486 results about "Neighbor algorithm" patented technology

A method of automatically labeling of unlabeled text data can be practiced independent of human intervention, but that does not preclude manual intervention. The method can be used to extract relevant features of unlabeled text data for a keyword search. The method of automated labeling of unlabeled text data uses a document collection as a reference answer set. Members of the answer set are converted to vectors representing centroids of unknown groups of unlabeled text data. Unlabeled text data are clustered relative to the centroids by a nearest neighbor algorithm and the ID of the relevant answer is assigned to all documents in the cluster. At this point in the process, a supervised machine learning algorithm is trained on labeled data, and a classifier for assigning labels to new text data is output. Alternatively, a feature extraction algorithm may be run on classes generated by the step of clustering, and search features output which index the unlabeled text data.

The invention discloses a voice synchronous-drive three-dimensional face mouth shape and face posture animation method. A user can input new voice information, and the new voice information can be preprocessed to combine mouth shape animations and face posture animations which are synchronous with voice on the face head of a virtual man. The method specifically comprises two stages. In a training stage, voice visualization modeling can be achieved through a k-nearest neighbor algorithm (KNN) and hidden Markov model (HMM) mixed model. In a combining stage, the user can input new voice information, characteristics of voice signals are extracted, face posture and mouth shape sequence parameters corresponding to the voice signals can be generated through the KNN and HMM mixed model and are processed in a transition mode, and X face open source software is used to combine delicate and abundant three-dimensional face animations. The method has significant theoretical study value and has wide application prospect in the fields of visual communication, virtual meetings, games, entertainments, teaching assistance and the like.

The invention belongs to the technical field of privacy protection in cloud computing and discloses a privacy protection method in face authentication system based on edge calculation. The method comprises the steps that: a camera collects a face image and uploads the face image to a connected edge computing node, an authority assigning mechanism sends a permission vector Li of a user to the edgecomputing node after collecting user face information, an edge computing device carries out feature extraction on a face image of a user requesting registration to obtain a feature vector by using a method based on a deep convolutional neural network, a safe nearest neighbor algorithm is performed to encrypt the feature vector of a registered user, and a secret sharing homomorphic algorithm is performed and n sub-secrets are generated according to the feature vector and are distributively stored in n edge computing devices. The direct upload of sensitive data to a cloud server is avoided, theprivacy of the face data is protected, and the fault tolerance of a system is improved. Results prove that the accuracy of face recognition under a ciphertext of the present invention is completely equal to the accuracy of face recognition under a plaintext.

The invention discloses a method for quickly identifying two-dimension code system type in images, comprising a learning training process and a classification identifying process. The learning training process is as follows: collecting and building a sample image set of various two-dimension code images; converting each sample image into a grey image, performing Gaussian smoothing filtering and binaryzation to obtain binaryzation images; scanning prospect boundaries of the binaryzation images in the horizontal and vertical directions, obtaining an outer boundary point set of the two-dimension code; enabling the two-dimension code to be horizontal by rotating images, achieving horizontal correction of the two-dimension code; performing partitioning, combining and normalizing for the two-dimension code; performing fast wavelet transform for the normalized sample image to obtain a wavelet characteristic sample set. The classification identifying process is as follows: extracting wavelet characteristic of the to-be-identified image to build a distance measurement model; using the K nearest neighbor algorithm to identify code system type. The method is convenient and quick, has real-time performance, accuracy and high identification rate.

The invention discloses a method and system for locating a mobile terminal, and belongs to the field of wireless service supporting in mobile communication. The method integrates the advantages of a support vector machine and a K-nearest neighbor algorithm, and through the stages of off-line drive test sampling and model training, two layers of SVM locating models and KNN locating models are constructed. In an on-line locating stage, the two layer of the SVM locating models and the KNN locating models are combined, so that the locating precision of the mobile terminal is greatly improved. The method achieves compromise and balance between the algorithm complexity and the locating precision, and compared with a traditional locating method, the method effectively improves the locating precision, greatly reduces calculated amount and improves the locating speed.

The invention discloses an intelligent urban construction examining and approving method based on a case-based reasoning technology. The intelligent urban construction examining and approving method based on the case-based reasoning technology comprises the following steps of constructing an examining and approving case library; inputting new examining and approving case and model parameter information; submitting jogs to a Hadoop cluster to search KNN (k-nearest neighbor algorithm) Map Reduce cases; statistically analyzing a searching result on the basis of a 'weighted integral model'; evaluating and correcting the cases; and performing distributed full-text searching on examining and approving data. The intelligent urban construction examining and approving method has the advantages that by the method, the circumstance of manual examination and approval application at present can be changed, the work efficiency is improved, the basis on examining and approving is increased, and an examining and approving process is intelligent. Distributed searching can be carried out by using a Hadoop frame and a MapReduce frame through a cloud computing center, and a distributed case searching model based on the case-based reasoning technology is established. The 'weighted integral model' is creatively raised to statistically analyzing searched similar cases, and a guidance which is beneficial to new examining and approving cases is obtained.

The present invention facilitates identifying applications based on communicated packets between applications. Characteristics of communicated packets are used to identify the packet as being part of a communication between applications. Identification can be accomplished through the use of packet fingerprints or through a K nearest neighbor algorithm.

The invention provides a digit recognition method for identification cards. The digit recognition method comprises the following steps: acquiring a classifier training file; transforming a color image to be identified into a gray level image; accurately positioning digit element positions of each identification card; accurately segmenting digit characters of each identification card; verifying whether a candidate region is a real digit region of each identification card; carrying out digit recognition on the basis of a nearest neighbor algorithm; judging whether the current digit element is the last group of digit element. According to the digit recognition method provided by the invention, positioning, segmentation and identification of digits on the identification cards are carried out by adopting an image processing technology; the recognition precision is relatively high, no special equipment is needed and the cost is low; the digit recognition method has strong applicability to various environments and can be used for reading information of multiple identification cards simultaneously.

The invention discloses a locating method in a wireless LAN and a device thereof. The locating method comprises the following steps: acquiring a sample set composed of a plurality of sample points closest to the location to be determined according to a nearest neighbor algorithm; acquiring central positions of the sample points in the sample set, and the distance between the sample points and the central positions; judging whether the sample points with the distance from the central positions which is more than a preset standard exist, if yes, deleting the sample points with the distance from the central positions which is more than the preset standard from the sample set, and returning to the previous step; otherwise continuing to the next step; and determining the location to be determined according to the various sample points included in the sample set. The locating method in the wireless LAN and the device thereof help improve precision and credibility of locating results in the wireless LAN.

The present invention discloses a polarized SAR image classification method based on the semi-supervised depth distance metric network, and the technical problems that the traditional depth learning only considers the non-linear relationship between the sample characteristics and the classification accuracy is not high when the number of marked samples is relatively small are solved. The method comprises the following steps: inputting to-be-classified polarized SAR image data; solving a neighboring sample of the marked sample; constructing the loss function of the semi-supervised large boundary neighbor algorithm; initializing parameters of the network; pre-training the network; carrying out fine tuning on the network; carrying out classification prediction on the unmarked samples; and outputting a classification result image and classification accuracy of the to-be-classified polarized SAR image. According to the method disclosed by the present invention, by constructing a depth distance metric network, a popular learning regular term is added to the large boundary neighbor algorithm, so that problems of the influence of insufficient marked samples on the classification accuracy and the waste of information of a large number of unmarked samples are overcome; and the characteristics learned in the method of the present invention fully depicts intrinsic attributes of the samples, and the method can be applied to the earth resources survey, military systems and other technical fields.

The invention discloses an indoor passive positioning method based on geographic fingerprints. The method is applied to the technical field of indoor positioning and comprises: in a situation that a mobile terminal is in an off-line state, collecting a received signal strength (RSS) value of each reference point where the mobile terminal is by utilizing an access point device; calculating a prior probability that the access point device collects the RSS values in a period; establishing a geographic fingerprint database according to a coordinate position of each reference point, the RSS values and the prior probability; when the mobile terminal is in an on-line state, testing an RSS value of each test point where the mobile terminal is; matching the test points with reference points in the geographic fingerprint database based on a prior probability nearest neighbor algorithm according to the RSS value at each test point where the mobile terminal is; and determining the positioning position of the mobile terminal. According to the invention, the prior probability is applied to the K-nearest neighbor algorithm for positioning, and the positioning accuracy is improved at the same time and space complexity.

The invention provides a point cloud feature point detection method and a cloud point feature extraction method. The checking method comprises the following steps that: taking one data point in pointcloud data as a sphere center to establish a local spherical coordinate frame, and finding k pieces of neighbourhoods through a k nearest neighbor algorithm; independently connecting the sphere centerwith each nearest neighbor point to obtain k pieces of line segment groups; according to the horizontal projection included angles of the line segment groups, sorting k pieces of line segment groups,checking each line segment group through a Laplace operator, and determining whether the data point is a feature point or not; and according to the above steps, processing each data point in the cloud point data, and obtaining all feature points of the cloud point data. The point cloud feature extraction method comprises the following steps that: according to the above detection method, determining feature points, and recording the regional connection information of each feature point; and determining a sequential connection relationship between the feature points, connecting the feature points, and forming a segmented feature polygon to realize region segmentation. The amount of labeled feature points is small, feature points are extracted orderly, and feature lines are conveniently reconstructed.

The invention provides a method for detecting an abnormal time sequence without a class label, and aims at solving the problems that ideal effect of segmenting fixed points based on satellite remote detecting data cannot be achieved, the clustering number is manually set during layer-based clustering, and offline and online abnormality detection methods for the label time sequence without the class label are currently not developed. According to the technical scheme, the method comprises the steps of 1, segmenting the satellite remote detecting historical data according to the cycle property of the satellite remote detecting data to obtain the time sequence without class label, namely, X={x1, x2..., xn}; 2, performing adaptive layer-based clustering for the X={x1, x2..., xn} obtained in step 1, and determining and deleting the abnormal sequence in the time sequence without the class label to obtain the formulas as shown in the specification; 3, adopting the formulas as shown in the specification as samples, performing mode matching for the formula shown in the specification by the nearest neighbor algorithm according to the matching threshold, so as to finish the abnormal satellite remote detecting data detection. The method is applied to the field of satellite data detection.

The invention discloses a multilayer bitmap color feature-based image retrieval method. In the method, fast clustering is performed on an image with rich color information to obtain rational statistical distribution centers of each color cluster, and based on the rational statistical distribution centers, features capable of reflecting color differences among different distribution layers of the image are extracted to perform image retrieval. The method comprises the following steps of: first performing meshing on a color space of the queried image, counting the numbers of pixel points in each mesh and selecting the mesh with a number local maximum; then quickly generating each color cluster and the rational statistical distribution centers thereof by adopting a novel distance optimization algorithm and an equal-average nearest neighbor algorithm search (ENNS) algorithm in a K-average clustering algorithm, and on the other hand, performing space sub-block division on the queried image and calculating a Gaussian-weighted color average of sub-blocks; next comparing the color average of the image sub-blocks with the rational statistical distribution centers of the color clusters to extract the features of a K-layer bitmap; and finally performing the matched searching of the image features by combining the similarity measurements of the rational statistical distribution centers of the color clusters and the bitmap.

The invention discloses a multi-target identification tracking method for a road traffic scene. According to the method, the road traffic field environment and obstacle characteristics are combined. Dynamic obstacles around a road site are detected through a laser radar. The method comprises the following steps of: firstly, preprocessing laser radar data and converting the data into a binary image, secondly, extracting a dynamic obstacle by using a background difference method, carrying out clustering analysis, further carrying out classification recognition by using a Point CNN algorithm, obtaining pose transformation of the obstacle by using an iterative nearest neighbor algorithm, and finally, carrying out tracking by using a UKF for obstacle classification. The multi-target identification tracking method provided by the invention has good real-time performance, identification precision and tracking precision, and can effectively realize safety monitoring of the road traffic scene.

The invention relates to a SIFT and Otsu matching based colored eyeground image splicing method. The method is realized by that (1) a green channel of a reference eyeground image and a target eyeground image is extracted; (2) the green channel is preprocessed via contract limited adaptive histogram equalization and two-dimensional Gaussian filtering to enhance the contract between vessels and tissue background; (3) SIFT characteristics of the image are extracted; (4) characteristic points of the two images are matched initially via a nearest neighbor and a next nearest neighbor algorithm; (5)an Otsu algorithm is used to screen the initial matching points to obtain correct matching characteristic point pairs; (6) a random sampling consistency algorithm is used to calculate an affine transformation matrix to carry out space transformation o the images and complete registration; and (7) the registering images are fused in a maximum value algorithm, and splicing is completed. According tothe invention, SIFT is combined with the Otsu method, the disadvantage of non-ideal registering effect caused by mismatching points is overcome, and the method has significance in the field of diagnosing diabetes and pathology degree thereof.

The invention provides and establishes a multi-agent frame structure based tour multimedia information personalized service system. The system adopts the Agent technology, finds and mines interests of users through constant independent study, and realizes automatic user interest modeling by adopting a vector included angle based K-nearest neighbor algorithm. As for each phase of a system working process, modules with relatively independent functions are divided, and each module is packaged into an Agent agency to form a multi-agent framework; and under the framework, the agencies are separated and cooperated so as to realize the characteristics of intelligence, personalization and the like of the multimedia information service. The system mainly comprises five functional modules, namely a user interface Agent, a filter sequencing Agent, an interest study Agent, a monitoring Agent and an information searching Agent. The method proposed by the invention has higher theoretical significance and application value for establishing high-quality intelligent tour information service.

The invention relates to a potato image acquisition device based on RGB-D cameras and a method for identifying and locating a bud eye. The image acquisition device consists of three RGB-D cameras thatacquire the color maps and the depth maps of a potato sample from three different angles. The image processing part comprises obtaining a potato target image by preprocessing the color maps and performing a mask-based target extraction method; then training a classifier by using an Adaboost algorithm and a Haar-like feature, identifying a bud eye area, and obtaining the two-dimensional coordinates of the bud eye area; calibrating the RGB-D cameras to obtain the internal parameters and the external parameters of respective RGB-D cameras, and generating point cloud in combination with the depthmaps and the color maps acquired by the corresponding cameras; successively registering the three groups of point cloud by using an iterative nearest neighbor algorithm to obtain the three-dimensional model of the potato sample; then converting the obtained two-dimensional coordinates of the bud eye into coordinates in a three-dimensional space where the three-dimensional model of the potato sample is located, so as to achieve the three-dimensional positioning of the potato bud eye and lay a foundation for realizing the automation of the potato seed dicing.

The invention provides a disease detection method and device based on a wearable device and the wearable device. The method comprises the steps that original electrocardio signals of a user are collected through an electrocardio sensor; feature vectors corresponding to the original electrocardio signals are determined, wherein the feature vectors comprise time-domain feature data of the original electrocardio signals and frequency-domain feature data of the original electrocardio signals; a disease type corresponding to the original electrocardio signals is determined according to the feature vectors and a trained large-distance nearest neighbor algorithm corresponding to the original electrocardio signals. According to the technical scheme, the accuracy on disease detection of the user can be greatly improved, and the user can recognize and predict health risk as early as possible through the wearable device.

The invention relates to a robot SLAM object state detection method in a dynamic sparse environment. Firstly, image collecting of the environment is carried out through a vision sensor, and a feature vector set of images is obtained through a SURF describor; then matching of an image of the current moment and an image of a historical moment is carried out according to a nearest neighbor algorithm, whether matching is successfully carried out is detected by means of an RANSAC algorithm and whether an object of the current moment is consistent with the object of the historical moment is judged according to the detection result; depth information of the object is obtained in a parallax error method, and a world coordinate and a relative position difference of the object at the two moments are obtained according to a plane geometrical relationship; ultimately, an acceptance region is obtained by combination with hypothesis testing, and the state of the object is judged by detecting whether the relative position difference of the object is within the acceptance area. When the state of the object in the environment is detected, influences of positioning and measurement errors of a moving robot on an object position observation value are taken into consideration, and object state judgment accuracy is improved.

The invention discloses an odometer method and device for sports equipment; the odometer method comprises: acquiring current pose of sports equipment, current second laser frame and a local point cloud map; calculating according to the current pose, the second laser frame and the local point cloud map to obtain a current optimal pose; and other steps. In the odometer method, initial relative exercise amount and pose are acquired through the number of turns of a coded disc, laser inter-frame matching is performed by means of iterative nearest neighbor algorithm and the like, and accurate relative exercise amount and pose are acquired accordingly; the pose acquired in the above step is used as an initial value to perform matching on the laser frame and the local point cloud map so as to obtain more accurate pose and exercise amount that are then output. The odometer device provided herein enables the poor precision and accumulative error defects of an existing odometer to be overcome, and is applicable to general or special computing system environments or configurations.