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1359results about How to "Enhance expressive ability" patented technology

Front small-region object recognition and vehicle early warning method

The invention relates to a front small-region object recognition and vehicle early warning method, and belongs to technical field of early warning for collision in front of vehicles. The front small-region object recognition and vehicle early warning method specifically comprises the steps that an image of a target to be recognized in front of a vehicle is acquired, and a region of interest (ROI)image is obtained; image preprocessing is performed on the obtained region of interest image, the preprocessed region of interest image is inputted into an improved YOLO convolutional network structure model to perform target recognition, and a target regression frame and a target category are outputted; the recognition result is inputted into a vehicle early warning system, if the target regression frame is located beyond the range of a vehicle driving route or the target is recognized to be of a non-dangerous category, the target is marked as a non-dangerous target, and the vehicle does notperform early warning; and if the target regression frame is located within the vehicle driving route and the recognized object belongs to a dangerous category or the object cannot be recognized but has certain speed features, the target is marked as a dangerous target, the vehicle performs early warning to remind a driver to pay attention so as to avoid occurrence of an accident.
Owner:JILIN UNIV

Method and device for acquiring knowledge graph vectoring expression

ActiveCN105824802ARich relevant informationSolve the problem of insufficient representation effect caused by sparsityNatural language data processingSpecial data processing applicationsStochastic gradient descentGraph spectra
The invention discloses a method and a device for acquiring knowledge graph vectoring expression. The method comprises the following steps of labeling an entity, existed in and belonging to a knowledge graph, in a given auxiliary text corpus by utilization of an entity labeling tool according to a to-be-processed knowledge graph so as to obtain an entity-labeled text corpus; constructing a co-occurrence network comprising words and entities on the basis of the text corpus so as to relate text information of the auxiliary text corpus to entity information of the knowledge graph, and then learning to obtain a text context embedded expression; respectively modeling the embedded expression of the entity and relation in the knowledge graph according to the text context embedded expression so as to obtain an embedded expression model of the knowledge graph; training the embedded expression model by utilization of a stochastic gradient descent algorithm so as to obtain the embedded expression of the entity and relation in the knowledge graph. The method and the device disclosed by the invention have the advantages that not only can the expression capability of the relation be improved, but also the problem of insufficient expression effect caused by sparseness of the knowledge graph can be effectively solved.
Owner:TSINGHUA UNIV

Deep learning-based human body motion recognition method of multi-channel image feature fusion

The invention discloses a deep learning-based human body motion recognition method of multi-channel image feature fusion. The method comprises: (1) extracting original RGB pictures from videos, and calculating dynamic graphs and optical flow graphs of the segmented videos through the RGB pictures; (2) carrying out cropping operations on the input pictures to expand a training data set; (3) constructing a three-channel convolutional-neural-network, and respectively inputting lastly obtained video segments into the three-channel convolutional-neural-network to carry out training to obtain a corresponding network model; and (4) for a to-be-recognized video segment, extracting original RGB pictures, calculating dynamic graphs and optical flow graphs corresponding thereto, and obtaining a recognition result of a final motion category. According to the method, the three-channel convolutional-neural-network is utilized for learning essential features of data for original input of different morphologies, multi-channel dense fusion operations are carried out on the data of the three morphologies in the middle of the network, expression ability of the features is improved, and purposes of multi-channel information sharing and a high accuracy degree are achieved.
Owner:SOUTH CHINA UNIV OF TECH

Brain tumor segmentation network and segmentation method based on U-Net network

The invention discloses a brain tumor segmentation network and segmentation method based on a U-Net network. The tail of a contraction path of the segmentation network is connected with a spatial pyramid pooling structure; hole convolution of different scales is introduced into a network jump connection part of the segmentation network; an Add operation and original input are adopted to form a residual block with hole convolution; a receptive field of shallow feature information in the contraction path is expanded; fusing with an expansion path of a corresponding stage is carried out. The segmentation method comprises the following steps: cutting and preprocessing a training data set, then constructing a brain tumor segmentation network DCU-Net based on a U-Net network, then inputting a preprocessed two-dimensional image into a segmentation model for feature learning and optimization, obtaining an optimal parameter model of the segmentation model, and finally inputting a to-be-segmented test data set image into the segmentation model for tumor region segmentation. According to the method, the problems of over-segmentation and under-segmentation in brain tumor segmentation can be effectively solved, and the brain tumor segmentation precision is improved.
Owner:HENAN UNIVERSITY OF TECHNOLOGY

Image description generation method based on depth LSTM network

The invention relates to an image description generation method based on a depth LSTM network, comprising the following steps: (1) extracting the CNN characteristics of an image in an image description dataset, and acquiring an embedded vector corresponding to the image and describing the words in a reference sentence; (2) building a double-layer LSTM network, and carrying out series modeling based on the double-layer LSTM network and a CNN network to generate a multimodal LSTM model; (3) training the multimodal LSTM model by means of joint training; (4) gradually increasing the number of layers of the LSTM network in the multimodal LSTM model, carrying out training each time one layer is added to the LSTM network, and finally, getting a gradual multi-objective optimization and multilayer probability fused image description model; and (5) fusing the probability scores output by the branches of the multilayer LSTM network in the gradual multi-objective optimization and multilayer probability fused image description model, and outputting the word corresponding to the maximum probability through common decision. Compared with the prior art, the method has such advantages as multiple layers, improved expression ability, effective updating, and high accuracy.
Owner:TONGJI UNIV

Improved CNN-based facial expression recognition method

The invention provides an improved CNN-based facial expression recognition method, and relates to the field of image classification and identification. The improved CNN-based facial expression recognition method comprises the following steps: s1, acquiring a facial expression image from a video stream by using a face detection alignment algorithm JDA algorithm integrating the face detection and alignment functions; s2, correcting the human face posture in a real environment by using the face according to the facial expression image obtained in the step s1, removing the background information irrelevant to the expression information and adopting the scale normalization; s3, training the convolutional neural network model to obtain and store an optimal network parameter before extracting feature of the normalized facial expression image obtained in the step s2; s4 loading a CNN model and the optimal network parameters obtained by s3 for the optimal network parameters obtained in the steps3, and performing feature extraction on the normalized facial expression images obtained in the step s2; s5, classifying and recognizing the facial expression features obtained in the step s4 by using an SVM classifier. The method has high robustness and good generalization performance.
Owner:CHONGQING UNIV OF POSTS & TELECOMM

A face multi-area fusion expression recognition method based on depth learning

The invention discloses a face multi-area fusion expression recognition method based on depth learning, which comprises the following steps of detecting a face position with a detection model; obtaining the coordinates of the key points by using the key point model; aligning the eyes according to the key points of the eyes, then aligning the face according to the coordinates of the key points of the whole face, and clipping the face region by affine transformation; cutting the eye and mouth areas of the image to a certain proportion; dividing the convolution neural network into one backbone network and two branch networks; carrying out the feature fusion in the last convolution layer, and finally obtaining the expression classification results by the classifier. The method of the inventionutilizes the priori information, besides the whole face, the eyes and mouth regions are also used as the input of the network, and the network can learn the whole semantic features of facial expressions and the local features of facial expressions through model fusion, so that the method simplifies the difficulty of facial expression recognition, reduces the external noise, and has strong robustness, high accuracy, low complexity of the algorithm and so on.
Owner:SOUTH CHINA UNIV OF TECH

Senile dementia monitoring system based on healthy service robot

ActiveCN105078449AAvoid one-sidedness and inconsistency in diagnosisImprove accuracySensorsPsychotechnic devicesDiagnostic accuracyHealth services
The invention discloses a senile dementia monitoring system based on a healthy service robot. The senile dementia monitoring system based on the healthy service robot comprises the healthy service robot, an intelligent terminal and a cloud server. The healthy service robot comprises a robot body, a main control unit, a human-computer interaction unit and a medical detection unit; the human-computer interaction unit is connected with the main control unit and comprises a tablet computer, and the tablet computer is arranged in front of the chest of the robot body; the medical detection unit is connected with the main control unit and comprises a brain electrical detection device which is independent of the robot body, and the brain electrical detection device is connected with the intelligent terminal and the tablet computer through Bluetooth signals; the intelligent terminal and the tablet computer are connected with the cloud server through the mobile Internet, and data interaction between the intelligent terminal and the tablet computer is achieved through wireless signals. By means of the senile dementia monitoring system based on the healthy service robot, automatic auxiliary diagnosis and treatment of senile dementia can be achieved, the diagnostic accuracy is improved, prevention and early detection of the senile dementia are facilitated, the pathogenetic condition is mitigated from exacerbating, and the purpose of healing is achieved.
Owner:SOUTH CHINA UNIV OF TECH +1

Pre-fetching-based phishing web page detection method

The invention discloses a pre-fetching-based phishing web page detection method, and relates to the acquisition of website information and the extraction and classification of topological characteristics and mainly aims to solve problems on phishing web page detection capacity. In the method, a user interface module 1 serves as an interface, a master control module 2 serves as a center, and a classifier module 3, a characteristic extraction module 4 and a web page extraction module 5 are scheduled, wherein the classifier module needs training in a training set and adopts an incremental updating mode to ensure that a classifier keeps capacity in the detection of new phishing web pages; the characteristic extraction module mainly extracts the pre-fetched characteristics of topological website structures, saves the characteristics into a training set database and simultaneously transmits the characteristics to the classifier module; and the web page extraction module captures a certain number of web pages of a given website according to an instruction of the master control module and saves the web pages into a web page database. Through the pre-fetching-based phishing web page detection method provided by the invention, both accuracy and recall rate are greatly improved.
Owner:NANJING UNIV OF POSTS & TELECOMM

Commodity property characteristic word clustering method

The present invention relates to a commodity property characteristic word clustering method. The method comprises the following steps: A1: obtaining comment texts of a target commodity from related e-commerce websites, and performing data preprocessing; A2: selecting a comment text containing commodity property characteristic words, performing manual annotation on the commodity property characteristic words, and using the manually annotated commodity property characteristic words as a training sample of an obtained part-of-speech template; A3: training the part-of-speech template according to the manually annotated data in the A2; A4: using data obtained in the A1 to train a language model, thereby obtaining a word vector representation; and A5: using a word vector obtained in the A4 to perform clustering on the commodity property characteristic words obtained in the A3, thereby obtaining a final property characteristic word set of the target commodity. The commodity property characteristic word clustering method provided by the present invention can be applied to a commodity recommendation system based on a commodity comment text. The number of commodity property characteristic words can be reduced by clustering, so that characteristic dimensions and characteristic sparsity are reduced, and the designed recommendation system is faster and more accurate.
Owner:SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV

Method for automatically identifying breast tumor area based on ultrasound image

The invention discloses a method for automatically identifying a breast tumor area based on an ultrasound image. The method comprises the following steps of acquiring the ultrasound image of the breast, and preprocessing the ultrasound image; segmenting the ultrasound image subjected to preprocessing through an image segmentation method to obtain a plurality of segmented subareas; extracting a grey level histogram, texture features, gradient features and morphological features of the ultrasound image, and combining the grey level histogram, the texture features, the gradient features and the morphological features of the ultrasound image with two-dimensional position information to obtain high-dimensionality feature vectors; selecting the most effective feature subset of the high-dimensionality feature vectors through feature ordering based on biclustering and a selection method; performing learning classification on the selected most effective feature subset through a classifier, and then automatically identifying the breast tumor area. By means of the method, the breast tumor area can be identified automatically from segment results of the breast tumor ultrasound image, therefore, automation performance of computer-aided diagnosis is improved, manual operation of clinical doctors is reduced, and subjective influence of clinical doctors is reduced.
Owner:SOUTH CHINA UNIV OF TECH

Facial feature recognition method and system based on multi-region characteristic and metric learning

The invention discloses a facial feature recognition method and system based on the multi-region characteristic and metric learning. The method comprises the steps that convolution neural network parameters of the corresponding location and scale are obtained through the multi-scale facial area training, and corresponding facial area features are extracted according to the neural network parameters; the above features are filtered to obtain the high dimensional facial features; metric learning is conducted according to the high dimensional facial features, the after defined loss function of the feature expression is obtained through the dimension reduction processing of the features, a network model of the metric learning is obtained through the training of the loss function; the images to be recognized are inputted into the network model, the facial features are dimension reduced using the Euclidean distance in order to be recognized. In the method, multiscale is used to select multiple areas, the convolutional neural networks are trained, and the expression skills of the characteristics are improved. Meanwhile, through the selection of the acquired multi-scale features, the efficiency of expression of characteristics is improved, and the accuracy of face recognition is effectively improved.
Owner:苏州飞搜科技有限公司

Visual target tracking method based on self-adaptive subject sensitivity

The invention discloses a visual target tracking method based on self-adaptive subject sensitivity, and belongs to the technical field of computer vision. The visual target tracking method comprises an overall process, an offline part and an online part. The whole process includes: designing a target tracking process, and designing a network structure; adjusting the feature map of each stage of the network into an adaptive size to complete the end-to-end tracking process of the twin network; the offline part comprises six steps: generating a training sample library; carrying out forward tracking training; calculating a back propagation gradient; calculating a gradient loss item; generating a target template image mask; and training a network model and obtaining the model. The online part comprises three steps: carrying out model updating; carrying out online tracking; and positioning a target area. The model updating comprises forward tracking, back propagation gradient calculation, gradient loss item calculation and target template image mask generation; the online tracking comprises the steps of performing forward tracking to obtain a similarity matrix, calculating the confidencecoefficient of a current tracking result and returning to a target area. The method can better adapt to target robust tracking of appearance changes.
Owner:BEIJING UNIV OF TECH

Multilanguage question and answer interface fast constituting method based on domain ontology and template logics

The invention relates to a multilanguage question and answer interface fast constituting method based on domain ontology and template logics. The method comprises the following steps of (1) multilanguage domain ontology structure building; (2) first-order template logic system building based on the domain ontology; (3) multilanguage question template structure design based on domain ontology and template logics; (4) domain-oriented question template base building; (5) user question preprocessing and question template matching; (6) user question semanteme obtaining and multilanguage intertranslation method. The multilanguage question and answer interface fast constituting method has the advantages that the question template semanteme can be more precisely expressed through the domain ontology and the template logics, the question template expression ability is improved by combining template operation characters, higher representativeness is realized, the template base scale can be reduced, in addition, a multilanguage body is similar to a multilanguage semantic dictionary, and the cross-language information inquiry can be easily realized. According to the method, various multilanguage man-machine interactive interfaces of field-oriented intelligent information retrieval and automatic question and answer systems can be fast constituted.
Owner:盐城市凤凰园科技发展有限公司
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