35 results about "Sparse matrix factorization" patented technology

The invention discloses a single-channel monitor-free voice and noise separating method based on low-rank and sparse matrix decomposition. The method includes the steps of converting a time domain waveform of noise-contained voice to a time frequency domain via short-time Fourier transform to obtain a magnitude spectra with noises; decomposing the magnitude spectra with the noises into a sum of a noise magnitude spectra, a voice magnitude spectra and a residual noise magnitude spectra by a low-rank and sparse matrix decomposition algorithm; and finally reconstructing a voice time-domain waveform from the voice magnitude spectra via short-time Fourier transform. Without any priori information about voice and noise, the method is the single-channel monitor-free voice and noise separating method, pure voice can be separated from noise-contained voice by the aid of an algorithm, and the single-channel monitor-free voice and noise separating method is simple, effective, and particularly suitable for voice extraction in strong-noise environment.

The invention relates to a method for detecting abnormality of thermal infrared hyperspectral in emissivity domain based on block and low-rank prior. First, temperature and emissivity inversion were performed on the original image to obtain the emissivity map and temperature map of the image, and then the temperature map and radiance map were combined with the Potts-based image segmentation algorithm to segment the emissivity map into multiple homogeneous regions. It is observed that in locally homogeneous regions, the background signal has an enhanced low-rank nature, while the anomalies manifest spatial sparsity. Based on this observation, background pixels can be low-rank reconstructed from a set of fundamental background signals, while anomalies can be represented by sparse residuals. Then use the extracted background endmembers to perform low-rank sparse matrix decomposition on the original hyperspectral data matrix to remove part of the noise and obtain purer background information than the original image. Then, the Mahalanobis distance is combined with the original emissivity image and the background information to calculate the spectral difference between the anomaly and the background, so as to realize the separation of the anomaly and the background.

The invention discloses an image repairing and denoising method and system. For a designated original training image, the method includes the steps of decomposing a designated training sample image data matrix into a combined low-rank and sparse principal component feature coding matrix and sparse error matrix through the convex optimization technology by introducing the combined low-rank and sparse matrix decomposition concept, and determining a combined low-rank and sparse main component feature and error matrix of training image sample data according to the low-rank and sparse features of the training image sample data so that repairing and denoising can be conducted on original images probably containing errors and images subjected to repairing and denoising can be obtained. By means of the method and system, robust low-rank and sparse features of data are sufficiently considered while feature depiction is conducted on image data, defects in the prior are overcome, and image repairing and denoising performance and model robustness are improved.

The present disclosure is directed to systems and methods to perform absolute rotation estimation including outlier detection via low-rank and sparse matrix decomposition. One example method includes obtaining a relative rotation estimates matrix that includes a plurality of relative rotation estimates. The method includes determining values for a low-rank matrix that result in a desirable value of a cost function that is based on a low-rank and sparse matrix decomposition of the relative rotation estimates matrix. The cost function includes the low-rank matrix and a sparse matrix that is nonzero in correspondence of one or more outliers of the plurality of relative rotation estimates. The method includes determining an absolute rotations matrix that includes a plurality of absolute rotations based at least in part on the values of the low-rank matrix that result in the desirable value of the cost function.

The invention discloses a speech enhancement method based on the principle of non-negative low rank and sparse matrix decomposition. In this method, the noisy speech signal is first smoothed, framed and discrete Fourier transformed to obtain the noisy speech spectrum; then the noisy speech amplitude spectrum of each frame is arranged as a column vector in chronological order to form a noisy speech time-frequency Matrix, by performing non-negative low-rank and sparse matrix decomposition on the noisy speech time-frequency matrix, a non-negative low-rank and sparse matrix is ​​obtained; using the sparse matrix and noisy speech phase reconstruction to enhance the speech spectrum, and finally through the inverse Fourier Transformation results in enhanced speech in time domain form. The invention has strong adaptability to noise, does not need endpoint detection and model training, has few parameters and is easy to adjust, has good performance in strong noise environment, and has good application prospect.

The invention discloses an image quality evaluation method based on low-rank sparse matrix decomposition. The method first randomly divides an input image into a training image set and a test image set, and converts the color distorted image in the training image set into a grayscale distorted image, and then Perform sparse and low-rank matrix decomposition to obtain an eigenvector. After combining the two eigenvectors, the combined subjective MOS score is sent to the support vector regression machine for training, and the trained support vector regression machine is obtained. Finally, the test image Also follow the above steps to extract feature vectors, send them to the trained support vector regression machine for testing, and obtain objective image quality evaluation results. The method of the present invention decomposes the input distorted image into a low-rank matrix and a sparse matrix to realize the effective separation of the foreground object and the background of the image, thereby extracting features from the foreground object and the background respectively, and using the extracted features to evaluate the image quality, which improves the efficiency of Accuracy of reference image quality assessment.

The present invention provides a method for classifying defects of injection molding products based on machine learning, which includes the following steps: collecting training samples; inputting the training samples into a classifier for training to obtain a trained classifier; The image of the product is preprocessed; the image of the product to be classified is input into the trained classifier to obtain the classification label, and then the one-hot encoding is transcoded to obtain the label matrix; the label matrix is ​​stacked to obtain the label containing the label information. Stacked matrix; separate the data of the stacked matrix to obtain a low-rank matrix and a sparse matrix, and decompose the sparse matrix into the noise of the training sample and the noise of the image of the product to be classified; iterate the low-rank matrix and take the optimal solution after iteration The corresponding label matrix is ​​used as the optimized label matrix; the injection molding product system converts the optimized label matrix into a machine signal to realize the control of the defect classification of the injection molding product by the machine on the production line.

The invention belongs to the digital image processing field and is for eliminating mole stripe patterns in fabric images. The fabric image mole stripe elimination method based on low-rank sparse matrix decomposition is characterized in that, local self-similarity of fabric textures and energy concentration distribution characteristics of the mole stripe in a frequency domain are utilized, a low rank sparse matrix decomposition model is constructed, the fabric textures are separated from the mole stripe pattern, and thereby a problem of mole stripes in the fabric image can be solved. The fabric image mole stripe elimination method based on low-rank sparse matrix decomposition is mainly applied to digital image processing.