217results about How to "Accurate labeling" patented technology

The present disclosure relates to performing similarity metric analysis and data enrichment using knowledge sources. A data enrichment service can compare an input data set to reference data sets stored in a knowledge source to identify similarly related data. A similarity metric can be calculated corresponding to the semantic similarity of two or more datasets. The similarity metric can be used to identify datasets based on their metadata attributes and data values enabling easier indexing and high performance retrieval of data values. A input data set can labeled with a category based on the data set having the best match with the input data set. The similarity of an input data set with a data set provided by a knowledge source can be used to query a knowledge source to obtain additional information about the data set. The additional information can be used to provide recommendations to the user.

The invention discloses a sample data processing method and device and a computer readable storage medium. The method comprises the steps that a classification model is acquired; automatic annotationof model retraining sample data is performed through the classification model; the model retraining sample data and an automatic annotation result are displayed, the selection of correcting the displayed automatic annotation result is received, and an annotation expansion result of the model retraining sample data is obtained; and the model retraining sample data and the annotation expansion result are fed back to the model for training till the classification model no longer obtains classification performance improvement and iterative optimization of the annotation result corresponding to themodel retraining sample data is performed, and the error correction and leakage finding process of the corresponding annotation result is completed. In this way, annotation accuracy is improved, thesample data is continuously expanded, the corresponding automatic annotation result is obtained after automatic annotation through the classification model, the more precise annotation expansion result is obtained through correction, and the sample data in a large scale and large-quantity wide-dimensional annotation performed on the sample data are obtained.

A medical syringe apparatus includes a syringe unit, a hypodermic needle attached to the syringe unit and at least one interfacing member for mating a labeled medication container to the syringe unit. A filling needle is attached to the syringe unit for drawing fluid from a container. A movable needle sheath protects the hypodermic needle when not in use and may be moved out of the way when administering an injection. The medication label is visible. The container remains mated to the syringe when the syringe is in use. Risk of mislabeling the syringe may therefore be substantially reduced or eliminated and patient safety may accordingly be improved.

A semantic-based approach that classifies Android malware via dependency graphs. To battle transformation attacks, a weighted contextual API dependency graph is extracted as program semantics to construct feature sets. To fight against malware variants and zero-day malware, graph similarity metrics are used to uncover homogeneous application behaviors while tolerating minor implementation differences.

A method and apparatus for vending a containerized fluid includes placing a machine-readable indicia on the container identifying the base fluid. The container may be filled and then distributed or distributed prior to filling through various distribution channels to consumers. When a consumer wishes to fill or re-fill the container, the consumer places the container adjacent a reader that interprets the indicia to identify the base fluid to be dispensed. A dispenser then fills the container through a movable fill probe that elevates as the liquid within the container rises. In some embodiments, and prior to filling, the dispenser apparatus may also query the consumer about options are provided at the time of dispensing for further dispensing various optional ingredients, including scents, flavors, colors, nutritional supplements or other additives together with the base product. In further embodiments, the dispenser may also print discount tickets or coupons that may discount the purchase of the containerized fluid or other promotional merchandise. The machine-readable indicia may include affiliate codes that permit crediting or compensating various affiliates in relation to the container filling.

The present invention is a combined image viewing, drawing, and reporting system, which provides: an easy way to access digital images while creating a digital drawing; and an automated method of creating medical reports by translating the drawing data into standard reports. The present invention system provides automated medical drawings and creates medical reports as a result of translating drawing results. The system provides invaluable tools for drawing, diagnosing and treating ophthalmic and / or other types of diseases by generating an automated translation report based on the physician drawing. Customized reports (e.g., a medical drawing report, a referral note, a medical letter, billing based information, a proofsheet for the patient chart, etc.) can be added by the user. A physician can design a format for a report via system utility tools. The present invention may be a stand-alone system, and / or connected to a network.

The invention relates to live-action information system and method thereof based on a GPS positioning and direction identification technology, comprising a live-action information device, a communication network and an interest database. The live-action information device comprises a camera, a GPS positioning module, a horizontal and vertical direction identification module, a display module, a data storage module and a processing control module, wherein the processing control module is connected with the other six modules. The method comprises the following steps of: picking up an external live-action image by the camera, acquiring current GPS coordinate information by the GPS positioning module, acquiring information by adopting the horizontal and vertical direction identification module, analyzing the information to establish searching criteria, identifying, acquiring points of interest and describing data of the points of interest, transmitting the data to the interest database, and overlying and fusing the information to show the describing information of the points of interest. The invention has the advantages of perfect dynamic fusion of live action and data, lively observation and inquiry to surrounding scenes, precise, convenient and visualized information about directions and distances of the points of interest, and rich content of the surrounding points of interest.

The invention relates to an interactive method of image semi-automatic annotation, comprising S1 dividing an initial sample into three different types of annotation samples according to different category attributes; labeling the three types of labeling samples manually to get different kinds of labeling results, and then using three models of Mask-RCNN, Fast-RCNN and FCN to train separately; S2 processing the data set of the picture to be annotated in an offline manner, wherein the annotating process is that the data set of the picture to be annotated passes through the three depth learning models in turn to output the json format files of all types and coordinate points of the data samples; S3 calling the relevant attribute tag value and coordinate point value of the json format file according to the name of the annotated image; S4 displaying the corresponding automatic marking result in the marking software, and judging whether the category and area marking of the target object arestandardized and reasonable by manpower; S5 carrying out data augmentation on the correctly labeled labeling samples and feeding back the augmented data to the model for retraining.

The invention discloses a method and device used for processing point cloud data. A concrete implementation method includes displaying a to-be-marked point cloud frame and a camera image formed by photographing one scene at the one moment by the point cloud frame; determining an area of an object in the point cloud frame in response to operation of selecting the object in the point cloud frame; projecting the area into the camera image from the point cloud frame and obtaining a projection area in the camera image; adding a mark in the projection area so as to enable a user to mark the selectedobject in the point cloud frame according to the object indicated by the mark in the camera image. By adopting the implementation method, aid can be provided to a marking worker to mark the object inthe point cloud frame quickly and accurately.

The invention relates to an electronic map, in particular to an electronic map provided with roadside panoramic view picture strips, a method for manufacturing the same, and a method for marking interested places. The invention is dependent on road information of the prior electronic map and the correlation between the road information and the panoramic view picture strips for the overlay of the panoramic view picture strips showing both roadsides on roads displayed on the electronic map. Each panoramic view picture strip is a narrow picture which records all the scenes on a section of road, and users can comprehensively, continuously and intuitively know the conditions on both sides of a street through the panoramic view picture strip and determine the actual position of a place in the panoramic view picture strip on the electronic map.

The present invention relates to an image automatic marking method based on Monte Carlo data balance. The method comprises the steps of carrying out the region segmentation on the training sample images in a public image library, enabling the segmented regions possessing different characteristic description to correspond to one marking word, then carrying out the Monte Carlo data balance on the different types of image sets, extracting the multiscale characteristics of the balanced images, and finally inputting the extracted characteristic vectors in a robustness least squares increment limit learning machine to carry out the classification training to obtain a classification model in the image automatic marking; for the to-be-marked images, carrying out the region segmentation on the to-be-marked images, adopting the same multiscale characteristic fusion extraction method and inputting the extracted characteristic vectors in the least squares increment limit learning machine to obtain a final image marking result. Compared with a conventional image automatic marking method, the method of the present invention enables the images to be marked more effectively, is strong in timeliness, can be used for the automatic marking of the large-scale images, and possesses the actual application meaning.

The invention discloses a method and a system for distributing and identifying an optical network resource. Optical network equipment is provided with a two-dimension code label, and equipment information of the optical network equipment is recorded in the two-dimension code label; the two-dimension code label on the optical network equipment is scanned by using a mobile communication terminal, and the equipment information is identified and sent to an information processing center; the information processing center searches port configuration information associated with the equipment information according to the received equipment information, and sends the port configuration information to the mobile communication terminal; and the port configuration information of the current optical network equipment is displayed on a display screen of the mobile communication terminal to guide the field operation of on-site engineering technicians so as to improve the field operation efficiency. The invention solves the problem that the port status information of traditional optical network equipment cannot be electronically acquired to cause a condition that the optical network resource cannot be intelligently managed, and the accurate utilization rate and management efficiency of the optical network resource are improved.

The invention provides a method for establishing a gait data set. The method comprises steps as follows: 1) a test person walks along a straight line in a test area, and wearable sensors fixed on the left foot and the right foot of the test person acquire sensing data of a current walking process sample to serve as gait data of the sample; the wearable sensors comprise an inertial sensor and a sound sensor; 2) gait parameters of the current walking process sample are obtained by the aid of a camera preinstalled in the test area and / or footprints left in the test area; 3) gait data of the corresponding sample are marked by means of the gait parameters obtained in the step 2), and the marked gait data set is obtained. The invention further provides a corresponding gait analysis method based on the labeled data set. The accuracy of precise gait analysis is easily improved on the basis of the wearable sensors; multi-modal data are integrated, and the obtained data set is large in data volume, clear in classification and convenient to analyze and research.

The embodiment of the invention provides a method for training an image semantic annotation device, and the method comprises the steps: a, providing a plurality of training images, wherein the semantic and visual attribute description of each training image is known; b, enabling at least a part of the training images to be inputted to a positioner of the image semantic annotation device; c, enabling the positioner to determine at least one local area of each inputted training image, and inputting the determined local regions into an attribute predictor of the image semantic annotation device; d, obtaining the visual attribute prediction result of each inputted local region through the attribute predictor; e, training the positioner and the attribute predictor according to an obtained visual attribute prediction result of each local region and the known visual attribute description of the corresponding training images.

The invention discloses an intelligent optical fiber connecting and distributing system and an adapter port state information identification method. The intelligent optical fiber connecting and distributing system comprises an optical fiber connecting and distributing device, a mobile communication terminal and an information processing center; optical fiber adapters, a microprocessor assembly for forming an electronic tag and a wireless communication module are arranged in the optical fiber connecting and distributing device; the electronic tag is used for storing port state information of each optical fiber adapters and is communicated with the mobile communication terminal by the wireless communication module; and the mobile communication terminal is used for searching the port state information of the optical fiber adapters by accessing the electronic tag and transmitting update data to the electronic tag and the information processing center when the port state information needs to be updated so as to complete the dynamic updating on the port state information of the corresponding optical fiber adapters. The invention solves the difficult problem that the port state information of a conventional optical fiber connecting and distributing device cannot be electronically acquired, so that an optical network cannot implement the intelligent management, and improves the management efficiency of the optical network.

Described is a solution for an unlabeled target domain dataset challenge using a domain adaptation technique to train a neural network using an iterative 3D model fitting algorithm to generate refined target domain labels. The neural network supports the convergence of the 3D model fitting algorithm and the 3D model fitting algorithm provides refined labels that are used for training of the neural network. During real-time inference, only the trained neural network is required. A convolutional neural network (CNN) is trained using labeled synthetic frames (source domain) with unlabeled real depth frames (target domain). The CNN initializes an offline iterative 3D model fitting algorithm capable of accurately labeling the hand pose in real depth frames. The labeled real depth frames are used to continue training the CNN thereby improving accuracy beyond that achievable by using only unlabeled real depth frames for domain adaptation.

The invention relates to a semantic point cloud generation method, device and equipment based on laser radar and visual fusion and a computer readable medium. The method comprises the following stepsthat a point cloud cluster map is obtained based on laser radar point cloud data; an object detection classification map is obtained based on image data of a vision sensor; according to position information of the detected object and classification tag probability information in the object detection classification map and position information of laser clusters in the point cloud cluster map, classification label probabilities, matched with the position of the detected object, of the laser clusters are obtained; and according to the classification label probability of the laser clusters, classification label of the detected object is marked onto the laser clusters, and semantic point cloud data are generated. The classification label probabilities of the laser clusters at corresponding positions are calculated based on the classification label probability of the detected object, the laser clusters are subjected to classification label marking, and therefore accurate point cloud laser cluster classification label marking is achieved, and accuracy of semantic annotation of the generated semantic point cloud is improved.

In a method for labeling, a label strip (20) is moved by means of an electric motor (80). Arranged on this label strip are labels (26) of predetermined length (EL) with uniform interstices (SB). The motor has associated with it a position controller (218), also a sensor (44) for sensing a predetermined position of a label (26) when the latter is moved on the label strip (20) relative to the sensor (44). The method has the following steps: in accordance with a stored profile, the label strip (20) is set in motion beginning from a start position (A), a first target position (Z) of the label strip being specified to the position controller (218); during the motion of the label strip (20), a predetermined position (M) of the label strip (20) is sensed; and subsequently thereto, a revised target position (Z) is specified to the position controller (218). This makes possible fast and precise labeling, since the target position can be reached very accurately. A corresponding device has a compact design.

An embodiment of the invention provides a method for generating a grid map used for unmanned vehicle navigation control, and belongs to the field of automatic driving. The method comprises the steps of performing segmentation clustering on original point clouds according to the positions of the original point clouds, and dividing the original point clouds into multiple blocks; extracting feature data of each block in the blocks; and according to the feature data of each block, classifying point clouds in each block, and projecting classified point cloud data to the grid map. According to the method, regions occupied by barriers can be marked on the map accurately, and the type of the barrier in each region of the map can be marked.

Disclosed are systems and methods for generating data sets for training deep learning networks for key point annotations and measurements extraction from photos taken using a mobile device camera. The method includes the steps of receiving a 3D scan model of a 3D object or subject captured from a 3D scanner and a 2D photograph of the same 3D object or subject at a virtual workspace. The 3D scan model is rigged with one or more key points. A superimposed image of a pose-adjusted and aligned 3D scan model superimposed over the 2D photograph is captured by a virtual camera in the virtual workspace. Training data for a key point annotation DLN is generated by repeating the steps for a plurality of objects belonging to a plurality of object categories. The key point annotation DLN learns from the training data to produce key point annotations of objects from 2D photographs captured using any mobile device camera.

A human face image marking method includes clustering human face images to be marked; calculating the rate of the varieties of the human face images belonging to marked humans according to pre-stored classifier models, and marking the human face images according to the rate; training fresh classifiers and updating the classifier models according to the marked human face images. The invention further provides a human face image marking system. Thus, fast and accurate human marking of the human face images can be implemented, the marking speed and accuracy rate are increased, the effects of high marking speed and high accuracy are more significant when massive human face images are marked especially, and the massive human face image marking efficiency can be improved greatly.

The invention discloses an event element identification method and device, and relates to the technical field of information extraction. According to the specific implementation scheme, the event element identification method comprises: obtaining candidate sentences from a target corpus according to a preset trigger word list, and the candidate sentences comprising trigger words in the preset trigger word list; determining event statements and event types of the event statements from the candidate statements through statement characteristics of the candidate statements by utilizing a rapid text classification model; and according to the event type of the event statement, identifying event elements in the event statement. According to the method, the statement characteristics of the candidate statements are obtained by utilizing the rapid text classification model, and the event statements can be rapidly and accurately screened out, so that event elements in the identified event statements can be more accurate.

The invention discloses a pathological image automatic labeling method based on reinforcement learning and a deep neural network, and the method comprises the following steps: carrying out the multi-scale superpixel division of a dyed standardized original pathological image, and marking the epithelial probability threshold of superpixels; constructing a super-pixel classification training set; training the marked superpixels based on a machine learning method to obtain a learning model, classifying the superpixels in the test image by the learning model, awarding and punishing a classification result by a pathologist, feeding back awarding and punishing results to the learning model, and adjusting the learning model again through an awarding and punishing mechanism; constructing a training set of an end-to-end labeling model by adopting a pre-established method; adopting end-to-end learning based on a deep neural network to automatically mark and segment an organization region; constructing a labeling model; and testing the constructed annotation model by using pre-configured real data. The method has the beneficial effect that the learning models of different tissue regions can be quickly, accurately and automatically marked.

A method for automatically identifying and extracting K complex waves in sleep brain waves comprises the following steps of performing wavelet decomposition and reconstruction on brain wave signals; performing Teager energy operator calculation on reconstructed data and obtaining absolute values; smoothing and performing 0 / 1 coarse graining on an obtained Teager energy curve; performing threshold detection on the data which is performed on coarse graining; performing morphology detection on reconstructed signals satisfying the threshold values, enabling the signals at positions which satisfying a morphology condition to be the K complex waves and recording starting and final positions and wave crest and wave trough values and positions. The method for automatically identifying and extracting the K complex waves in the sleep brain waves has the advantages of analyzing the signals which is performed on the wavelet decomposition and the reconstruction through the Teager energy operator, extracting an absolute value sequence of the Teager energy operator and performing smoothness and coarse graining processes, being easy to achieve and high in anti-noise capacity, accurately confirming K complex wave positions and the wave crest and trough values and positions and establishing foundation for identification of a non-rem second period in sleep stage and research of the K complex waves.

An automatic positioning method of network key resource pages belongs to the Internet information handling sector. The invention is characterized in that: the invention firstly screens out inquiry theme words with timeliness and typicality from a number of search engine user inquiry click information records, extracts the corresponding user click result page and 'user click rate' on the result page, and obtains the inquiry theme collection, key resource page candidate collection, and 'user click distribution'; and then, the invention integrates the 'user click distribution' obtained from user inquiry click information records of different search engines, and obtains the integrated 'user click distribution' through 'inquiry liability' information; at last, the invention judges the pages in the key resource page candidate collection according to the integrated 'user click distribution' so as to obtain the subject-related key resource page. The method has the advantages of being automatic, accurate and objective, and being quickly positioned with computers.

The invention discloses an image annotation method and system based on a weak matching probability canonical correlation model, relating to the technical field of processing of network cross-media information. The image annotation method comprises the following steps: obtaining an annotated image and a non-annotated image in an image database, respectively extracting image features and textual features of the annotated image and the non-annotated image, and generating a matched sample set and an unmatched sample set, wherein the matched sample set contains an annotated image feature set and an annotated textual feature set; and the unmatched sample set contains a non-annotated image feature set and a non-annotated textual feature set; training the weak matching probability canonical correlation model according to the matched sample set and the unmatched sample set; and annotating an image to be annotated through the weak matching probability canonical correlation model. According to the invention, correlation between a visual modality and a textual modality is learned by using the annotated image, keywords of the annotated image and the non-annotated image simultaneously; and an unknown image can be accurately annotated.

The invention discloses a Pulmonary nodule labeling system comprising the following parts: an import module used for importing a CT image; a labeling module used for labeling the CT image and obtaining a first CT image; the labeling module comprises an image display unit, a drawing unit, a data forming unit, and a label filling unit; the image display unit is used for displaying each slice image of the CT image; the drawing unit is used for drawing a Pulmonary nodule frame on the slice image; the data forming unit is used for forming size data of the Pulmonary nodule frame, and forming a framenumber; the label filling module is used for displaying the frame number, and allowing a labeler to fill in the Pulmonary nodule information according to the frame number; the system also comprises adatabase used for storing data labels corresponding to the first CT image, and an export module used for exporting the first CT image and corresponding data labels. The Pulmonary nodule marking system can accurately and effectively label the Pulmonary nodule on the CT image, thus providing accurate training samples for a Pulmonary nodule detection system.