185 results about "Random field ising model" patented technology

Apparatus and methods according to some embodiments of the present invention use a graphical model, such as a Markov random field model, to represent principal driving directions within an environment. The model has a plurality of nodes representing spatial locations within the environment, and the principal direction for each node is determined probabilistically using linear features detected within an image of the environment. Apparatus and methods according to embodiments of the present invention can be used in improved autonomous navigation systems, such as robotic vehicles.

The invention discloses a non-uniform image motion blur removing method based on a deep neural network. The non-uniform image motion blur removing method comprises two main steps of training deep neural network parameters, and estimating and removing motion blur by applying a deep network to an image. The deep network structure consists of a convolution network layer and a forward network layer; the network model training process comprises the following steps: generating a blur image block and a blur kernel pair by using a natural image, and training the deep neural network model parameters by using the data; providing a motion blur image, decomposing the image into an image block set with overlapped areas, inputting each image block into motion blur probability distribution corresponding to deep neural network output image blocks, and further acquiring different motion blur kernels per pixel points of the image by optimizing a Markov random field model; finally, acquiring a blur removed image by using a deconvolution algorithm based on the estimated motion blur kernels. Due to good learning capability of the deep neural network, non-uniform motion blur of the image can be precisely estimated, and clear images can be further obtained.

The invention discloses an automatic sleep staging method of a single-lead electroencephalogram. A training model comprises a feature extraction module and a staging optimization module, the feature extraction module comprises CNNs (convolutional neural networks) (1) and an Softmax layer (2), the staging optimization module comprises bi-directional LSTM (long-short term memory) recurrent neural networks (3) and a CRF (corticotropin releasing factor) conditional random field model (4), and the CNNs (1), the Softmax layer (2), the LSTM recurrent neural networks (3) and the CRF conditional random field model (4) are sequentially connected. The method only needs the single-lead sleep electroencephalogram, portable and comfortable sleep monitoring requirements are met, temporal and spatial characteristics of the electroencephalogram are sufficiently excavated according to the convolutional neural networks and the recurrent neural networks, the method has dynamic learning capacity and can adapt to great changed environments of diseases, the staging optimization module sufficiently considers relation between the front and the back of N 30s of electroencephalogram data, and the staging accuracy and the generalization ability of the model are improved.

The invention discloses a method for segmenting HMT images which is based on the nonsubsampled Contourlet transformation. The method mainly solves the problem that the prior segmentation method has poor area consistency and edge preservation, and comprises the following steps: (1) performing the nonsubsampled Contourlet transformation to images to be segmented and training images of all categories to obtain multi-scale transformation coefficients; (2) according to the nonsubsampled Contourlet transformation coefficients of the training images and the hidden markov tree which represents the one-to-one father and son state relationship, reckoning the model parameters; (3) calculating the corresponding likelihood values of the images to be segmented in all scale coefficient subbands, and classifying by examining possibility after integrating a labeled tree with a multi-scale likelihood function to obtain the maximum multi-scale; (4) updating category labels for each scale based on the context information context-5 model; and (5) with the consideration of the markov random field model and the information about correlation between two adjacent pixel spaces in the images to be segmented, updating the category labels to obtain the final segmentation results. The invention has the advantages of good area consistency and edge preservation, and can be applied to the segmentation for synthesizing grainy images.

A method and apparatus for detecting vascular landmarks in a 3D image volume, such as a CT volume, is disclosed. One or more guide slices are detected in a 3D image volume. A set of landmark candidates for multiple target vascular landmarks are then detected based on the guide slices. A node potential value for each landmark candidate is generated based on an error value determined using spatial histogram-based error regression, and edge potential values for pairs of landmark candidates are generated based on a bifurcation analysis of the image volume using vessel tracing. The optimal landmark candidate for each target landmark is then determined using a Markov random field model based on the node potential values and the edge potential values.

The present invention is a method of generating a model of a random field which has directionally varying continuity. First, a tentative model for the random field is specified. Second, connected strings of nodes within the model are identified. Third, a spectral simulation on each of the strings of nodes is performed to determine updated values for the random field. Finally, the tentative model is updated with the data values from the spectral simulations.

The invention provides a method and a system for discovering new words in open fields. The method includes receiving corpora to be processed, conducting format conversion and word segmentation processing on the corpora to obtain a plurality of text messages, extracting characteristic messages of the plurality of text messages, judging whether combinations of adjacent text messages of a part of the text messages in the plurality of text messages are new words, conducting new word boundary labeling on the adjacent text messages on yes judgment, estimating parameters of a conditional random field model according to the labeled text messages and characteristic messages and identifying the surplus text messages according to the estimated conditional random field model to obtain new words of the surplus text messages. By means of the method, the new word boundary labeling is conducted on the text messages, the parameters of the conditional random field model are estimated, the plurality of text messages are identified to obtain the new words in the text messages, the new words in various fields can be identified, and meanwhile identification efficiency is improved.

The invention discloses a method for detecting remote sensing image change based on non-parametric density estimation, which mainly solves the problem that the estimation to the statistic items which relevant to a change type and a non-change type in a differential chart in the prior art has error. The realizing process of the method is that inputting two remote sensing images with different time-phase, removing noise of each channel of each image, obtaining noise-removing images of the two time-phase, and constructing difference images through adopting the change time-vector method, gathering the difference images into change type and a non-change type through applying K-means clustering algorism, obtaining the initial sorting results, and estimating the statistic items relevant to the change type and the non-change type in differential images through adopting non-parameter density estimation, carrying out the self-adapting space restriction combining the variable weight markov random field model, and obtaining the final change detecting results. The experimentation shows that the invention can effectively keeps the structure information of the images, removes insulation noise, improves the change detection processing efficiency, and can be used for the fields of disaster surveillance, land utilization and agriculture investigation.

The invention provides a metal artifact correction method, comprising: first, employing a method based on a Markov random field model to divide metal areas, thereby solving the problem of inaccurate division of metal areas by a traditional metal artifact removal algorithm, and avoiding more secondary artifacts caused by inaccurate positioning of metal projection borders; and employing a prior interpolation algorithm to solve the problem of unsmooth borders of metal projection areas after linear interpolation; in addition, metal edge framework structure information is fed back to corrected projection data, thereby solving the problem that metal edge high contrast substance structures may be lost in correction results by a traditional interpolation algorithm.

The invention belongs to the technical field of SAR (synthetic aperture radar) image change detection, and discloses an SAR image change detection method based on a support vector machine and a discriminative random field. The SAR image change detection method based on the support vector machine and the discriminative random field includes the steps: normalizing gray values of two original time phase images, and extracting corresponding gray characteristic differences and textural characteristic differences in the processed images; forming difference characteristic vectors; extracting boundary strength of each pixel in a difference image by the aid of weighted average ratio operators; selecting training samples in the difference image, and expressing the training samples by the aid of the corresponding difference characteristic vectors to obtain initial category labels of testing samples and posterior probabilities of the category labels of the testing samples by the aid of the training support vector machine; obtaining initial support vector machine-discriminative random field models; updating the support vector machine-discriminative random field models to obtain final category labels and change detection results of the corresponding testing samples.

The invention discloses a method for detecting two-time-phase remote sensing image change. In the method for detecting two-time-phase remote sensing image change, a gaussian process classifier is combined with a Markov random field model, effective utilization of image space information is reinforced, and change detecting precision is improved.

A method and apparatus are provided for training and using a hidden conditional random field model for speech recognition and phonetic classification. The hidden conditional random field model uses features, at least one of which is based on a hidden state in a phonetic unit. Values for the features are determined from a segment of speech, and these values are used to identify a phonetic unit for the segment of speech.

The invention provides a target color recognition method for video surveillance. The method comprises the steps that a color recognition target area is extracted from a video image; colors of the target area are initialized; color category detection is carried out on all color components h, s and l of the target area; the proportion of each color of the color recognition target area is calculated, and the color with the largest proportion serves as the color of the color recognition target area. According to the method, the Markov random field model is adopted, influence on color recognition from environment changes and target posture changes can be reduced according to the neighborhood position relation of the color recognition target area, and therefore the color recognition rate can be increased, a color target can be effectively positioned in intelligent surveillance, and strong evidence can be provided for solving criminal cases like violation of laws and rules.

The invention is applicable to the technical field of image processing, and provides a depth image processing method and a depth image processing device. The method comprises the following steps: inputting a depth image and a corresponding color image, wherein the depth image and the color image are pre-registered and are of the same size; extracting a boundary image from the color image, and determining a boundary mask based on the boundary image; filtering the depth image in multiple preset directions through a preset filter based on the boundary mask to get corresponding multiple filtering candidate results; and fusing the multiple filtering candidate results into a depth information completion result of the depth image through a Markov random field model. According to the invention, the boundary of the color image is extracted, the boundary mask is introduced, the depth image is filled in multiple preset directions through an improved local bilateral filter, and the filling results of the multiple preset directions are fused into a final information completion result, and thus, the integrity of depth information is guaranteed.

The invention discloses a named entity identification method based on adversarial training. The method comprises the following steps: obtaining relevance features among judicial field characters through RoBERTa model training and Bi-LSTM training; splicing the two relevance features together, and predicting a training sample by using a conditional random field model to obtain a prediction result;according to the method, external word vectors and word vectors of different dimensions can be introduced to be combined with judicial domain text word mixing vectors of different dimensions, confrontation disturbance is carried out on the mixing word vectors in a judicial domain text, and the accuracy of model recognition is improved.

The invention discloses an automatic filling method for a medical health record based on speech recognition. The method comprises that a training set file used for speech recognition training is obtained in advance; a speech recognition model of a deep neural network is called, and the model is trained to obtain an improved speech recognition model; a condition random field model is called and trained to obtain an information extraction model; voice data to be recognized is input, recognized by the improved speech recognition model automatically, and converted into text data; and feature of each word is obtained to form text feature data, the text feature data is marked, labels of each word in the text feature data is obtained, the words are logged into corresponding data according to the labels, and a record filled automatically is obtained. Compared with an automatic filling result in the prior art, objective facts and main diagnosis are combined, the reliability is very high, medical staff is effectively freed from tedious work of writing the health records of patients manually, the method is simple, and the accuracy is high.