141 results about "Gabor wavelet" patented technology

The present invention may be embodied in a method, and in a related apparatus, for classifying a feature in an image frame. In the method, an original image frame having an array of pixels is transformed using Gabor-wavelet transformations to generate a transformed image frame. Each pixel of the transformed image is associated with a respective pixel of the original image frame and is represented by a predetermined number of wavelet component values. A pixel of the transformed image frame associated with the feature is selected for analysis. A neural network is provided that has an output and a predetermined number of inputs. Each input of the neural network is associated with a respective wavelet component value of the selected pixel. The neural network classifies the local feature based on the wavelet component values, and indicates a class of the feature at an output of the neural network.

The invention relates to a face recognition method based on Gabor wavelet transform and local binary pattern (LBP) optimization. Two-dimensional Gabor wavelet transform can associate pixels of adjacent areas so as to reflect the change conditions of image pixel gray values in a local range from different frequency scales and directions. The feature extraction and the classification recognition are carried out on the basis of a face image two-dimensional Gabor wavelet transform coefficient. For a high-dimensional Gabor wavelet transform coefficient, overall histogram features are extracted by adopting the LBP, and then the image is blocked by utilizing priori knowledge to extract the features of each piece of LBP local histogram. The method has better recognition rate, better robustness to illumination and wide using prospect in the fields of biometric recognition and public security monitoring.

An innovative optical system and method is disclosed for analyzing and uniquely identifying high-order refractive indices samples in a diverse population of nearly identical samples. The system and method are particularly suitable for ultra-fine materials having similar color, shape and features which are difficult to identify through conventional chemical, physical, electrical or optical methods due to a lack of distinguishing features. The invention discloses a uniquely configured optical system which employs polarized sample light passing through a full wave compensation plate, a linear polarizer analyzer and a quarter wave retardation plate for producing vivid color bi-refringence pattern images which uniquely identify high-order refractive indices samples in a diverse population of nearly visually identical samples. The resultant patterns display very subtle differences between species which are frequently indiscernable by conventional microscopy methods. When these images are analyzed with a trainable with a statistical learning model, such as a soft-margin support vector machine with a Gaussian RBF kernel, good discrimination is obtained on a feature set extracted from Gabor wavelet transforms and color distribution angles of each image. By constraining the Gabor center frequencies to be low, the resulting system can attain classification accuracy in excess of 90% for vertically oriented images, and in excess of 80% for randomly oriented images.

An image processing technique which identifies pixels in images which are associated with features having a selected shape, such as but not exclusively step edge, roof, ridge or valley. The shape of the intensity profile in the image is compared in an intensity independent way with a shape model to select those pixels which satisfy the shape model and are thus associated with the feature of interest. This comparison is achieved by examining the phase and amplitude of a spectral decomposition of parts of the image profile in the spatial or spatio temporal frequency domain. This decomposition can be achieved using quadrature wavelet pairs such as log-Gabor wavelets. The difference between the odd and even components, known as the feature asymmetry, gives an indication of the shape of the feature. The analysis may be extended to the time domain by looking at the shape of the image profile across a time sequence of images, which gives an indication of the velocity of a moving feature. Pixels identified as belonging to a feature of the right shape are labelled with the value of feature asymmetry, the local amplitude, feature orientation and feature velocity, and this information can be used to improve the tracking of detected features through a sequence of images.

A face recognition method and apparatus using extended Gabor wavelet features are provided. In the face recognition method, extended Gabor wavelet features are extracted from a face image by applying an extended Gabor wavelet filter, a Gabor wavelet feature set is selected by performing a supervised learning process on the extended Gabor wavelet features, and the selected Gabor wavelet feature set is used for face recognition. Accordingly, it is possible to solve problems of a high error rate of face recognition and low face recognition efficiency caused from a limitation of parameters of the Gabor wavelet filter. In addition, it is possible to solve the problem of increased calculation complexity caused from using an extended Gabor wavelet filter and to implement robust face recognition which is excellent in dealing with a change in expression and illumination.

The invention discloses a face identification method which blends a global feature and a local feature based on an ICA. As a DCT can efficiently transit a high-dimensional face image into a low-dimensional space and reserves most of identifiable information of the image, the DCT is suitable for extracting the global feature of the image, while a Gabor wavelet transformation is suitable for extracting the local feature and the classification feature of the image, and the two features are widely applied to the face identification. Based on the two methods, we bring in the ICA technology to extract the independent Gabor feature and the independent DCT feature of the image, then efficiently blends the independent Gabor feature and the independent DCT feature to obtain a novel independent feature, enables the novel independent feature to have the local information of the Gabor feature and the global information of the DCT feature, efficiently reduces the dimension of a feature vector, and removes superfluous features. Finally, the blended independent feature is used for the SVM to realize the face classification identification.

The invention belongs to the technical field of image processing, discloses a method for identifying and positioning power transmission line insulators in unmanned aerial vehicle aerial images, and solves the problems in the prior art that the detection precision of an identification algorithm of the insulators is not high, the robustness is low, and the identification algorithm is easy to be affected by sample number. A group of Gabor wavelet basis with different sizes and different directions and training sample images are taken as convolutions so as to form a group of characteristic vectors which accurately describe sample image texture characteristics. A random forest machine learning algorithm with a semi-supervised learning mode is used to train sample data sets of the known category and the unknown category so as to obtain an insulator identification model. Through the mode from left to right and from top to bottom, a detection window with the same size as the training sample traverses the input images with different sizes. The detection window combining the identification model detects and positions the positions of the insulators in the input images with different sizes. And finally the accurate positions of the insulators in the input image with the original size are determined by using a non-maximum inhibition method.

The invention discloses a method for extracting the characteristic of a natural image based on dispersion-constrained non-negative sparse coding, which comprises the following steps of: partitioning an image into blocks, reducing dimensions by means of 2D-PCA, non-negative processing image data, initializing a wavelet characteristic base based on 2D-Gabor, defining the specific value between intra-class dispersion and extra-class dispersion of a sparsity coefficient, training a DCB-NNSC characteristic base, and image identifying based on the DCB-NNSC characteristic base, etc. The method has the advantages of not only being capable of imitating the receptive field characteristic of a V1 region nerve cell of a human eye primary vision system to effectively extract the local characteristic of the image; but also being capable of extracting the characteristic of the image with clearer directionality and edge characteristic compared with a standard non-negative sparse coding arithmetic; leading the intra-class data of the characteristic coefficient to be more closely polymerized together to increase an extra-class distance as much as possible with the least constraint of specific valuebetween the intra-class dispersion and the extra-class dispersion of the sparsity coefficient; and being capable of improving the identification performance in the image identification.

The invention requests to protect an identification method for a human facial expression based on two-step dimensionality reduction and parallel feature fusion. The adopted two-step dimensionality method comprises the following steps: firstly, respectively performing the first-time dimensionality reduction on two kinds of human facial expression features to be fused in the real number field by using a principal component analysis (PCA) method, then performing the parallel feature fusion on the features subjected to dimensionality reduction in a unitary space, secondly, providing a hybrid discriminant analysis (HDA) method based on the unitary space as a feature dimensionality reduction method of the unitary space, respectively extracting two kinds of features of a local binary pattern (LBP) and a Gabor wavelet, combining dimensionality reduction frameworks in two steps, and finally, classifying and training by adopting a support vector machine (SVM). According to the method, the dimensions of the parallel fusion features can be effectively reduced; besides, the identification for six kinds of human facial expressions is realized and the identification rate is effectively improved; the defects existing in the identification method for serial feature fusion and single feature expression can be avoided; the method can be widely applied to the fields of mode identification such as safe video monitoring of public places, safe driving monitoring of vehicles, psychological study and medical monitoring.

The invention provides an ultrasonic guided wave detection device for quality evaluation of a composite laminated plate. In the device, ultrasonic guided wave is taken as a detection means for accurately and conveniently evaluating quality of the composite laminated plate. The detection means is achieved by the following steps: (a) performing time-frequency analysis on guided wave signals of the composite laminated plate, comparing a time-frequency distribution diagram of the signals with a theoretical time-frequency distribution curve of the guided wave, and providing a mode separation method; (b) obtaining accurate time delay information in a time-frequency domain based on good characteristics of a Gabor wavelet, combining optimal sensor placement and multi-path positioning, broadening the range of defect positioning, introducing cluster analysis and then improving accuracy and reliability of two-dimensional positioning; and (c) improving rationality and accuracy for extracting signal characteristics, and effectively separating various mode components from ultrasonic guided wave signals by adopting an improved HTT method, and taking instantaneous components as a characteristic parameter according to linear regression analysis; and (d) constructing a detection device of the ultrasonic guided wave signals, improving detection steps, designing software architecture and programming for processing the guided wave signals based on the time-frequency analysis.

The invention provides a facial expression recognizing method based on Gabor transform optimal channel blur fusion, comprising the following steps of: S1, dividing a Gabor filter into 11 channels, carrying out Gabor wavelet transform on a facial expression image subjected to normalization by the Gabor filter to extract texture characteristics of the facial expression image; S2, establishing classifiers corresponding to the channels one by one, respectively sending the texture characteristics extracted from the channels into the classifiers for classifying and recognizing, calculating the recognition rate and the definition of each classifier to various expressions, selecting 4 classifiers with top comprehensive ranking of the recognition rate and the definition to be used as optimal classifiers; and S3, carrying blur fusion on the four optimal classifiers to obtain a facial expression recognition result. The facial expression recognition method has the advantages of small calculated amount, high calculating speed and high recognition precision.

The invention discloses a method for evaluating medical image quality in combination with phase consistency, gradient magnitude and structural prominence. The method comprises the following steps: 1) calculating a phase consistency map of a reference image and a distorted image by using a log-Gabor wavelet; 2) calculating a gradient magnitude map of the reference image and the distorted image by using a Scharr operator; 3) obtaining a local image quality map in combination with the phase consistency map obtained in the step 1) and the gradient magnitude map obtained in the step 2); 4) calculating a structural prominence map, based on a human eye visual feature, of the reference image; 5) performing weighting summation on the image quality map in the step 3) by using the structural prominence map as an aggregation policy to obtain an image quality score; and 6) when the image quality score is higher than a preset score threshold, judging that the medical image quality is relatively high. The method is relatively high in reliability and high in practicality.

The invention discloses a crowd abnormity detection and positioning system and a crowd abnormity detection and positioning method based on a time recurrent neural network. The method comprises the steps of carrying out gridding division on a scene on the basis of preprocessing acquired sample data, and dividing a video fragment consisting of n frames into a plurality of time-space blocks; then constructing a multi-scale optical flow histogram and Gabor wavelet texture features in each time-space block; vectoring and combining the features in the multiple time-space blocks, finding out a relation between a space dimension and a time dimension by taking the video fragment as a time sequence through respectively using hidden nodes and feedback nodes of the time recurrent neural network, and training a time recurrent neural network model capable of detecting a long dependency relation; finally detecting and positioning a crowd abnormity by the model. The method is higher in instantaneity and accuracy, and can detect abnormities activated by a small quantity of individuals or a large quantity of individuals.

The invention discloses an identification method for video and living faces based on background comparison, comprising the following steps of: firstly, detecting the face position of each frame of an image of input video, and determining a background comparison district according to the detected face position; secondly, selecting an extreme point of the input video and the background comparison district on dimension space as a characteristic point of the background comparison district to obtain a background characteristic point set (Pt); thirdly, describing the characteristics of the image I on the background characteristic point set (Pt) by Gabor wavelet transformation; and fourthly, defining a living body value (L) by the result of the third step, wherein if the living body value (L) is larger than the threshold theta, the living body is determined, and otherwise, the living body is fake video. The invention mainly solves the problem of the computer automatic identification of video faces and living body faces only by a single camera.

The invention provides an Alzheimer's disease and mild cognitive impairment identification method based on two-dimension features and three-dimension features. The method particularly comprises a step of performing pretreatment of a medical image, wherein the pretreatment comprises pre-segmentation, registration and other processes; a step of performing two-dimension textural feature extraction of the medical image, wherein features comprise the quadratic statistic of a gray-level co-occurrence matrix and a multiscale and multidirectional feature value of Gabor wavelet transformation; a step of performing three-dimension morphological feature extraction of the medical image, i.e., extracting volume features of an area of interest; a step of performing feature fusion of three-dimension morphological features and two-dimension textural features; and a step of constructing a support vector machine to achieve identification of Alzheimer's disease and mild cognitive impairment. According to the method provided by the invention, the three-dimension morphological features and the two-dimension textural features are combined, so that the content of the medical image can be expressed in a comprehensive and accurate manner. The method can improve identification of Alzheimer's disease and mild cognitive impairment, thereby providing a more effective clinic assistant diagnosis.

A facial expression identification method based on a Gabor wavelet and a gray-level co-occurrence matrix. According to the method, firstly, a pure face region in an expression image is extracted by a manual partitioning method and color image gray processing, histogram equalization and scale normalization processing are carried out; then data redundancies of conventional Gabor features are greatly reduced by a method for carrying out block extraction on a Gabor feature statistic, and the gray-level co-occurrence matrix which reflects the image texture features is introduced into the field of expression identification for the first time so as to make up the defect of deficiency of pixel spatial relativity, which is caused by Gabor feature block processing; and finally, a set of low-dimension feature vectors for feature expression are generated and on the premise of ensuring a high expression identification rate, the expression identification rate is greatly improved.

The invention relates to palm print identifying technology, and provides a palm print identifying method and a palm print identifying device aiming at the defects that the preprocessing requirement for the acquired palm print images is high in the prior palm print identifying technology, and the like. The palm print identifying method comprises the step of extracting the palm print characteristics of an image to be extracted, and the extraction specifically comprises the following steps: rotating and correcting the image to be extracted to generate a corrected image; carrying out convolution operation for the corrected image according to the preset Gabor wavelet, carrying out the operation of taking an amplitude value or a phase from the corrected image after the convolution operation, and generating a response image; and carrying out local binary mode characteristic extraction for the response image to acquire a local binary mode code of the image to be extracted so as to generate the palm print characteristics. The invention also provides the palm print identifying device. The technical proposal of the invention can provide rotational invariance and has high processing speed, so the technical proposal can achieve real-time palm print identification.

The invention relates to a seismic wave decomposition method which comprises the following steps: selecting a Gabor small wave or a Morlet small wave as a wavelet form; carrying out multiwavelet decomposition on an obtained seismic signal; calculating three parameters of the small wave, namely time shift u, center frequency Omega, phase shift Phi through the complex attribute of the seismic signal in each decomposition; calculating a time scale parameter Sigma according to the three parameters; estimating the amplitude Alpha of the small wave; and determining a final parameter through iteration and decomposing wavelets with different dominant frequencies. The seismic wave decomposition method improves the traditional matched tracking method and improves the calculation efficiency; and meanwhile, Sigma is self-adaptive and adjustable.

The invention provides an intelligent wheelchair man-machine interaction system and method based on facial expression recognition and relates to the field of the biology, the psychology, the computer vision, mode recognition, artificial intelligence and the like. According to the method, a geometric model matching algorithm is used for automatically locating centralized effective eyebrow, eye and mouth areas of facial features, then ASM feature points of the eyebrow, eye and mouth areas are located, and convolution operation is carried out on located feature point pixels and Gabor wavelet kernel functions to extract the facial expression features; an Adaboost algorithm is used for iterating and training the facial features to obtain an expression classifying model; the expression classifying model is adopted for recognizing input expression sequences in a classifying mode, the input expression sequences are compared with a control instruction defined in advance, and therefore interaction control over an intelligent wheelchair from facial expression recognition is achieved. When the facial expression features are extracted, real-time recognition of facial expressions is greatly improved, and real-time reaction with the intelligent wheelchair is greatly improved.

The invention discloses an input method based on facial expression recognition, which comprises the following steps of: acquiring an image by using a camera device; recognizing a face and positioning an outline by utilizing a reference template method, a face rule method, a sample learning method, a skin color model method or a characteristic sub-face method; extracting a facial expression feature from the face by utilizing a principal component analysis method or Gabor wavelet method; acquiring the classification of the facial expression from the facial expression feature by utilizing a template-based matching method, a neural network based method or a support vector machine based method; matching the classification with a corresponding input result; and inputting the input result.

The invention relates to a road vanishing point detection method, in particular to a road vanishing point detection method based on video images, and belongs to the field of road detection. The problems that an existing detection method is high in error rate, a traditional voting algorithm is complex, most of time is consumed by the voting algorithm, and the calculation quantity is large when the multi-scale and multi-direction characteristics of Gabor wavelets are used for extracting textural features are solved. The road vanishing point detection method is characterized by including the steps that a frame of image data are input and converted into a gray level image, and fast Fourier transform is carried out; the textural response range is calculated based on a Gabor filter; the main direction of image textures is calculated; the number of votes of particles is calculated; the range of particle distribution is adjusted; a dynamic observation model for vanishing points is set up; particle filtering is performed, and the vanishing points are output. The road vanishing point detection method can be applied to computer vision systems including an autonomous navigation system of an intelligent walking robot or a pilotless automobile and the like.

The invention provides an En-ULLELDA-based (ensemble-unified locally linear embedding and linear discriminant analysis) method of multi-view model recognition. The method comprises the following steps: firstly, selecting a vehicle ROI (region of interest); secondly, extracting the high-level information that can reflect the model as features through Gabor wavelet transform; then, carrying out the dimension reduction of features on the basis of a novel manifold learning algorithm En-ULLELDA, so as to transform the features to a low-dimensional space; and obtaining the corresponding models from the features of a test sample by a classifier during the recognition. According to test results, the invention can effectively handle the problem of multi-view model recognition on urban roads; and the invention can eliminate the effects of parameter variation on the recognition results by employing the integration technology, so that the invention has higher robustness.

The invention discloses a traffic light identification method and device, and belongs to the field of traffic safety. The traffic light identification method comprises the steps that an original image is collected; threshold segmentation is conducted on three channel values of the original image in an RGB space to obtain a black region binary image; according to the characteristics of a light panel, the light panel area is positioned in the black region binary image; the portion, in the light panel region, of the original image is converted from the RGB space to a YCbCr space to obtain a red and yellow region binary image and a green region binary image through segmentation; according to the characteristics of traffic lights, traffic light candidate regions are determined on the red and yellow region binary image and the green region binary image; graying and normalization are conducted on the portion, in the traffic light candidate regions, of the original image, and Gabor wavelet transformation is conducted on the image obtained after graying and normalization to obtain a Gabor wavelet image; the amplitude of the Gabor wavelet image is sampled to obtain a feature vector; the similarity between the feature vector and a training sample is compared to determine the types of the traffic lights.

The invention relates to the technical field of voice recognition and provides a PCNN spectrogram feature integration based emotion voice recognition system. According to the invention, windowing andframing are performed on voice signals, discrete Fourier transform is performed and a spectrogram of the voice signals is drawn; a PCNN model is constructed and the spectrogram is processed through apulse coupling neural network; performing convolution on a PCNN graph and 5-scale 8-direction Gabor wavelets and extracting Gabor amplitude features so as to obtain 40 Gabor spectrograms; extracting uniform mode LBP features on each Gabor spectrogram and acquiring a feature vector as indicated in the description through cascading connection of histograms in the 40 Gabor spectrograms. According tothe invention, Fourier analysis is made on the voice signals; the voice signals are converted to the spectrogram; the spectrogram is processed through the pulse coupling neural network; 40 Gabor spectrograms are obtained through convolution on the spectrogram and the 5-scale 8-direction Gabor wavelets; a local two-value mode feature and a local Hu matrix feature are extracted; and a support vectormachine is adopted for classification and recognition after the two features are integrated.

The invention discloses a remote sensing image variation detecting method on the basis of weighted Gabor wavelet characteristics and two-stage clusters. The processed objects include optical remote sensing images and SAR (synthetic aperture radar) images and the remote sensing image variation detecting method includes (1) generating difference images according to remote sensing image types; (2) subjecting the difference images to Gabor wavelet transform; (3) extracting multiscale and multidirectional characteristics of the Gabor wavelet transform of the difference images; (4) designing the weighting coefficient and acquiring the weighted Gabor wavelet characteristics; (5) clustering the weight Gabor wavelet characteristics by means of the two-stage cluster strategy; (6) acquiring variation detecting results. By the remote sensing image variation detecting method, loss of marginal information is reduced, stronger, weak and slight varying areas can be detected at the same time, the total mistake pixel number is decreased, more detail information is reserved and variation results can be effectively extracted.

The invention discloses a color image quality comprehensive evaluation method. According to the method, first, a to-be-evaluated color image and a corresponding reference image are input; color spacetransformation is performed on the reference image and the to-be-evaluated image, and brightness channel graphs and chrominance channel graphs of the reference image and the to-be-evaluated image areobtained; the chrominance channel graphs of the reference image and the to-be-evaluated image are extracted, and chrominance similarity features are calculated; the brightness channel graphs of the reference image and the to-be-evaluated image are extracted, and log-Gabor wavelets are used to obtain phase-consistent similarity features; position saliency features of the reference image and the to-be-evaluated image are acquired; pooling is performed on the phase-consistent similarity features and the chrominance similarity features through a position saliency feature weighted standard deviation ; and the quality of the to-be-evaluated image is obtained. Through the method, different characteristics of human vision systems are fully considered, the three aspects are reasonably combined, andthe quality of the color image is well evaluated.

The invention discloses a face identification method based on Gabor wavelet and SB2DLPP. The face identification method based on the Gabor wavelet and SB2DLPP mainly includes pre-treatment, feature extraction, feature dimension reduction and classification identification, and to be specific, the face identification method includes that (1) pre-treating all the face images in a known face database, wherein the pre-treatment includes scale normalization and histogram equalization; (2) using the Gabor wavelet to extract features of the pre-treated face images; (3) leading in class information, and using a supervised bidirectional two-dimensional local preserving projection (SB2DLPP) algorithm to reduce the dimensions of the high-dimensional image features extracted through the step (2) to extract feature matrices mapped to a low-dimensional sub-space; (4) using a nearest neighbor classifier to perform classification identification. The face identification method based on the Gabor wavelet and SB2DLPP uses the Gabor wavelet and improved SB2DLPP algorithm to identify images, the problems that a traditional face identification method is easy to be influenced by light, expression and the like external factors are overcame, and the face identification rate is effectively improved.

The invention relates to an accurate positioning method for human eye on the basis of gray gradation information, belonging to the technical field of information. The method comprises: (1) human face detection: the area of a human face is positioned from an input image; (2) gray gradation column diagram analysis: gray gradation column diagram analysis is carried out on the human face image to determine the gray gradation range of the complexion area of the human face; (3) image strengthening: gray gradation adjustment is carried out on the image to enable eye characteristics to be more obvious; (4) Gabor wavelet filtration: Gabor wavelet filtration is carried out on the strengthened image, a real part image filtered by Gabor is synthesized to obtain a reference image; (5) cluster analysis: the synthesized reference image is analyzed by using the K-Means cluster method to obtain a binarization human eye candidate window, and the white area in each human eye candidate window after binarization is analyzed to determine the rough position of the human eye, so that the human eye window is obtained; (6) neighbourhood operation: the human eye window is scanned by two neighbourhood operators to determine the canter position of the pupilla. The invention has the advantages of simple detection method and accurate eye positioning.

The invention relates to an iris identification method based on multidirectional Gabor and Adaboost. The method comprises the following steps that: (1), block division is carried out on a normalized iris image and two-dimensional Gabor characteristics are extract to carry out coding; and a Hanmming distance between corresponding blocks is calculated; and (2), an Adaboost algorithm is used to carry out classification and identification on the block Hanmming distance obtained in the step (1). More particularly, in the characteristic extraction process, Gabor wavelets of eight directions under a same scale are employed; and block division is carried out on the expanded iris image; Gabor characteristics of the whole iris image and submodules of the iris image are simultaneously extracted by combining integral and local information of the iris and then coding is carried out; the whole and local combination is carried out to form a multi-dimensional characteristic vector; the Adaboost algorithm is introduced to carry out characteristic selection; and a classifier is constructed to carry out identification. According to the invention, beneficial effects of the method are as follows: a noise influence is reduced; an identification problem of a low quality iris image can be solved; and the identification performance is good.

The invention provides a method and a system for identifying human facial expression. The method for identifying the human facial expression comprises the steps of utilizing ASM (active shape model) algorithm to extract a shape model and feature points of a to-be-identified facial image, then, utilizing Gabor wavelet transform algorithm to extract amplitude texture characteristics of the feature points; obtaining a to-be-identified eigen face through projection for the amplitude texture characteristics; and identifying expression of a to-be-identified human face by comparing similarity between the to-be-identified eigen face and a sample eigen face. According to the method and the system for identifying human facial expression, the ASM algorithm is combined for extracting the feature points, so that identification through the Gabor wavelet transform algorithm can be performed only by comparing textural characteristics of the feature points. Compared with the existing facial identification technology only adopting the Gabor wavelet algorithm, the method and the system for identifying the human facial expression has the advantages of small data processing capacity, short identification time, low internal memory loss and the like.