31results about How to "Fast Feature Extraction" patented technology

The invention relates to an intelligent terminal network teaching method. The intelligent terminal network teaching method comprises the steps that communication connection among the interior of a teaching terminal, the interior of a learning terminal, the interior of a parent management terminal, the interior of a server and the interior of a service platform is set up; the teaching terminal is used for recording the course of a teacher and uploading and storing the course of the teacher in the server; the learning terminal feeds information back to the teaching terminal through the server, and the teaching terminal answers the fed back information correspondingly; the parent management terminal is used for maintaining parent information; data are stored in the server and used for being called by the teaching terminal, the learning terminal and the parent management terminal, and the server is used for carrying out data interaction with other external devices; the teaching terminal and the learning terminal both are in information interaction with the service platform, a teacher management module, a student management module and an intelligent search module are arranged in the service platform, and the intelligent search module comprises an image retrieval module and an image recognition module; the learning module and a sign-in management module carry out information transmission. The intelligent terminal network teaching method can be used for developing a pre-class lesson preparing application, a classroom teaching application, an after school care application, a remote education application and the like based on the Internet.

Embodiments of the invention provide a big data feature extraction method and device. The device comprises a data structured module and a representative learning module, wherein the data structured module is used for pre-processing original big data and networking the pre-processed original big data to obtain a relationship network with nodes and an edges; and the representative learning module is used for obtaining high-dimensional vectors of the relationship network by adoption of an embedded mapping-based representative learning algorithm so as to obtain features of the original big data. The device provided by the embodiments of the invention can effectively extract the feature information in the big data without artificial participation; the feature information is uniformly expressed in a form of high-dimensional vectors, so that the features can provide a uniform effective processing method for a plurality of application services; and by adoption of the embedded mapping-based representative learning algorithm, structure information is retained in the high-dimensional vectors, so that more correct application services can be subsequently provided for the users.

The invention provides a vehicle license character feature extracting and classifying method using projection symmetry as the precondition for judgment. The method comprises the following steps: firstly extracting character feature to 26 letters and 10 numbers which may appear in vehicle license characters by using projection symmetry as precondition to divide into four classes, namely vertical projection class, horizontal projection class, central point projection class and dissymmetric property class and realize the coarse classifications of vehicle license characters; and fining, performing normalization transformation, then extracting features of points and rings and completing the fine classification of vehicle license characters. The invention combines the projection symmetry with the feature extraction method of points and rings to set a vehicle license character classifier, thus laying the foundation of realizing the identification of vehicle license characters finally. The method has better identification effect on confusable characters, such as '0' and 'D', '8' and 'B', '7' and 'T' and the like, thus increasing the identification speed and accuracy of vehicle license characters.

The invention provides a vibration type flow meter characteristic signal extraction method based on improved assemble average empirical mode decomposition. The method includes the steps of conducting end continuation processing on a collected vibration type flow meter flow vibration signal through a waveform-matched self-adaption end continuation method, conducting envelope line fitting on the collected vibration signal through a cubic B-spline method, conducting MEEMD decomposition to obtain a plurality of IMF components, conducting relevance analysis on the IMF components and the original signal, selecting the useful IMF components, conducting HHT conversion on the IMF components, and obtaining the Hilbert time-frequency spectrum and the marginal spectrum of the flow signal, wherein the Hilbert time-frequency spectrum and the marginal spectrum are the signal characteristics of the vibration type flow meter flow vibration signal. The method is suitable for accurately and rapidly metering the pipe network fluid flow in the industrial field.

The invention provides an SURF-based compression tracing method and an SURF-based compression tracing system. The method comprises the following steps of respectively collecting a positive sample and a negative sample of a t-th frame image, and positive samples and negative samples of the t-N-th to t-th frame images of a video file, using SURF to extract image features and a compression perception technology to obtain a first classifier and a second classifier, and weighting the first classifier and the second classifier to obtain a third classifier; using the third classifier to trace target images in the t+1 frame images of the video file. According to the target tracing method and system provided by the invention, due to the fact that the SURF extracted image features are adopted, the feature extracting speed is high, the image features can be still correctly extracted when a target environment is subjected to size change, rotation change and the like; original image features are compressed through a sparse random measurement matrix, so that the number of processed data is small, the updating speed of the classifiers is increased, and the target tracing method can be used for accurately tracing a target in a complex environment, and has real-time property and robustness.

The invention discloses a real-time gesture tracking method based on a cascade deep neural network. The method comprises: image data are obtained by a TOF camera and a color camera; an image preprocessor carries out preprocessing on the image data; essential characteristic extraction is carried out on the data after preprocessing by using a primary characteristic extractor; a cascade artificial neural network system carries out advanced characteristic abstracting processing; a mode matching device carries out mode matching according to the advanced abstract characteristics after characteristic abstract processing; and then an attitude processing center calculates all positions of twenty six nodes of a hand to obtain a hand attitude and spatial position data, and the data are transmitted to a computer application by a gesture attitude data flow. Rapid characteristic extraction, matching, and attitude calculation are carried out on image information of the human hand to guarantee the high stability, precision, and real-time performance of the calculated gesture attitude.

The invention discloses a method and a system for effectively sharing and collecting. A method for storing websites comprises the following steps: classifying the stored websites when the websites are stored by a user; adding identifiers to the websites. The invention further discloses a method for recommending the websites. The method for recommending the websites comprises the following steps: analyzing using habits of the user; recommending the websites which are matched with the using habits of the user and contain specific classification and identifier characteristics to the user, or regulating the display priority of the websites of different classifications and identifiers according to the using habits of the user. By adopting the scheme of the invention, the characteristics of the websites can be extracted quickly, and the websites are classified according to the characteristics; the websites with certain characteristics can be recommended to the user according to the hobbies and interests of the user; the accuracy of websites recommending is improved; the interesting websites of the user are found by the user conveniently and more quickly.

The invention provides a visual word bag model constructing model based on an improved SURF characteristic. A box filtering template added with gradient information is used to replace a Gaussian filter, and the template is more close to a Gaussian second-order differential template. In SURF characteristic expression, the overhead in time is reduced, and a SURF descriptor reduced to 32 dimensions while rotation invariance is ensured. When a word bag is constructed, an improved SURF algorithm is used to extract all improved SURF characteristic in an image library, a k-means clustering method is used to cluster all SURF characteristics as a visual word, and each image is expressed as the high-dimensional vector of the appearance frequency of each visual word. The comprises gradient information with rich images, one time of calculating a Haar wavelet is omitted, compared with the direct use of the SURF characteristic, the problem of non-uniform characteristic amounts extracted from different images can be solved well, the a word bag function allows multiple images to be expressed by a certain number of visual words, the space is saved, the processing is convenient, and the scalability is high.

The invention discloses an image comprehensive similarity analysis method based on description content and image content features. The method comprises the steps of extracting the image color information, namely the RGB model and image texture information, in image content feature information; extracting image title content and description content in the image description content feature information, and performing word segmentation processing; converting the RGB model into an HSV model, obtaining an HSV value of the image, and obtaining 24-dimensional feature information of the image color; calculating a gray value of the image in the image texture information; calculating cosine similarity to obtain image content similarity; obtaining features of the image title content and the image description content; calculating cosine similarity of the title contents and the description contents of the two images to obtain similarity of the description contents of the images; calculating image feature similarity; combining the image content features with the image description content features to generate composite features of the image; calculating cosine similarity of the image composite features to obtain similarity of the image composite features; judging whether images are similar or not only when the image feature similarity and the image composite feature similarity are both greater than or equal to the threshold.

The invention belongs to the technical field of information security, and discloses a video tampering detection method and system, a storage medium, a computer program and a terminal. The method comprises using a three-frame difference method to extract a video key frame; carrying out DCT on the obtained key frame to reduce high-dimensional video data and carry out de-noising processing to obtaina sub-graph containing most signal energy; performing feature extraction on the sub-graph through an ORB algorithm; and comparing the similarity between a to-be-detected video and an original video through the Hamming distance. The detection performance of the proposed method is evaluated by using a statistical method, feature extraction is performed on the key frame of a test video by using an SIFT algorithm, an ORB algorithm and the algorithm of the invention, and the average feature point number, the average time consumption and the average storage space of the key frame are counted. According to the embodiment of the invention, the number of feature points extracted from the key frame is far less than the number of feature points extracted by an SIFT algorithm and is 40% less than thenumber of feature points extracted by an ORB algorithm.

The invention discloses a bearing fault detection method based on a variable learning rate multi-layer perceptron. The method comprises the following steps: Step 1, collecting and extracting a vibration signal of a bearing by using a sensor; step 2, carrying out feature iteration on the vibration signal by using a multilayer perceptron; 3, a back propagation process is used for automatically optimizing the weight coefficient of the multi-layer perceptron, the learning rate of automatic optimization is changed according to the number of iterations, time domain signals obtained after iteration enter a support vector machine classifier, and a diagnosis model is obtained; step 4, inputting test data into the diagnosis model for diagnosis, and completing the detection of the bearing fault; according to the method, the bearing signal faults are extracted in a crossed mode through the multi-layer perceptron based on the variable learning rate, the loss function value can be rapidly reduced to complete fault diagnosis, the feature extraction speed is high, data do not need to be preprocessed, diagnosis precision is high, and the method has the advantages of being rapid in feature extraction, high in precision and good in robustness.

The invention provides an internet big data analysis and extraction method, which comprises the following steps of: 1, dividing a data object into different parts and types according to the characteristics of data to obtain a data range to be extracted; step 2, establishing a regression model, solving each parameter of the model according to the measured data, then evaluating whether the regression model can fit the measured data, and if the regression model can fit the measured data, further narrowing the range of the data to be extracted according to the independent variable; step 3, dividing the data into more than two aggregation classes according to the characteristic attributes of the data, and grouping the data to be grabbed, the elements in each aggregation class having the same characteristics; 4, calculating the similarity degree of the two pieces of data by adopting a similarity matching method; 5, the word frequency is used as a statistical index to indicate data segment information fed back by the data; and step 6, obtaining a data analysis result. The method is automatically completed by using an embedded mapping-based representation learning algorithm, and the calculation efficiency is high.

The invention provides a method for reading a two-dimensional code under a screen based on a photoelectric sensor, and the method comprises the steps: carrying out the networking of a mobile payment device which is arranged in a plurality of scenes and is provided with the photoelectric sensor, a camera and a control system based on a central processing unit and supports the cooperative application of an input device and a printing device, and enabling the mobile payment device to be connected with an electronic terminal and a server cluster; obtaining parameter data to generate a two-dimensional code image; when it is monitored that the payment event is triggered, code scanning and non-contact card swiping payment are supported, and the first distance measurement sampling point is monitored, reading is completed through deduction scanning. According to the method, for a two-dimensional code image, a preset algorithm is adopted for decoding, when various devices sense that a bar code exists in front, display content is removed, and a display screen is changed into transparent glass to facilitate reading; through algorithm processing, the bar code content is read while the content is displayed on the screen. And the flexibility and usability of code scanning are improved. The invention further provides a device for reading the two-dimensional code under the screen based on the photoelectric sensor.

The invention discloses a rolling bearing fault detection method based on a multilayer residual network model. The rolling bearing fault detection method comprises the following steps: Step 1, collecting and extracting a vibration signal of a bearing by using a sensor; step 2, carrying out feature iteration on the vibration signal by utilizing a residual network; 3, the time domain signals subjected to iteration enter a neural network full-connection layer, weighted summation is conducted on the time domain signals through the network full-connection layer, the weight coefficient of a network structure is automatically optimized through the back propagation process, and a diagnosis model is obtained; step 4, inputting test data into the diagnosis model for diagnosis, and completing the detection of the bearing fault; according to the method, bearing signal faults are extracted in a cross mode based on the multi-layer residual error network model, so that the loss function value can be rapidly reduced to complete fault diagnosis, the feature extraction speed is high, data does not need to be preprocessed, the diagnosis precision is high, and the method has the advantages of being rapid in feature extraction, high in precision and good in robustness.