611 results about "Independent component analysis" patented technology

A method and apparatus for recognizing and searching for a face using 2nd-order independent component analysis (ICA) are provided. The method for describing feature points uses 2nd-order ICA d to describe a facial image space and improve recognition performance in various illumination conditions. According to the method and apparatus, use of pose or illumination invariant face descriptor enables retrieval of human faces and authentication of a specific individual.

EKG sensors ((150) are placed on a patient (140) to receive electrocardiogram (EKG) recording signals, which are typically combinations of original signals from different sources, such as pacemaker signals, QRS complex signals, and irregular oscillatory signals that suggest an arrhythmia condition. A computing module (120) uses independent component analysis to separate the recorded EKG signals. The separated signals are displayed to help physicians to analyze heart conditions and to identify probably locations of abnormal heart conditions. At least a portion of the separated signals can be further displayed in a chaos phase space portrait to help detect abnormality in heart conditions.

A method for coding and decoding seismic data acquired, based on the concept of multishooting, is disclosed. In this concept, waves generated simultaneously from several locations at the surface of the earth, near the sea surface, at the sea floor, or inside a borehole propagate in the subsurface before being recorded at sensor locations as mixtures of various signals. The coding and decoding method for seismic data described here works with both instantaneous mixtures and convolutive mixtures. Furthermore, the mixtures can be underdetemined [i.e., the number of mixtures (K) is smaller than the number of seismic sources (I) associated with a multishot] or determined [i.e., the number of mixtures is equal to or greater than the number of sources). When mixtures are determined, we can reorganize our seismic data as zero-mean random variables and use the independent component analysis (ICA) or, alternatively, the principal component analysis (PCA) to decode. We can also alternatively take advantage of the sparsity of seismic data in our decoding process. When mixtures are underdetermined and the number of mixtures is at least two, we utilize higher-order statistics to overcome the underdeterminacy. Alternatively, we can use the constraint that seismic data are sparse to overcome the underdeterminacy. When mixtures are underdetermined and limited to single mixtures, we use a priori knowledge about seismic acquisition to computationally generate additional mixtures from the actual recorded mixtures. Then we organize our data as zero-mean random variables and use ICA or PCA to decode the data. The a priori knowledge includes source encoding, seismic acquisition geometries, and reference data collected for the purpose of aiding the decoding processing.

The coding and decoding processes described can be used to acquire and process real seismic data in the field or in laboratories, and to model and process synthetic data.

A cardiac monitoring and/or stimulation method and systems provide monitoring, defibrillation and/or pacing therapies, including systems detecting and/or treating cardiac arrhythmia. A system includes a housing coupled to a plurality of electrodes configured for subcutaneous non-intrathoracic sensing. A signal processor receives a plurality of composite signals associated with a plurality of sources, separates a signal from the plurality of composite signals using blind source separation, and identifies a cardiac signal. The signal processor may iteratively separate signals from the plurality of composite signals until the cardiac signal is identified. A method of signal separation includes detecting a plurality of composite signals at a plurality of locations, separating a signal using blind source separation, and identifying a cardiac signal. The separation may include a principal component analysis and/or an independent component analysis. The composite signals may be filtered before separation using band-pass filtering, adaptive, or other filters.

The invention discloses a method for recognizing a road traffic sign for an unmanned vehicle, comprising the following steps of: (1) changing the RGB (Red, Green and Blue) pixel value of an image to strengthen a traffic sign feature color region, and cutting the image by using a threshold; (2) carrying out edge detection and connection on a gray level image to reconstruct an interested region; (3) extracting a labeled graph of the interested region as a shape feature of the interested region, classifying the shape of the region by using a nearest neighbor classification method, and removing a non-traffic sign region; and (4) graying and normalizing the image of the interested region of the traffic sign, carrying out dual-tree complex wavelet transform on the image to form a feature vector of the image, reducing the dimension of the feature vector by using a two-dimension independent component analysis method, and sending the feature vector into a support vector machine of a radial basis function to judge the type of the traffic sign of the interested region. By using the method, various types of traffic signs in a running environment of the unmanned vehicle can be stably and efficiently detected and recognized.

The system and method for spectral analysis uses a set of spectral data. The spectral data is arranged according to a second dimension, such as time, temperature, position, or other condition. The arranged spectral data is used in a signal separation process, such as an independent component analysis (ICA), which generates independent signals. The independent signals are then used for identifying or quantifying a target component.

The invention relates to a dynamic visual attention area extraction method based on features in the technical field of machine vision. The method comprises the following steps: first, an independent component analysis method is adopted for carrying out sparse decomposition to mass natural images so as to obtain a group of filtering base functions and a group of corresponding reconfigurable base functions and the input images are divided into small RGB blocks of m multiplied by m and projected on a group of the base to obtain the features of the images; second, effective encoding principle is utilized to measure an incremental encoding length index for each feature; third, according to the incremental encoding length index, the remarkable degree of each small block is processed through the energy reallocation of each feature and finally a remarkable map is obtained. The method can reduce a 'time slice', realize continuous sampling, therefore, data of different frames can direct the processing of remarkable degrees together, and the problem that the remarkable degrees of different frames require independent process is solved so as to realize the dynamic performance.

A system and method for separating a mixture of audio signal into desired audio signals (430) (e.g., speech) and a noise sign (440) is disclosed. Microphones (310, 320) are positioned to receive the mixed audio signals, and an independent component analysis (ICA) processes (212) the sound mixture using stability constraints. The ICA process (508) uses predefined characteristics of the desired speech signal to identify and isolate a target sound signal (430). Filter coefficients are adapted with a learning rule and filter weight update dynamics are stabilized to assist convergence to a stable separated ICA signal result. The separated signals may be peripherally-processed to further reduce noise effects using post-processing (214) and pre-processing (220, 230) techniques and information. The proposed system is designed and easily adaptable for implementation on DSP units or CPUs in audio communication hardware environments.

The invention provides a magnetic tile surface defect feature extraction and defect classification method based on machine vision. A concrete algorithm comprises a first step of building a 5-scale and 8-direction Gabor filter bank suitable for magnetic tile surface defect feature extraction, conducting filtering to an original image and obtaining a 40-width component plot, a second step of respectively extracting a gray average and a variance feature of the component plot and forming a 80-dimension feature vector, a third step of conducting dimensionality reduction to the original 80-dimension feature vector through a principal component analysis (PCA) method and an independent component analysis (ICA) method, removing relevance and redundancy and obtaining a 20-dimension feature vector, a fourth step of conducting normalization pretreatment to feature vector data, wherein the original data are normalized between zero and one, and a fifth step of adopting a grid method and a K-CV method to achieve SVM parameter optimization at first and training an SVM model using training sample data offline, wherein pretreated testing sample data are input into a support vector machine during online testing, and automatic classification and identification of defects can be achieved. The feature extraction method can effectively filter interference and prominent defects of magnetic tile surface texture, extracted features can reflect defect information accurately, data values are small, and a classifier used for classifying the defects can achieve defect identification fast and accurately online.

Electrocardiogram (ECG) recorded signals are processed by a computer-implemented method to substantially remove extraneous signals to produce intermediary signals, and to separate the intermediary signals using a non-orthogonal transformation method such as independent component analysis to produce independent components of signals. The separated signals are displayed to help physicians to analyze medical conditions and to identify locations of abnormal heart conditions.

A method and system decomposes a cardiac signal, such as an electrocardiogram (ECG) signal, into components. The components are then usable to assist in the detection of an abnormal heart condition. More particularly, a single lead sensor is used to generate a single lead cardiac signal. The cardiac signal is segmented into a set of cycle segments according to detected heart waveforms. The cycle segments are aligned and used to generate a set of cross-sectional signals. The cross-sectional signals are aligned and presented as inputs to a signal separation process, which separates the cardiac signal into a set of components. The components may be grouped according to predefined criteria. The components or groups may be analyzed or displayed to assist in the detection of an abnormal cardiac signal, which may be indicative of an abnormal heart condition. In one example, the signal separation process is a non-orthogonal transformation method such as independent component analysis (ICA).

A system and method of signal denoising using Independent Component Analysis (ICA) and fractal dimension analysis of the signal components in the ICA domain is described. The signal components with fractal dimensions higher than a pre-determined threshold are automatically attenuated or canceled in order to alleviate the noise in the signal. The denoised signal is reconstructed using inverse ICA transform of the signal components.

The invention discloses a single channel blind source separation method, and belongs to the technical field of electronic information. The single channel blind source separation method is characterized by adopting an extreme point symmetric prolongation method, carrying out processing of removing an end effect to ensemble empirical mode decomposition, transforming one-way mixed signals to intrinsic mode functions (IMFs) by using the ensemble empirical mode decomposition, restraining noise, carrying out dimension reduction processing to multi-channel IMFs by utilizing principal component analysis, removing invalid components in the IMFs, and carrying out independent component analysis to multi-channel signals after dimensionality reduction to achieve blind source separation. Implementation steps comprise carrying out linear adding to the multi-channel signals and mixing the multi-channel signals to single-channel signals to transmit, recovering source signals simply, fast and effectively under the condition of not influencing later stage pattern recognition effect, and achieving the outputting of multi-channel outputs. The single channel blind source separation method has the advantages of being capable of separating the multi-channel frequency-spectrum-overlapped signals mixed to one channel under the condition of not influencing the later stage recognition effect.

Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.

Data is analysed by an independent component analysis to derive a transform matrix W encoding properties of the data. The data is encoded using this transform matrix W, and a watermark is embedded into it. Then that the inverse of the transform matrix is applied to obtain watermarked data. The presence of the watermark is found by applying the transform matrix again, and examining the result for the presence of the watermark. The invention is particularly, but not exclusively suitable for data which is image data 10.