48 results about "Distance classifier" patented technology

The invention discloses an external insulation filth status identification method based on visible-light images. According to the method, images of an insulator are photographed, the images of the insulator are extracted by utilization of an image segmentation technology, related characteristic quantities are calculated and selected, and the insulator filth degree is determined by utilization of a distance classifier. The method has advantages comprising capability of detecting the insulator filth degree under a condition of no power cut, no need of installing any equipment in a pole or a tower, low operation cost and maintenance cost, simple operation, safe and reliable usage and high detection precision.

The invention discloses a human behavior recognition and classification method based on an accelerometer. The method includes the first step of collecting human behavior samples as a training set, the second step of searching for a projection matrix U which is optimal for the recognition and classification of the training set, the third step of carrying projection on no-labeled data, and the fourth step of classifying the projected data by using a minimum distance classifier to obtain a recognition result. According to the human behavior recognition method, a partial approximate linear hypothesis is carried out on adjacent blocks formed by labeled data so as to enable the distance between different types of samples on the blocks to be large enough, positional sequence information of the same type of samples is reserved as far as possible through class sigmoid function penalty factors, and finally a global objective function is established on the basis of the objective functions on all blocks. The human behavior recognition method can reserve the information of the distance between the samples in a higher dimensional space properly, and reduces dependence of recognition models on artificial tagging samples, and the recognition effect is superior to a representative human behavior recognition method based on linear discriminant analysis.

The invention discloses a scene recognition method based on a layered Gaussian hybrid model. The method comprises the following steps: extracting the layered Gaussian hybrid model corresponding to all images in a training image library and obtaining lie algebra descriptors of the layered Gaussian hybrid model; receiving images to be sorted and extracting the lie algebra descriptors of the layered Gaussian hybrid model of the images to be sorted; obtaining the distances from the lie algebra descriptors of the layered Gaussian hybrid model of the images to be sorted to the lie algebra descriptors of the layered Gaussian hybrid model of all images in the training image library; and sorting the images to be sorted according to the obtained distances by a minimum mid-value distance sorter and returning sorting results. According to the method provided by the invention, character representation of the layered Gaussian hybrid model of the images is optimized, so that the scene recognition performance is greatly improved.

The invention provides an increment study classification method under limited storage resources, which belongs to the technical field of pattern recognition. The method adopts a minimum distance classifier and comprises the following concrete steps: firstly, pre-classifying new samples, adding the new samples which are correctly pre-classified to the corresponding subset of the classifier, adding the new samples which are wrongly pre-classified to the set of wrong samples, and carrying out K-mean clustering on the samples in the set of wrong samples; then respectively selecting representative samples for each subset in the classifier and the set of wrong samples, adding the subsets in the set of wrong samples to the classifier after selection, and updating the classifier; and finally, adopting the updated classifier to classify the new samples. In the invention, by selecting the representative samples, not only the studied knowledge is saved, but also the new knowledge is acquired, and higher sample recognizing accuracy is achieved on the basis of reducing the storage cost and calculating the cost.

The invention discloses a computing method and a computing method system for crop disease index. The computing method comprises the following steps: acquiring image of crop and pre-processing the image; dividing the pre-processed image into multiple sub-images and extracting color characters of the sub-images; using a minimum distance classifier for identifying the disease types and disease level of the sub-images according to the color characters of the sub-images; computing the crop disease index according to the disease types and disease level of the sub-images. The method solves that the disease index computing of the prior art depends on experience and knowledge of pathology of field crop protection persons, the statistical result is inaccurate and manual computation is required. The method is conducive to improve the prevention and treatment level for crop disease index and promote the implementation of precision agriculture, and novel method and technology are provided for the crop disease prevention and treatment field.

The invention provides a multi-classifier integration-based image character recognition method. The method comprises the following steps of: converting a colored to-be-identified image into a grayscale image; carrying out binary processing on the grayscale image and segmenting an image region with character information; segmenting each Chinese character from a whole character image; extracting grid features and direction features of each Chinese character; selecting stroke density total length features to carry out first-layer rough classification by adoption of a minimum distance classifier; and respectively selecting peripheral features, the grid features and the direction features to complete second-layer classification matching by adoption of a nearest-neighbor classifier. The method has the advantages that the character recognition has relatively strong anti-jamming capability and relatively strong character local structure description capability, and is less influenced by stroke widths; by adoption of a classifier integration technology of complementing and combining the minimum distance classifier and the nearest-neighbor classifier, a system is more reliable; and the characters can be intelligently recognized, so that the adaptability of the system is improved and the recognition rate is high.

The invention provides a plastic part defect detection method based on multiple threshold intervals. The method comprises the following steps: S1, inputting a grayed image of a surface picture of a detected part; S2, obtaining a defect eigenvalue vector of the grayed image of the detected part and a defect threshold interval in multiple threshold intervals of the defect; and S3, calculating a defect type corresponding to the defect eigenvalue vector by using a least distance classifier according to the defect threshold interval. Through the plastic part defect detection method, multiple threshold intervals are set, and the least distance classifier is used; the automation detection of the surface defects of the plastic parts is achieved; the detection efficiency of the surface defects of the plastic parts is improved; the manpower, the material resources and the financial resources are reduced.

The method of classifying raw EEG signals uses a classification method based on nuclear features extracted as dominant singular values from an EEG signal segment using singular value decomposition (SVD) and a class means-based minimum distance classifier (CMMDC) to classify a patient's EEG signals. From a mean EEG signal, a set of zero-centered EEG signals are calculated, and from the zero-centered EEG signals and a standard deviation of the EEG signals, a unit variance is calculated for each component. Using the standardized component signals a nuclear matrix is calculated, to which singular value decomposition is applied to generate a set of singular values. The CMMDC is applied to class means associated with first and second classes and a nuclear feature vector to classify the patient's EEG signals as belonging in either the first or second class.

The invention relates to a bearing fault diagnosis method which is characterized by including the following steps: (1) obtaining multiple groups of vibration acceleration signal data of the spindle bearing of a wind generating set in different states, randomly extracting a plurality of groups of vibration acceleration signal data as standard sample data, and taking the other groups of vibration acceleration signal data as to-be-detected sample data; (2) adaptively decomposing the standard sample data and the to-be-detected sample data to obtain a series of intrinsic rotational components; (3)making a time-domain analysis of the instantaneous amplitude and instantaneous phase information of the first intrinsic rotational components in the standard sample data and the to-be-detected sampledata, and extracting bearing fault feature vectors; (4) inputting the extracted fault feature vector of the standard sample data to a neighbor distance classifier for training to get a trained fault diagnosis model; and (5) inputting the extracted bearing fault feature vector of the to-be-detected sample data to the trained fault diagnosis model for fault identification to obtain the fault state of the bearing. The bearing fault diagnosis method of the invention can be widely applied to bearing fault diagnosis.

A discrimination-oriented correlated characters fusion recognition system of an image belongs to the technical field of telecommunication, comprising the following steps: first, determining a projection matrix via an iterative process according to a training image; second, respectively projecting the training image and test image to a characteristic space by utilizing the projection matrix, extracting the training characteristic and test characteristic and then carrying out parallel fusion respectively on the characteristics to form a new characteristic; at last, extracting characteristics with more discriminability by utilizing linear discriminant analysis method and adopting minimum distance classifier, thus identifying the category to which the test image belongs. The system can more naturally depict the relation between row vectors and the relation between column vectors of an image matrix; the extracted characteristics can more intensively reflect the image information; further discrimination-oriented processing can extract characteristics with more discriminability; and the system can improve not only processing speed but also recognition performance by being applied to the image recognition.

The invention discloses a feature dimension-reduction optimization method for Chinese character recognition. The feature dimension-reduction optimization method comprises a first step of conducting preprocessing and feature extraction on a Chinese character sample and conducting LDA dimension reduction changing on the extracted Chinese character features, a second step of conducting classification and recognition by means of a minimum Euclidean distance classifier, a third step of regarding the sample with wrong classification and recognition as a newly-added sample, adding the newly-added sample to an original sample set, and conducting the dimension reduction changing again by means of a learning method of ILDA incremental linear judgment, a fourth step of conducting the classification and recognition again by means of the minimum Euclidean distance classifier, and a fifth step of repeating the third step and the fourth step, outputting the LDA optimizing parameter after repeated iterative calculation, and enabling the LDA optimized parameter to be used for the classification and recognition of Chinese characters. The feature dimension-reduction optimization method for the Chinese character recognition overcomes the defect that an existing LDA changing method can not effectively optimize the LDA transformation matrix parameters by means of recognition classification and information, and has the advantages that the performance of the LDA feature dimension reduction changing and the accuracy rate of the character recognition can be greatly improved.

The invention relates to a training method and system of a sleep-state classifier, wherein the method comprises the following steps: constructing feature vectors of sample data of a plurality of sleep-state types and a cluster center formed by aggregating the feature vectors, and establishing an objective function according to the feature vectors and the cluster center of the feather vectors; using the objective function to characterize the distance between the minimized sample data of same types and dictionary atoms, and the distance among maximized atoms of different types; respectively selecting a plurality of the feature vectors from the sample data of a plurality of the sleep-state types to be taken as initial values of the atoms, distributing various sample data to the atoms and solving the objective function, thus obtaining a classification dictionary; using the classification dictionary to classify the sample data, comparing the types and the distances of the atoms which are closest to the sample data, if the distance is less than a preset threshold value, judging that the types of the sample data are consistent with the types of the atoms; training the sleep-state classifier according to the classified sample data. By adopting the training method and system of the sleep-state classifier, a more accurate sleep-state classifier can be trained.

The invention discloses a human ear recognition method based on fusion of a gradient direction histogram and a local binary pattern, which solves the problem of low human ear recognition rate in a human ear image. According to the method, firstly, the gradient direction histogram of the image is extracted, dimensionality reduction is carried out through a principal component analysis method, thenthe texture features of the image are extracted through the local binary pattern, then the two features are fused, and finally classification is carried out through a minimum distance classifier. According to the invention, through the multi-feature fusion, the recognition rate of human ear recognition is improved, and the method has good implementability and effectiveness.

The invention provides a base station identification method in a single-frequency network positioning system. The method, based on the positioning system, converts the problem of base station identification to the problem of data classification, and the base station identification is realized through a minimum-distance classifier; a state prediction model is utilized to estimate motion parameters of a mobile station, and thus hardware requirement of the system is lowered; and to get a better base station identification effect in a non-line-of-sight environment, an interaction multi-model method is utilized to reduce non-line-of-sight effect, and thus more accurate mobile station object information is obtained. Two different functions, position estimation and base station identification, of the positioning system are closely associated, so that the method is economic and effective, and is suitable for all digital television standards.

The invention provides a texture classification method on the basis of fractional Fourier transform (FrFT). The texture classification method comprises the following steps: (1) acquiring an image and calculating one-dimensional discrete FrFT of each pixel of the image in four directions; (2) carrying out descending sorting on each obtained one-dimensional discrete FrFT according to amplitudes; (3) calculating a fractional Fourier frequency histogram; and (4) carrying out classification on the texture image by utilizing a chi2- statistical distance classifier. The invention provides the texture classification method which is on the basis of FrFT and comprehensively utilizes the fractional Fourier frequency histogram and the chi2- statistical distance classifier. The texture classification method provided by the invention has the main effect of avoiding the problem of cross terms of Wigner distribution.

The invention discloses an iris recognition method based on wavelet packet decomposition, and the method comprises the steps: collecting a human eye image, carrying out the iris image preprocessing, obtaining a processed iris image, modulating the diagonal high-frequency information of the wavelet packet decomposition of the iris image into an iris feature code through a threshold value, extracting the iris features, and finally, adopting a Hamming distance classifier to perform classification and matching calculation on iris features, and judging whether the two feature codes are from the same iris or not by calculating the similarity between the current iris feature code and iris codes in an iris template library, so that iris images are classified. The iris features extracted through the iris feature recognition method have good classification performance, so that the iris recognition precision is improved.

The invention belongs to the field of computer vision, particularly relates to a compressed domain-oriented video content comparison system, an optimization method and a comparison method, and aims tosolve the problem of low efficiency of completing video content comparison by using full decoding information. The comparison system comprises: a feature learning module which is used for respectively obtaining feature maps of multiple modes based on multiple pieces of compressed domain information of an input video; a multi-modal compressed domain information fusion module which is used for carrying out information fusion on the multi-modal feature maps output by the feature learning module to obtain a fusion feature vector of the input video; a second module which is configured to obtain the L1 distance of the fusion feature vector of the two input videos; and a classifier which is a binary classification network and is configured to perform binary classification of the comparison result based on the L1 distance output by the second module. According to the invention, high-level semantic information of the video content can be effectively extracted, and high comparison speed and high performance of the video content are ensured.

The invention relates to a method for identifying vehicle types of vehicles of the same brand based on spatial position information. The method includes: collecting images of the vehicles; performing histogram equalization on the images of the vehicles; setting the size of the license plate, and adjusting the histogram The image after image equalization is stretched and rotated; the feature area of ​​N vehicle models to be tested is extracted according to the position of the corrected image relative to the license plate; the feature vector of each feature area is calculated; the minimum distance classifier is used to classify each feature vector Classify and compare with the reference feature vector of the brand vehicle in the database to identify the specific model. The method of the invention extracts the feature area of ​​the license plate and then identifies the relevant parameters of the feature area, thereby improving the recognition accuracy of the vehicle type.