scholarly journals Human Recognition Using Joint Coordinate Images (JCIs) with Convolutional Neural Network

2021 ◽  
Vol 2095 (1) ◽  
pp. 012056
Author(s):  
Deyu Kong ◽  
Xuejun Zhang ◽  
Yini Wei ◽  
Xianfu Xu ◽  
Hongjie Zeng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Color Image ◽  
Recognition Task ◽  
Skeletal Structure ◽  
Neural Network Classifier ◽  
Recognition Method ◽  
Human Recognition ◽  
Complementary Method

Abstract Human recognition with skeletal data has the advantage in detecting people without their face characteristic on image. However, the accuracy of recognition by this method is always low because it relies deeply on manual feature selection. We propose a novel human recognition method called Joint Coordinate Images (JCIs) with Convolutional Neural Network (CNN) based on the image generated from skeletal information tracked by KinectV1. In order to represent human physical skeletal characteristic, the coordinate values XYZ of human joint tracked by KinectV1 are firstly created in color image called Joint Coordinate Images (JCIs), in which the relative position of the pixels represents the skeletal structure characteristics of participants with shape in “大” structure. Secondly, a new convolution neural network classifier Lenet-5 model, which always performed well in image classification, was modified to be able to input our JCIs for human recognition. The experimental results show that human recognition using joint coordinate image and Lenet-5 network can reach the highest recognition accuracy of 90.00% on the G3D dataset, which demonstrates the feasibility to transform the skeletal coordinate information into color image for human recognition task and could be used as a complementary method to the well-known application of face recognition.

scholarly journals A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1999
Author(s):  
Sadiq H. Abdulhussain ◽  
Basheera M. Mahmmod ◽  
Marwah Abdulrazzaq Naser ◽  
Muntadher Qasim Alsabah ◽  
Roslizah Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Orthogonal Polynomials ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
The State ◽  
Support Vector ◽  
Recognition Method ◽  
Handwritten Numeral Recognition ◽  
Numeral Recognition

Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.

scholarly journals Underwater target recognition method based on convolution residual network

2019 ◽  
Vol 283 ◽  
pp. 04011
Author(s):  
Yuechao Chen ◽  
Shuanping Du ◽  
HengHeng Quan ◽  
Bin Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Deep Structure ◽  
Target Recognition ◽  
Complex Signal ◽  
Recognition Method ◽  
Residual Network ◽  
Time Frequency ◽  
Underwater Target

The underwater target radiated noises usually have characteristics of low signal to noise ratio, complex signal components and so on. Therefore the recognition is a difficult task and powerful recognition method must be applied to obtain good results. In this paper, a recognition method for underwater target radiated noise time-frequency image based on convolutional neural network with residual units is proposed. The principles and characteristics of the convolutional residual network are analyzed and three basic convolutional residual units are put forward. Then three convolutional residual network models with very deep structure are established based on basic convolutional residual units and some normal convolution layers. The number of the hidden layers is 50, 100 and 150 respectively and softmax algorithm is used as the top classifier. The wavelet transform is adopted to generate time-frequency images of the underwater target radiated noises with frequency band of 10~200Hz, thus ensuring the accuracy of local structure of the image, then the above three models can be used to recognize the images. The experimental data of two types of targets were processed. The results are as follows. As the number of training time increases, the training loss shows a convergence trend and the recognition accuracy of test data gradually increases to more than 90%. In addition, the top-level output has obvious separability. The final recognition accuracies of the three convolutional residual networks are all over 93% and higher than that of normal convolutional neural network with 5 layers. As the number of layers increases, the recognition accuracy of the convolutional residual network increases to a certain extent, illustrating the increase of layer number can improve the processing effect. The analysis results show that the convolution residual network can extract features with separability through deep structure and achieve effective underwater target recognition.

scholarly journals Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

2021 ◽  
Vol 11 (11) ◽  
pp. 5235
Author(s):  
Nikita Andriyanov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Inference ◽  
Recognition Accuracy ◽  
Network Architectures ◽  
Reduced Dimensions ◽  
Different Types ◽  
Rectangular Area ◽  
Noise Impulse ◽  
Selection Of

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions.

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

2022 ◽  
Author(s):  
Canyi Du ◽  
Rui Zhong ◽  
Yishen Zhuo ◽  
Xinyu Zhang ◽  
Feifei Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Vibration Signals ◽  
One Dimensional ◽  
Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

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