate. Said method is directed against fuzzy operation most commonly used in digital image falsification and distortion, and can utilize the digital image local color attribute abnormality due to fuzzy operation, and utilize definition of abnormal tone collection and abnormal tone rate and quantifies the local consistency of image tone and correlation extent so as to further detect the digital image undergone the processes of falsification and fuzzy operation.

A method for achieving consensus amongst a distributed and decentralized set of computers, devices or components in a network interacting via messaging is presented. The method does not rely on the availability of an overall ledger that is consulted for every interaction. Rather, the interacting components communicate directly with each other via messages that contain proofs of consistency that may be used to achieve local consistency amongst the interacting components. Local consistency guarantees global consistency. For regulatory and record keeping purposes, use of an overall ledger may be contemplated for regulatory and record keeping purposes. The latter may be updated by the interacting devices via an asynchronous updating mechanism.

The invention discloses a remote-sensing image semi-supervised projection dimension reducing method based on local consistency. The method includes the following steps: (1) dividing a remote-sensing image data set; (2) generating a semantics similar matrix, a neighbor matrix and a location consistency matrix; (3) mixing a label matrix and the neighbor matrix; (4) generating neighbor mean vector; (5) generating an alien divergence matrix, a similar divergence matrix and a local consistency divergence matrix; (6) calculating an optimum projection matrix; and (7) conducting projection and dimension reducing. The method adopts the semi-supervised learning based on local consistency binding and improves recognition rate under small sample learning condition.

The present invention proposes a moving object detection method with the combination of sample consistency and a local binary pattern. A background model is established for each pixel of a video imagesequence in each frame of the image, the differential operation of a current image after an Nth frame of image of a visible light video sequence and a previous frame of image is carried out, images which are subjected to differential operation are grayed and binarized, a stable background point is determined, and a target foreground is extracted. Through a flooding filling algorithm and connected-domain detection, a large number of voids is filled and discrete noises are removed, the concept of the stable background point is proposed, and a foreground target is accurately judged in the case of a sudden change in light with the combination with the robustness characteristics of the LBP (Local Binary Pattern) for illumination change.

Disclosed in the invention is a saliency-map-based Laplacian cooperation compression radar imaging method. The method comprises the following steps: step 1, carrying out processing by using a range Doppler algorithm to obtain a low-resolution ISAR image; step 2, constructing a sparse dictionary; step 3, carrying out PTC conversion based on a formula on a preliminary ISAR imaging result S^to obtain a saliency map; step 4, constructing a Laplacian matrix of the saliency map; and step 5, in order to obtain a high-resolution ISAR image, carrying out processing by using a basis pursuit algorithm and an analytical method alternately to solve an optimum theta and T. According to the invention, on the basis of the regional homogeneity priori assumption of image data, the background clutter is reduced and noises are suppressed; and the imaging quality is effectively improved.

The invention relates to a functional magnetic resonance data processing method utilizing partial uniformity method which comprises, (1) acquiring and preprocessing brain function magnetic resonant data, (2) sorting the time sequence, (3) local consistency measurement. reading, etc. }, but also for but also for data analysis of non-task condition (such as resting) and brain function magnetic resonance data analysis for other non-linear conditions.

The invention discloses a method of judging correctness of a terrain matching result in BSLAM, the method mainly being a method of judging whether a terrain matching result obtained during judgment of AUV bathymetric simultaneous localization and mapping (BSLAM) is correct. According to the method, judgment of terrain matching effectiveness is achieved through continuous iterative calculation of a consistency function as long as terrain matching data and milemeter data are input. A self-inspection method and a multi-window method are introduced during iteration, and the aims are to avoid local optimum and ensure global consistency and local consistency at the same time. The beneficial effects are that the method does not rely on auxiliary equipment, is high in calculating efficiency, enables local optimum to be avoided, and meets requirements of local consistency and global consistency at the same time.

The invention provides a multimodal data subspace clustering method based on global consistency and local topology. The method comprises obtaining a Laplacian matrix corresponding to each piece of modal data, establishing a multimodal data subspace clustering model according to the Laplacian matrixes, obtaining a self-expression matrix corresponding to each piece of modal data through the multimodal data subspace clustering model, selecting the first self-expression matrixes from all the self-expression matrixes of the various pieces of modal data, and clustering the first self-expression matrixes to obtain a clustering result. The multimodal data subspace clustering method based on global consistency and local topology is capable of obtaining better clustering performance and enhancing the robustness.

The invention discloses a regional homogeneity hash load equalizing method of a private cloud based on dynamic feedback. The regional homogeneity hash load equalizing method comprises the following steps of enabling a service finding module and a health examination module in the private cloud to obtain a service providing end and a real-time load thereof, calculating the weight for continuing to provide services, and utilizing a regional homogeneity hash algorithm with weight to calculate a harsh route list, so as to provide services for the request of the client. The regional homogeneity hash load equalizing method has the advantages that the weight of the service providing end can be dynamically updated in real time, the property of load equalizing service is improved, and the load condition of each service providing end is equalized.

The invention relates to a video abnormity detection method. Firstly, overall motion estimation is completed by using a gray projection algorithm according to the characteristic of local consistency of video jitter, in combination with a blocking thought; then a frame sequence which can represent the video jitter is selected to serve as an effective frame according to the longest path algorithm, jitter parameters such as the jitter rate, jitter frequency and jitter amplitude of the video jitter are computed according to an overall motion parameter of the effective frame, and an influence factor is set for the jitter parameters; and at last a video jitter degree is computed via a weighted average method. According to the method provided by the invention, limitation of existing video jitter detection technologies is overcome, image jitter detection and jitter degree estimation can be effectively achieved, and meanwhile the method is well applicable to the videos with local jitter.

The invention discloses a brain nuclei Granger causal analysis method based on RYGB surgery weight losing. The method comprises the following steps of obtaining functional magnetic resonance data in a resting state scanning mode, preprocessing the data, carrying out regional homogeneity analysis on the preprocessed data, defining regions of interest according to the difference area of the brain of an obese patient one month before and after surgery, carrying out Granger causal analysis, selecting two areas from selected regions of interest at random, extracting time sequences of the two areas, calculating a Granger causal value between the two areas by adopting a first-order autoregressive model, and carrying out normalization, wherein the step of preprocessing the data comprises the steps of time rectification, head moving rectification and space standardization. According to brain nuclei Granger causal analysis method based on the RYGB surgery weight losing, the RYGB surgery weight losing changes the functions of brain reward loops, brain cognitive loops and brain drive loops and the mutual cause and effect relation among the loops, the crapulent degree of the obesity patient is relieved, and iconography proofs are provided for development of central nervous system medicine.

The embodiment of the invention provides a generative adversarial network training method, an image completion method, equipment and a storage medium. In some exemplary embodiments of the present application, firstly, image completion training is performed by using a sample image including a missing region to obtain a preliminary completion network and a preliminary completion image; secondly, performing discriminator training by utilizing the preliminary completion image to obtain a first local context discriminator, a second local context discriminator and a global discriminator; and finally, performing confrontation training on the primary completion network through the combination of a first local context discriminator, a second local context discriminator and a global discriminator byusing the sample image containing the missing region to obtain an image completion network, wherein the first local context discriminator maintains the local consistency of image completion, the global context discriminator maintains the global consistency of image completion, and the second local context discriminator ensures the authenticity of texture information and the consistency of a completion center area and a surrounding area.

The invention discloses a method of identifying sharp geometric edge points in 3D point cloud based on normal consistency. The method is specifically implemented according to the following steps: calculating the normal direction and normal consistency of each point in 3D point cloud; looking for local extreme points with normal consistency in the neighborhood range of each point; judging whether there is a sharp edge nearby according to the number of local extreme points in the neighborhood range, and if there is a sharp edge, further classifying all the points in the neighborhood range according to the normal distribution, and finally judging whether each point is a sharp edge point according to whether the neighborhood center point is on the classification edge; and using the method above to locate all sharp edge points in the point cloud. The edge point detection method of the invention can adapt to any change of edge shape. No model assumption is needed for the intersecting surfaces forming a sharp edge. Straight and curved edges as well as corners formed by intersecting surfaces can be detected. Moreover, all kinds of sharp geometric edges can be identified from 3D point cloud.

The invention discloses a herpes zoster neuralgia curative effect prediction method and system based on functional magnetic resonance. The herpes zoster neuralgia curative effect prediction method comprises the steps: obtaining a first image which comprises a functional magnetic resonance image and a structure image of a herpes zoster neuralgia patient; performing parameter calculation according to the first image, the parameter comprising a local consistency value and a fractional low-frequency amplitude; and predicting the curative effect of herpes zoster neuralgia by adopting a machine learning method according to a parameter calculation result. According to the invention, the functional magnetic resonance image of the first image is combined with machine learning to predict the curative effect of herpes zoster neuralgia; and nerve activity signs such as local consistency values and fractional low-frequency amplitudes are searched through the machine learning method, and the medicine curative effect of a patient can be objectively pre-judged, and a doctor can formulate a medical scheme for the patient more reasonably, and the working efficiency of the doctor is improved. The herpes zoster neuralgia curative effect prediction method can be widely applied to the field of medical image pattern recognition.