Higher Correct Recognition Rate Research Articles

Fiber optic detection method for intrinsic defects in carbon fiber composite core overhead conductors considering bending performance

Carbon fiber composite core wire in the carbon fiber core rod bending strength is low, easy to break, and thus affects the detection effect. In this regard, it is proposed to consider the bending performance of carbon fiber composite core overhead wire intrinsic defects fiber detection method. The overhead wire is X-ray scanned, and the image is bent compensated by the filtering algorithm. The image is reconstructed using an iterative reconstruction algorithm, and the Tamura texture feature operator is used to extract the roughness feature parameters in the texture feature enhancement map of each conductor to achieve the identification of defects. In the experiments, the proposed method is verified for detection accuracy. The analysis of the experimental results shows that the proposed method has a high correct defect recognition rate and has a more excellent detection accuracy when the proposed method is used to detect the inherent defects of overhead conductors.

A multi-channel convolutional neural network based on attention mechanism fusion for facial expression recognition

Abstract Facial expressions can reflect people’s inner emotions to a certain extent, and studying facial expressions can help psychologists capture expression information in time and understand patients’ psychological changes quickly. In this paper, we establish a multi-channel convolutional neural network face expression recognition model based on the fusion of the attention mechanism. With the help of the attention mechanism and multi-channel convolutional neural network, we input expression images and perform average pooling and maximum pooling, output the features with high recognition after pooling, and identify the features with high recognition in expression images throughout the process. And with the help of multi-scale feature fusion, we improve the detection of subtle changes, such as the corners of the mouth and the eyes of the expression image target. The loss function is used to calculate the loss rate of facial expression images, which leads to the correct rate of facial expression recognition by a multi-channel convolutional neural network based on the fusion of attention mechanisms. It is demonstrated that the highest recognition correct rate of the multi-channel convolutional neural network faces expression recognition model with attention mechanism fusion is 93.56% on the FER2013 dataset, which is higher than that of the MHBP model by 23.2%. The highest correct recognition rate on the RAF-DB dataset is 91.34%, which is higher than the SR-VGG19 model by 19.39%. This shows that the multi-channel convolutional neural network face expression recognition based on the fusion of attention mechanisms improves the correct rate of facial expression recognition, which is beneficial to the research and development of psychology.

Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals

Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.

Detection of the moldy status of the stored maize kernels using hyperspectral imaging and deep learning algorithms

ABSTRACT It is significant to identify the moldy status of stored maize by fungi infection in the early stage. Hyperspectral imaging (HSI) combined with the sparse auto-encoders (SAE) and convolutional neural network (CNN) algorithms was used to classify the moldy grades of maize kernels. The HSI data were obtained in the range of 400–1000 nm, and four grades from health to heavy mildew were distinguished using the measured fungal spores of maize. The depth spectral features were represented using SAE and the image features were extracted by CNN. K nearest neighbors, support vector machine (SVM), and partial least squares discriminant analysis classifiers were combined with the spectral and image features to establish classification models to identify the different moldy grades of maize kernels. The comparison results indicated that the fusion of SAE and CNN combined with the SVM classifier to construct the SAE-CNN-SVM model had the most satisfactory identification result with high correct recognition rates of 99.47% and 98.94% for the training and testing sets, respectively, and the values of sensitivity and specificity were 0.95–1. The moldy grades were presented intuitively on the maize image based on pixels or kernel-wise. Therefore, the HSI with the SAE-CNN-SVM model had good recognition ability for the early detection of moldy maize kernels, which could potentially provide technical support for the development of online detection of moldy maize kernels during storage.

Multi-scale spatialtemporal information deep fusion network with temporal pyramid mechanism for video action recognition

In the deep learning-based video action recognitio, the function of the neural network is to acquire spatial information, motion information, and the associated information of the above two kinds of information over an uneven time span. This paper puts forward a network extracting video sequence semantic information based on deep integration of local Spatial-Temporal information. The network uses 2D Convolutional Neural Network (2DCNN) and Multi Spatial-Temporal scale 3D Convolutional Neural Network (MST_3DCNN) respectively to extract spatial information and motion information. Spatial information and motion information of the same time quantum receive 3D convolutional integration to generate the temporary Spatial-Temporal information of a certain moment. Then, the Spatial-Temporal information of multiple single moments enters Temporal Pyramid Net (TPN) to generate the local Spatial-Temporal information of multiple time scales. Finally, bidirectional recurrent neutral network is used to act on the Spatial-Temporal information of all parts so as to acquire the context information spanning the length of the entire video, which endows the network with video context information extraction capability. Through the experiments on the three video action recognitio common experimental data sets UCF101, UCF11, UCFSports, the Spatial-Temporal information deep fusion network proposed in this paper has a high correct recognition rate in the task of video action recognitio.

Research on Face Image Digital Processing and Recognition Based on Data Dimensionality Reduction Algorithm.

Because face recognition is greatly affected by external environmental factors and the partial lack of face information challenges the robustness of face recognition algorithm, while the existing methods have poor robustness and low accuracy in face image recognition, this paper proposes a face image digital processing and recognition based on data dimensionality reduction algorithm. Based on the analysis of the existing data dimensionality reduction and face recognition methods, according to the face image input, feature composition, and external environmental factors, the face recognition and processing technology flow is given, and the face feature extraction method is proposed based on nonparametric subspace analysis (NSA). Finally, different methods are used to carry out comparative experiments in different face databases. The results show that the method proposed in this paper has a higher correct recognition rate than the existing methods and has an obvious effect on the XM2VTS face database. This method not only improves the shortcomings of existing methods in dealing with complex face images but also provides a certain reference for face image feature extraction and recognition in complex environment.

Iris Recognition Based on Fine-Tune SquIrisNet

Convolutional neural networks (CNN) have achieved unprecedented results in the fields of pattern recognition and image processing. CNN methods have also been gradually applied to iris recognition. However, in the case of insufficient training samples, training deep CNN models is prone to overfitting. In order to solve the above problem, this paper proposes an iris recognition method based on fine-tune SquIrisNet. When the pre-trained SqueezNet is migrated to the iris dataset, the fully connected layers are used instead of the convolutional layer and the global pooling layer, then the parameters are adjusted using the error back propagation algorithm, and finally the images are classified by the Softmax classifier. The fully connected layers added in this model can play the role of “firewall” in the fine-tune process, retaining the model complexity to a certain extent. The experimental results on the IIT Delhi and SDUMLA-HMT iris databases show that the proposed method has higher correct recognition rate, faster convergence speed and better robustness than AlexNet and VGGNet.

Early determination of mildew status in storage maize kernels using hyperspectral imaging combined with the stacked sparse auto-encoder algorithm

The early detection the mildew status of maize kernels caused by fungal infection during storage is essential. Hyperspectral imaging (HSI) combined with the deep stacked sparse auto-encoders (SSAE) algorithm was employed to recognize the early mildewed degree of kernels. HSI data of kernels were collected in the spectral range of 400–1000 nm and the mildew status was classified into four grades (health, mild mildew, moderate mildew, and heavy mildew) using the measured fungal spores as the reference. The nonlinear features of the spectra were represented using the SSAE network, and the key wavelengths were selected by variable combination population analysis and random frog methods, which were combined with four classifiers-Softmax, kernel extreme learning machine (KELM), ELM, and support vector machine-to establish models for identifying the mildew grades of kernels. The comparative results indicated that SSAE combined with KELM classifier to build the SSAE-KELM model had better identification ability than other models with high total correct recognition rate of 97.36% and 96.84% for the training and testing sets, respectively. The sensitivity and specificity were 0.92–1. The different grades were visualized on the images of maize kernels based on pixels-level and object-level. The current results demonstrated that HSI with SSAE-KELM model performed well in the early detection of mildew status of maize kernel, and can be applied as the reference method for batch and online detection of mildew kernels to improve grain storage security.

The Analysis of Plants Image Recognition Based on Deep Learning and Artificial Neural Network

Classification and identification of plants are helpful for people to effectively understand and protect plants. The leaves of plants are the most important recognition organs. With the development of artificial intelligence and machine vision technology, plant leaf recognition technology based on image analysis is used to improve the knowledge of plant classification and protection. Deep learning is the abbreviation of deep neural network learning method and belongs to neural network structure. It can automatically learn features from big data and use artificial neural network based on back propagation algorithm to train and classify plant leaf samples. The main content of this paper is to extract plant leaf features and identify plant species based on image analysis. Firstly, plant leaf images are segmented by various methods, and then feature extraction algorithm is used to extract leaf shape and texture features from leaf sample images. Then the comprehensive characteristic information of plant leaves is formed according to the comprehensive characteristic information. In this paper, 50 plant leaf databases are tested and compared with KNN-based neighborhood classification, Kohonen network based on self-organizing feature mapping algorithm and SVM-based support vector machine. At the same time, the leaves of 7 different plants were compared and it was found that ginkgo leaves were easier to identify. For leaf images under complex background, good recognition effect has been achieved. Image samples of the test set are input into the learning model to obtain reconstruction errors. The class label of the test set can be obtained by reconstructing the deep learning model with the smallest error set. The results show that this method has the shortest recognition time and the highest correct recognition rate.

Study on face recognition under unconstrained conditions based on LBP and deep learning

At present, the correct recognition rate of face recognition algorithm is limited under unconstrained conditions. To solve this problem, a face recognition algorithm based on deep learning under unconstrained conditions is proposed in this paper. The algorithm takes LBP texture feature as the input data of deep network, and trains the network layer by layer greedily to obtain optimized parameters of network, and then uses the trained network to predict the test samples. Experimental results on the face database LFW show that the proposed algorithm has higher correct recognition rate than some traditional algorithms under unconstrained conditions. In order to further verify its effectiveness and universality, this algorithm was also tested in YALE and YALE-B, and achieved a high correct recognition rate as well, which indicated that the deep learning method using LBP texture feature as input data is effective and robust to face recognition.

Wireless Channel Scene Recognition Method Based on an Autocorrelation Function and Deep Learning

Wireless channel scene recognition plays a key role in cognitive radio (CR) mobile communication systems. This paper proposes a wireless channel scene identification framework based on the autocorrelation function and deep learning. First, a feature extraction (FE) method is developed to perform a channel scene date analysis based on the autocorrelation function (AF). The AF is used to realize the FE method because it can be combined with Fourier transform (FT) to accurately extract the characteristics accurately from a time-varying channels scene. Second, a deep belief network (DBN) with a robust learning approach is introduced to perform wireless channel scene recognition. A novel learning architecture is employed, which combines the feature parameter and classification techniques to achieve a high classification correct recognition rate. Third, the k-step contrastive divergence (CD-k) algorithm is introduced as the preliminary training method to optimize the traditional DBN network. This method can effectively calculate the logarithmic gradient of the Boltzmann machine. Moreover, the up-down optimization algorithm is applied to optimize the network parameters. Finally, the theoretical implementation is described in detail, and the method is verified by constructing an experiment platform for an engineering application. The experimental results indicate that the proposed classifier is an excellent approach to realize channel scene recognition through advanced methods. The classification accuracy of the proposed approach is higher than that of several existing techniques.

SVDTWDD Method for High Correct Recognition Rate Classifier With Appropriate Rejection Recognition Regions

At present, regions of the same class determined by Support Vector Machines (SVM) classifier, Support Vector Domain Description (SVDD) classifier and Deep Learning (DL) classifier may occupy regions of other classes or unknown classes in feature space. There exists a risk that samples of other classes or unknown classes are wrongly classified as a known class. In this paper, the Support Vector Domain Tightly Wrapping Description Design (SVDTWDD) method with appropriate rejection regions and the corresponding incremental learning algorithm are proposed to overcome the above problem. The main work includes: (1) We develop a construction algorithm of the tightly wrapping set for the homogeneous feature set; (2) Based on the homogeneous feature set and tightly wrapping set, a novel algorithm is presented for obtaining the tightly wrapping surface of the homogeneous feature region; (3) The method for constructing all the public regions outside of the tightly wrapping surface and the intersections of wrapping regions in two different tightly wrapping surfaces, as the rejection region of the classifier; (4) An incremental algorithm is also presented based on the SVD-TWDD method. The experimental results with UCI data sets show that the correct recognition rate of our proposed method is nearly100% even if with a low rejection rate.

Spatiotemporal information deep fusion network with frame attention mechanism for video action recognition

In the deep learning-based video action recognition, the function of the neural network is to acquire spatial information, motion information, and the associated information of the above two kinds of information over an uneven time span. We propose a network for extracting semantic information of video sequences based on the deep fusion feature of local spatial–temporal information. Convolutional neural networks (CNNs) are used in the network to extract local spatial information and local motion information, respectively. The spatial information is in three-dimensional convolution with the motion information of the corresponding time to obtain local spatial–temporal information at a certain moment. The local spatial–temporal information is then input into the long- and short-time memory (LSTM) to obtain the context relationship of the local spatial–temporal information in the long-time dimension. We add the ability of the regional attention mechanism of video frames in the neural network mechanism for obtaining context. That is, the last layer of convolutional layer spatial information and the first layer of the fully connected layer are, respectively, input into different LSTM networks, and the outputs of the two LSTMs at each time are merged again. This enables a fully connected layer that is rich in categorical information to provide a frame attention mechanism for the spatial information layer. Through the experiments on the three action recognition common experimental datasets UCF101, UCF11, and UCFSports, the spatial–temporal information deep fusion network proposed has a high correct recognition rate in the task of action recognition.

Correct recognition and continuum belief of mental disorders in a nursing student population

BackgroundThe current study aimed to explore the correct recognition of mental disorders across dementia, alcohol abuse, obsessive compulsive disorder (OCD), schizophrenia and depression, along with its correlates in a nursing student population. The belief in a continuum of symptoms from mental health to mental illness and its relationship with the non-identification of mental illness was also explored.MethodsFive hundred students from four nursing institutions in Singapore participated in this cross-sectional online study. Respondents were randomly assigned to a vignette describing one of the five mental disorders before being asked to identify what the person in the vignette is suffering from. Continuum belief was assessed by rating their agreeableness with the following statement: “Sometimes we all behave like X. It is just a question of how severe or obvious this condition is”.ResultsOCD had the highest correct recognition rate (86%), followed by depression (85%), dementia (77%), alcohol abuse (58%) and schizophrenia (46%). For continuum belief, the percentage of respondents who endorsed symptom continuity were 70% for depression, 61% for OCD, 58% for alcohol abuse, 56% for dementia and 46% for schizophrenia. Of concern, we found stronger continuum belief to be associated with the non-identification of mental illness after controlling for covariates.ConclusionsThere is a need to improve mental health literacy among nursing students. Almost a quarter of the respondents identified excessive alcohol drinking as depression, even though there was no indication of any mood symptom in the vignette on alcohol abuse. Further education and training in schizophrenia may need to be conducted. Healthcare trainees should also be made aware on the possible influence of belief in symptom continuity on one’s tendency to under-attribute mental health symptoms as a mental illness.

The Mobile Fruit Monitoring System Using Image Processing Technology and Temperature/Humidity Sensors

Recently, due to the rise in age of the population and the increase of novices in the countryside, the number of experienced workers in the countryside is decreasing. In addition, more and more people are suffering from the heat of the ever-increasing hot weather. In this paper a mobile fruit monitoring system based on the image processing technology and temperature/humidity sensors will be shown. The advantage of the mobile fruit monitoring system that is introduced in this paper is that it can be remote controlled by PCs and a Graphical User Interface (GUI) program by LabVIEW has been designed. Therefore it is no longer necessary to go to the orchards, experienced workers and novices can easily judge the ripeness of the fruits by the GUI program. From the experimental results shows that the proposed mobile fruit monitoring system has a very high correct recognition rate.

Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems

Two types of scores extracted from two-dimensional (2-D) and three-dimensional (3-D) palmprint for personal recognition systems are merged, introducing a local image descriptor for 2-D palmprint-based recognition systems, named bank of binarized statistical image features (B-BSIF). The main idea of B-BSIF is that the extracted histograms from the binarized statistical image features (BSIF) code images (the results of applying the different BSIF descriptor size with the length 12) are concatenated into one to produce a large feature vector. 3-D palmprint contains the depth information of the palm surface. The self-quotient image (SQI) algorithm is applied for reconstructing illumination-invariant 3-D palmprint images. To extract discriminative Gabor features from SQI images, Gabor wavelets are defined and used. Indeed, the dimensionality reduction methods have shown their ability in biometrics systems. Given this, a principal component analysis (PCA)+linear discriminant analysis (LDA) technique is employed. For the matching process, the cosine Mahalanobis distance is applied. Extensive experiments were conducted on a 2-D and 3-D palmprint database with 10,400 range images from 260 individuals. Then, a comparison was made between the proposed algorithm and other existing methods in the literature. Results clearly show that the proposed framework provides a higher correct recognition rate. Furthermore, the best results were obtained by merging the score of B-BSIF descriptor with the score of the SQI+Gabor wavelets+PCA+LDA method, yielding an equal error rate of 0.00% and a recognition rate of rank-1=100.00%.

On the invariance of EEG-based signatures of individuality with application in biometric identification.

One of the main challenges in EEG-based biometric systems is to extract reliable signatures of individuality from recorded EEG data that are also invariant against time. In this paper, we investigate the invariability of features that are extracted based on the spatial distribution of the spectral power of EEG data corresponding to 2-second eyes-closed resting-state (ECRS) recording, in different scenarios. Eyes-closed resting-state EEG signals in 4 healthy adults are recorded in two different sessions with an interval of at least one week between sessions. The performance in terms of correct recognition rate (CRR) is examined when the training and testing datasets are chosen from the same recording session, and when the training and testing datasets are chosen from different sessions. It is shown that an CRR of 92% can be achieved based on the proposed features when the training and testing datasets are taken from different sessions. To reduce the number of recording channels, principal component analysis (PCA) is also employed to identify channels that carry the most discriminatory information across individuals. High CRR is obtained based on the data from channels mostly covering the occipital region. The results suggest that features based on the spatial distribution of the spectral power of the short-time (e.g. 2 seconds) ECRS recordings can have great potentials in EEG-based biometric identification systems.

Facial Expression Recognition using Patch based Gabor Features

Facial Expression is one of the most natural, and powerful means for human beings to Communicate their emotions and intentions. The recognition of facial expressions is very important for interactive Human Computer Interfaces. One crucial step for facial expression recognition (FER) is the accurate extraction of emotional features. Numerous feature extraction techniques have been developed for recognition of expressions from static images as well as videos. This paper put forward an approach using distance features that are obtained by extracting patch based 3D Gabor features and conducting patch matching operations. The experimental results shows high correct recognition rate (CRR), fast processing time and significant performance improvements because of the consideration of facial components and muscle movements. Comparison with the state -of -the art indicates that the proposed approach achieves high CRR for JAFFE database and is one among the top performers on the CohnKanade (CK) database. General Terms Facial expression recognition, Human Computer Interfaces, Correct recognition Rate, Computer Vision.

Sasang Constitution Classification System by Morphological Feature Extraction of Facial Images

This study proposed a Sasang constitution classification system that can increase the objectivity and reliability of Sasang constitution diagnosis using the image of frontal face, in order to solve problems in the subjective classification of Sasang constitution based on Sasang constitution specialists’ experiences. For classification, characteristics indicating the shapes of the eyes, nose, mouth and chin were defined, and such characteristics were extracted using the morphological statistic analysis of face images. Then, Sasang constitution was classified through a SVM (Support Vector Machine) classifier using the extracted characteristics as its input, and according to the results of experiment, the proposed system showed a correct recognition rate of 93.33%. Different from existing systems that designate characteristic points directly, this system showed a high correct recognition rate and therefore it is expected to be useful as a more objective Sasang constitution classification system.

Pedestrian Classification Based on Full-SVM Decision Tree

Visual analysis has potential to be used for recognition, and it is one of the hottest but most difficult subjects in computer vision. In order to identify pedestrian movement in an Intelligent Security Monitoring System, the video activity in the prospect is represented by a series of spatio-temporal interest points. Since human posture has the characteristics of uncertainty and illegibility, the clustering centers of each class are computed by fuzzy clustering techniques. We presented a pedestrian classification method based on improved support vector machines in order to solve non-rigid objects that are difficult to identify in intelligent monitoring systems. The Support Vector Machine technology and the decision tree have combined into one multi-class classifier so as to solve multi-class classification problems. Then a full-SVM (Support Vector Machine) decision tree is constructed based on the conventional decision tree. At last, the method is evaluated on the KTH action dataset and receives a comparatively high correct recognition rate.