46 results about "Subspace model" patented technology

Various embodiments accelerate high-resolution magnetic resonance spectroscopic imaging (MRSI). Various embodiments are built on a low-dimensional subspace model exploiting the partial separability of high-dimensional MRSI signals. For two and three dimensional MRSI with one spectral dimension, various embodiments sparsely sample the corresponding (k,t)-space in two complementary data sets, one with dense temporal sampling and high signal-to-noise ratio but limited k-space coverage and the other with sparse temporal sampling but extended k-space coverage. The reconstruction is then done by estimating a set of temporal / spectral basis functions and the corresponding spatial coefficients from these two data sets. The imaging technique of various embodiments can be used for high-resolution MRSI of different nuclei.

A computational method is disclosed for producing a sequence of high-resolution (HR) images from an input sequence of low-resolution (LR) images. The method uses a structured subspace framework to learn pairs of LR dictionaries from the input LR sequence ‘and’ employ learned pairs of LR dictionaries into estimating HR images. The structured subspace framework itself is based on a pair of specially structured HR basis matrices, wherein a HR basis spans any HR image whose so-called polyphase components (PPCs) are spanned by the corresponding LR dictionary.

The invention discloses a local spline embedding-based orthogonal semi-monitoring subspace image classification method. The method comprises the following steps of: 1) selecting n samples serving as training sets and the balance serving as testing sets from image data sets, wherein the training sets comprise marked data and unmarked data; 2) building an extra-class divergence matrix and an intra-class divergence matrix by using the marked data; (3) training data characteristic space distribution by using a whole and building a Laplacian matrix in a local spline embedding mode; 4) according toa local spline, embedding an orthogonal semi-monitoring subspace model, and searching a projection matrix to perform dimensionality reduction on the original high dimension characteristic; 5) building a classifier for the training samples after the dimensionality reduction by using a support vector machine; and 6) performing the dimensionality reduction on the testing sets by using the projectionmatrix and classifying the testing sets after the dimensionality reduction by using the classifier. In the method, the information, such as image sample marking, characteristic space distribution andthe like, is fully utilized; potential semantic relevance among image data can be found out; and image semantics can be analyzed and expressed better.

The invention provides a speaker marking method and system based on density peak value clustering and variation Bayes, and belongs to the field of voiceprint identification and mode identification. The method includes: firstly establishing a training voice database, and obtaining a general background model and a subspace model; then obtaining an i-vector factor of each fragment of to-be-detected voice data through an i-vector factor extraction method; and obtaining the number of speakers of the to-be-detected voice data and the prior probabilities of the speakers at each moment by employing a density peak value clustering algorithm, performing iterative estimation on the posterior probability of each speaker corresponding to each fragment by employing variational Bayes, and obtaining a speaker marking result. According to the method and system, problems of uncertainty of estimation of initial values of the number of the speakers and the prior probability of each speaker at each moment and large deviation generated by easy influence of the initial values on the speaker marking performance in the prior art are solved, and the accuracy, the stability and the flexibility of speaker marking are improved.