677results about How to "Quick classification" patented technology

A novel solution for fast packet classification includes a novel data structure to store classifier rules which enables fast packet classification, which structure employs bitmaps for each field of the incoming packet for which classification is desired. A fast packet classification algorithm using the novel data structure allows the matching rule with the highest priority to be quickly obtained. A novel rule update algorithm allows new classifier rules to be added into the data structure incrementally. In one practical implementation of a classification engine employing the structures and algorithms of the present invention, a configurable processor with customized instructions is used to accelerate packet classification.

Embodiments of methods and devices for classifying background noise contained in an audio signal are disclosed. In one embodiment, the device includes a module for extracting from the audio signal a background noise signal, termed the noise signal. Also included is a second that calculates a first parameter, termed the temporal indicator. The temporal indicator relates to the temporal evolution of the noise signal. The second module also calculates a second parameter, termed the frequency indicator. The frequency indicator relates to the frequency spectrum of the noise signal. Finally, the device includes a third module that classifies the background noise by selecting, as a function of the calculated values of the temporal indicator and of the frequency indicator, a class of background noise from among a predefined set of classes of background noise.

A device, method, and system for detecting emboli in the brain is disclosed. A transcranial Doppler photoacoustic device transmits a first energy to a region of interest at an internal site of a subject to produce an image and blood flow velocities of a region of interest by outputting an optical excitation energy to said region of interest and heating said region, causing a transient thermoelastic expansion and produce a wideband ultrasonic emission. Detectors receive the wideband ultrasonic emission and then generate an image of said region of interest from said wideband ultrasonic emission. A Doppler ultrasound signal will also be deployed to image the region of interest. Doppler presents changes in velocity to map blood flow. Additionally, a dye can be given to visualize the brain vasculature and a perfusion measurement can be made in various regions of the brain along with the transcranial Doppler and the photoacoustic screening. Systems are taught using resultory medical data for better triage within an enhanced stroke ecosystem.

It is intended to provide a method whereby a plural number of antibodies against cell surface antigens are quickly classified and to provide a method whereby antigens of the thus classified antibodies are quickly identified. Further, it is intended to provide a method of promoting the utilization of the useful data obtained by the above methods. Furthermore, it is intended to provide an antibody which is effective in treating or diagnosing cancer. Namely, a method of classifying antibodies which comprises: (1) the step of preparing a plural number of antibodies respectively recognizing cell surface antigens; (2) the step of bringing each of these antibodies into contact with a cell of the same species; (3) the step of analyzing each of the cells having been treated in the step (2) by flow cytometry and thus obtaining data indicating the reactivity of each antibody with its cell surface antigen; and (4) the step of comparing the thus obtained data and classifying the individual antibodies depending on the similarity. A method of identifying antigens which further comprises: (5) the step of selecting one to several antibodies from each antibody group formed in the step (4) and identifying antigens thereof; and (6) on the assumption that antigens of the antibodies belonging to a single antibody group are the same or highly related to one another, making relations between the antigens having been identified in the step (5) and the antibody groups to thereby identify the antigens. An antibody against HER1, an antibody against HER2, an antibody against CD46, an antibody against ITGA3, an antibody against ICAM1, an antibody against ALCAM, an antibody against CD147, an antibody against C1qR, an antibody against CD44, an antibody against CD73, an antibody against EpCAM and an antibody against HGFR, each obtained by using the above methods.

The invention is applicable for the technical field of classification processing, and provides an application program display and classification method, an application program display and classification and a mobile terminal. The display method comprises the following steps of: presetting classifications to which application programs belong in an application program management setting interface; and displaying the at least one preset classification and application program icons in the classifications on the display interface. The classification method comprises the following steps of: when a user touches an application program iron, displaying a classification file folder; acquiring a command of dragging the application program icon to a corresponding file folder by the user; and classifying a dragged application program to the classification corresponding to the corresponding file folder. By the method and the system, the user can conveniently classify the application programs rapidly and can rapidly search desired application programs and enter an operation interface of an application program.

An image focus assessment method is provided that works reliably for images of a variety of relatively dissimilar workpieces or workpiece features. The focus assessment method is based on analysis of a single image (without the benefit of comparison to other images). The robustness of the focus assessment method is enhanced by the use of at least one classifier based on a plurality of focus classification features. In one application, a primary advantage of assessing focus from a single image is that an overall workpiece inspection time may be reduced by avoiding running an autofocus routine if an image is already in focus. In various embodiments, the focus assessment method may include an ensemble of classifiers. The ensemble of classifiers can be trained on different training data (sub)sets or different parameter (sub)sets, and their classification outcomes combined by a voting operation or the like, in order to enhance the overall accuracy and robustness of the focus assessment method.

Text information analysis apparatus arranges a plurality of texts according to the content of each text. In the text information analysis apparatus, a category decision unit classifies text to one of a plurality of predetermined categories. A cluster generation unit clusters texts having similar contents from the plurality of texts. A control unit controls the category decision unit and the cluster generation unit to simultaneously execute a category decision and clustering for the plurality of texts.

The invention discloses an urban road marker automatic sorting method based on vehicle-mounted laser scanning point cloud. The method includes: S1) a step of subjecting original point cloud data to road surface segmentation based on wheel path data to obtain road surface point cloud data; S2) a step of subjecting the obtained road surface point cloud data to binarization processing and extracting road marker points; S3) a step of clustering the road marker points and separating independent road marker targets; S4) a step of sorting large road markers and small road markers according to the dimensions of the obtained road marker targets; S5) a step of subjecting the large road markers to sorting processing based on wheel paths and road edge lines; and S6) a step of subjecting the small road markers to sorting processing based on deep learning and principal component analysis. The method rapidly and accurately extracts and sorts urban road markers, largely reduces the time and labor cost for data processing, and effectively guarantees traffic safety and intelligent drive reliability.

The present invention relates to a system, method, and computer program product for recognition objects in a domain which combines feature-based object classification with efficient search mechanisms based on swarm intelligence. The present invention utilizes a particle swarm optimization (PSO) algorithm and a possibilistic particle swarm optimization algorithm (PPSO), which are effective for optimization of a wide range of functions. PSO searches a multi-dimensional solution space using a population of “software agents” in which each software agent has its own velocity vector. PPSO allows different groups of software agents (i.e., particles) to work together with different temporary search goals that change in different phases of the algorithm. Each agent is a self-contained classifier that interacts and cooperates with other classifier agents to optimize the classifier confidence level. By performing this optimization, the swarm simultaneously finds objects in the scene, determines their size, and optimizes the classifier parameters.

The distribution states of defects are analyzed on the basis of the coordinates of defects detected by an inspection apparatus to classify them into a distribution feature category, or any one of repetitive defect, congestion defect, linear distribution defect, ring / lump distribution defect and random defect. In the manufacturing process for semiconductor substrates, defect distribution states are analyzed on the basis of defect data detected by an inspection apparatus, thereby specifying the cause of defect in apparatus or process.

The invention provides a clustering based point cloud segmentation method and system. The method comprises the following steps: calculating the normal vector, plane curvature and compatible set of each point, realized as follows, firstly constructing a k-d tree for the inputted point clouds, and then using the neighbor K points nearest to one point to get the normal vector and plane curvature of the point; clustering the point clouds, constructing a link table and a clustering center table to obtain a set of all clusters; conducting patch processing which includes constructing initial patches, including for each cluster in the set of clusters, and using a plane for approximate fitting to the corresponding point clouds for an MCS fitting plane, the normal vector, plane curvature and compatible set; and conducting patch combination for the final cloud point segmentation result. On the basis of the traditional region growing algorithm, the method and system provided by the invention directly use the normal vector and the plane curvature of the point clouds to carry out rapid classification, and does not need extra calculations to achieve fast segmentation.

The invention discloses a road marking automatic detection and classification method based on a mobile laser scanning point cloud. The method comprises the following steps: S1, performing pavement segmentation on original point cloud data to obtain pavement point cloud data; S2, performing intensity correction on the pavement point cloud data by use of an incident angle; S3, performing binary processing on the pavement point cloud data, and extracting road marking points; S4, segmenting the road marking points to separate mutually independent road marking targets; S5, calculating feature parameters of the road marking targets; and S6, by use of the feature parameters, constructing a decision tree, and classifying the road marking targets. The method can rapidly and accurately perform automatic detection and classification of road marks from the mobile laser scanning point cloud, greatly reduces the data processing time and the labor cost, and effectively guarantees the traffic safety and the reliability of intelligent driving.

The invention discloses a face recognition method without face data training. The invention does not need massive face data training, and automatically recognizes the collected face pictures under thecondition of lacking a face base database, which is convenient to quickly collect and classify the massive human data, reduces the workload of manual recognition, and has good adaptability to the recognition task of strangers. The invention realizes the real-time updating of the data in the human face base, improves the definition of the image in the human face base, and improves the accuracy ofthe human face recognition. The invention also discloses a face recognition system without a face base library. The face feature clustering module of the invention makes up for the problem of inaccurate judgment of the side face of the MTCNN, improves the accuracy of the photograph acquisition and improves the accuracy of the face recognition. So that face recognition technology can be used in a wider and more complex scene.

The invention relates to a road edge detection system and method based on laser radar and fan-shaped space segmentation. The method comprises the steps of 1, a laser radar scanning the surrounding environment of a vehicle to obtain reflection point cloud information and convert the reflection point cloud information into a locally constructed three-dimensional coordinate system; 2, preprocessing the point cloud data, and separating and extracting ground data in each frame of point cloud; 3, dividing the space in the coordinate system into fan-shaped structural bodies according to the data characteristics of the laser radar and the point cloud, and identifying the road extension direction according to the ground information and the fan-shaped structural bodies; 4, extracting road edge candidate points in the point cloud by using a parallel road edge retrieval algorithm; 5, clustering the road edge candidate points, and eliminating an interference point set according to fan-shaped spatial features; and 6, performing B spline curve fitting on the finally determined road edge point to obtain a road edge detection result. The method is high in adaptability, capable of adapting to roadsof various shapes and reducing the influence of obstacles, high in precision and reduction degree, high in reliability and low in error rate.

The invention relates to the technical field of conducting on-line detection through the machine vision and image processing technology, mainly relates to a method for conducting on-line detection on the non-continuity lithium battery thin film defects through a machine vision system and provides a non-continuity lithium thin film defect on-line detection method based on the machine vision. A grey level mutational point is obtained through adjacent grey level point difference method of three horizontal scanning lines, and therefore a continuity thin film section is determined; an optimal threshold value algorithm is used for achieving the binarization segmentation of a grey level image, a reversing large area defecting method is used for the binarization image to position a defective target, the geometrical and projection characteristics of the defects are extracted to be used as identification parameters, and finally the minimum Euclidean distance is used for achieving the rapid identification and classification of the defect target.

The invention provides a space domain and frequency domain characteristic based parallel SAR (synthetic aperture radar) image classification method. The method includes: by combining space domain and frequency domain characteristics of SAR images and based on the parallel computation environment, dividing the SAR images into n blocks prior to selecting a small image block in the size of 8*8 pixels around each pixel from each image block, computing corresponding wavelet energy features, gray-level co-occurrence matrix features and filtered gray-level average features of each pixel in each small image block, recovering the wavelet energy features, gray-level co-occurrence matrix features and filtered gray-level average features of each pixel in the n small image blocks to obtain wavelet energy features, gray-level co-occurrence matrix features and filtered gray-level average features of each pixel in the SAR images, forming the features into feature vectors for clustering, and finally classifying the SAR images. According to the method, quick classification of the SAR images depends on efficient information processing capability of a parallel cluster computer system, quick classification is realized, and the problem of low speed of SAR image classification in large data volume is solved.

Systems and methods for inspecting a surface of a specimen such as a semiconductor wafer are provided. A system may include an illumination system configured to direct a first beam of light to a surface of the specimen at an oblique angle of incidence and to direct a second beam of light to a surface of the specimen at a substantially normal angle. The system may also include a collection system configured to collect at least a portion of the first and second beams of light returned from the surface of the specimen. In addition, the system may include a detection system. The detection system may be configured to process the collected portions of the first and second beams of light. In this manner, a presence of defects on the specimen may be detected from the collected portions of the first and second beams of light.

The present invention relates to a system, method, and computer program product for recognition objects in a domain which combines feature-based object classification with efficient search mechanisms based on swarm intelligence. The present invention utilizes a particle swarm optimization (PSO) algorithm and a possibilistic particle swarm optimization algorithm (PPSO), which are effective for optimization of a wide range of functions. PSO searches a multi-dimensional solution space using a population of “software agents” in which each software agent has its own velocity vector. PPSO allows different groups of software agents (i.e., particles) to work together with different temporary search goals that change in different phases of the algorithm. Each agent is a self-contained classifier that interacts and cooperates with other classifier agents to optimize the classifier confidence level. By performing this optimization, the swarm simultaneously finds objects in the scene, determines their size, and optimizes the classifier parameters.

Provides methods, apparatus, and systems for processing an electronic document and its corresponding device, a method for browsing an electronic document and its corresponding browser, and an electronic document classification and query method and its corresponding system for the same. The method for processing an electronic document comprises generating at least one category names to which the document belongs according to the content of said electronic document when being written by an author; and correspondingly storing said category name information with the electronic document. Wherein the category name(s) which the document belongs has passed the verification in order to ensure its reliability.

The invention discloses a skeleton data behavior identification method based on a graph convolutional neural network, and the method comprises the steps: carrying out the downsampling of the time dimension of each segment of skeleton data in a training set, obtaining skeleton data with a fixed time dimension for training the neural network; dividing the input data into three channels according tothree-dimensional coordinates, conducting graph convolution with double attention on each space channel, and then combining the three channels; performing space-time convolution on the combined vectors; updating the weight of the neural network by labeling the action category; and finally, obtaining a behavior recognition model strongly related to the specified labeling result. According to the method, the topological structure and the space-time relationship of skeleton data are fully utilized, and the behavior recognition performance is improved.

The invention provides a commodity style classification determination method and an apparatus thereof. The method comprises the following steps of acquiring a commodity picture, and using a trained convolutional neural network to extract a characteristic vector of the commodity picture; calculating cluster density of the characteristic vector, and according to the cluster density, calculating a density distance between the characteristic vector and a first characteristic vector whose cluster density value is greater than a cluster density value of the characteristic vector; according to the cluster density of the characteristic vector and the density distance, determining an initial quantity and an initial center of a characteristic vector cluster; according to the initial quantity and the initial center of the cluster, carrying out characteristic vector clustering on the commodity picture, and acquiring a cluster result of a cluster stabilization condition satisfying setting; and according to the cluster result, determining commodity style classification. In the technical scheme provided in embodiments of the invention, an automatic, rapid, accurate and reliable classification basis is provided for a commodity style, accuracy and efficiency of commodity style classification are increased, and working strength of a worker is reduced.

The invention relates to a silicon chip surface defect detector capable of classifying defects and a defect classifying method. The flat surface of the silicon chip to be detected on a highly reliablesilicon chip working platform is irradiated by a laser from an inclination direction. Circular narrow channel collection and annular narrow channel collection are carried out on scattered lights produced by the silicon chip surface defect by two paraboloidal mirrors with different calibers in a direction near the silicon chip face normal and parallel with silicon chip face. Two photoelectric detectors detect intensities of the collected electrooptical signals in two channels respectively and then take a ratio of two intensities, then compare the ratio with a given threshold value, and at lastthe silicon chip surface defects can be classified. The defect detector has the advantages of simple structure, high sensitivity, fast detecting speed, capability of classifying the defects on the silicon chip surface, etc.

A process in which a ridge flow analysis of a fingerprint image can be used to determine a fingerprint classification. Areas of like valued ridge flow are collected as regions and said regions are then joined together when edges of the regions exist. These vertices are then reduced to form polygons. Polygons that have a predetermined number of vertices (i.e., 8) and contain values from across a range of the possible ridge flow values (i.e., one of each possible ridge flow value) from the ridge flow table are primary polygons associated with singularity points. These singularity points are further identified as cores or deltas allowing the print image to be globally classified as loop, arch or whorl. Further techniques including, but not limited to, ridge counting can be used to classify the fingerprint as a left or right loop, plain or tented arch, or whorl.

A method and device are provided for the objective evaluation of voice quality of a speech signal. The device includes: a module for extracting a background noise signal, referred to as a noise signal, from the speech signal; a module for calculating the audio parameters of the noise signal; a module for classifying the background noise contained in the noise signal on the basis of the calculated audio parameters, according to a predefined set of background noise classes; and a module for evaluating the voice quality of the speech signal on the basis of at least the resulting classification relative to the background noise in the speech signal.

The invention discloses a transformer area line loss calculation method based on correlation analysis and data mining. The transformer area line loss calculation method selects the characteristic index data with greater influence as the input of the deep belief network by comparing and analyzing the influence of the electrical characteristic indexes of different transformer areas on the line lossrate of the transformer areas, carries out training by distinguishing different types of transformer areas by using a clustering algorithm respectively so as to mine the complex incidence relation between the input parameters and the transformer area line loss rate, and finally, generates a transformer area line loss prediction model which is rapid and efficient in calculation and relatively highin result accuracy, so that the transformer area line loss is calculated and analyzed by utilizing the model, and the problems that an existing transformer area line loss calculation method cannot accurately and effectively mine association of transformer area line loss influence factors, the calculation working efficiency and accuracy are low and the like are solved.

A method for providing qualitative analysis of solid forms of a chemical compound / or drug candidate including polymorphous, hydrates, solvates and amorphous solids that does not require an a prior knowledge of either the solid form or the total number of groups of solid forms.

The invention discloses a deep learning-based haze visibility detection method. The method comprises the following steps of: firstly creating a road traffic haze image library which comprises a training sample set and a cross validation sample set; preprocessing all the haze images in the traffic haze image library; extracting farthest visibility edge features of the preprocessed haze images I the training sample set by adoption of a convolutional neural network so as to obtain a plurality of feature maps; forwardly propagating the feature maps to a configured convolutional neural network to be trained, adjusting weights between layers in the convolutional neural network through a counter-propagation algorithm, carrying out repeated iteration to solve a convolutional neural network model for haze image classification, and optimizing the convolutional neural network model through the preprocessed cross validation sample set so as to finally obtain a visibility detection model for haze image classification; and carrying out classification judgement on pictures shot by a pavement by utilizing the obtained visibility detection model so as to detect the haze condition in real time.