scholarly journals Hand-drawn sketch recognition with a double-channel convolutional neural network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Input Image ◽  
Contour Extraction ◽  
Sketch Recognition ◽  
Softmax Classifier ◽  
Dual Channel ◽  
Extraction Algorithm

AbstractIn hand-drawn sketch recognition, the traditional deep learning method has the problems of insufficient feature extraction and low recognition rate. To solve this problem, a new algorithm based on a dual-channel convolutional neural network is proposed. Firstly, the sketch is preprocessed to get a smooth sketch. The contour of the sketch is obtained by the contour extraction algorithm. Then, the sketch and contour are used as the input image of CNN. Finally, feature fusion is carried out in the full connection layer, and the classification results are obtained by using a softmax classifier. Experimental results show that this method can effectively improve the recognition rate of a hand-drawn sketch.

scholarly journals Hand-Drawn Sketch Recognition With Double-Channel CNN

2021 ◽  
Author(s):  
Lei ZHANG
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Contour Extraction ◽  
Sketch Recognition ◽  
Softmax Classifier ◽  
Extraction Algorithm ◽  
Double Channel ◽  
Novel Algorithm

Abstract In the task of hand-drawn sketch recognition, traditional deep learning methods have the insufficient of feature extraction and low recognition rate. To improve the insufficient, a novel algorithm based on double channel convolution neural network is proposed. First of all, the hand-drawn sketch is preprocessed to get a smooth sketch. And the contour extraction algorithm is adopted to get the contour of the sketch. The sketch and its contour are then used as input images of the CNN respectively. Finally, through performing feature fusion at the full connection layer, the classification results are obtained using the softmax classifier. The experimental results show that the proposed method can effectively improve the recognition rate of hand-drawn sketch.

scholarly journals Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6685
Author(s):  
Pu Yanan ◽  
Yan Jilong ◽  
Zhang Heng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Sensors ◽  
Inertial Sensors ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Table Tennis ◽  
User Privacy ◽  
Tennis Players ◽  
Wearable Inertial Sensors

Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.

Flower growth status recognition method based on feature fusion convolutional neural network

2021 ◽  
pp. 1-12
Author(s):  
Haiming Liu ◽  
Shixuan Guan ◽  
Weizhong Lu ◽  
Haiou Li ◽  
Hongjie Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Input Data ◽  
Network Performance ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Growth Environment ◽  
Growth Status ◽  
Current Growth ◽  
Flower Growth

The growth state of flowers is affected by many factors such as temperature, humidity, and light. Therefore, the maintenance of flowers often requires more professional knowledge. Ordinary people are often at a loss when face with various flower representations and do not know where the problem is. In response to the above problems, this article proposes the use of deep learning to identify the growth status of flowers to assist people in successfully raising flowers. In this article, we propose that the mainstream convolutional neural network has the limitation of only inputting images. In terms of network input, data of the current growth environment of flowers will also be input to supplement the input data of the network. In view of the lack of information interaction in the network, in terms of network structure, the shallow and deep characteristics of the network are integrated to make the network performance more advantageous. Experiments show that this method can effectively improve the recognition rate of flower growth status, so as to correctly distinguish the current growth status of flowers.

scholarly journals Iris Recognition using Convolutional Neural Network Design

2019 ◽  
Vol 8 (9S) ◽  
pp. 672-678
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Iris Recognition ◽  
Recognition Rate ◽  
Optimization Techniques ◽  
Backpropagation Algorithm ◽  
Generalization Ability ◽  
Softmax Classifier ◽  
Random Patterns

Iris trait has gained the attention of many researchers recently as it consists of unique and highly random patterns. Many methods have been proposed for feature extraction and classification for iris trait but suffer from poor generalization ability. In this paper, a scratch convolutional neural network is designed in order to extract the iris features and softmax classifier is used for multiclass classification. The various optimization techniques with backpropagation algorithm are used for weight updating. The results show that the Convolutional Neural Network based feature extraction has proven to provide good generalization ability with improved recognition rate. The effect of various optimization techniques for generalization ability is also observed. The method is tested on IITD and CASIA-Iris-V3 database. The recognition rates obtained are comparable with state of art methods.

scholarly journals ECT Image Reconstruction Algorithm Based on Multiscale Dual-Channel Convolutional Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Lili Wang ◽  
Xiao Liu ◽  
Deyun Chen ◽  
Hailu Yang ◽  
Chengdong Wang
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Convolutional Neural Network ◽  
Middle Layer ◽  
Input Image ◽  
Local Optimum ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Feature Maps ◽  
Dual Channel

For the problems of missing edges and obvious artifacts in Electrical Capacitance Tomography (ECT) reconstruction algorithms, an image reconstruction method based on a multiscale dual-channel convolutional neural network is proposed. Firstly, the image reconstructed by Landweber algorithm is input into the convolutional neural network, and four scales are selected for feature extraction. Feature unions are used across the scales to fuse the information of the output layer with feature maps. To improve the imaging accuracy, two frequency channels are designed for the input image. The middle layer of the network consists of two fully convolutional structures. Convolutional layers and jump connections are designed separately for different channels, which greatly improves the network’s ability to extract feature information and reduces the number of feature maps required for each layer. The number of network layers is shallow, which can speed up the network training, prevent the network from falling into local optimum, and ensure the effective transmission of image details. Simulation experiments are carried out for four typical dual media distributions. The edges of the reconstructed image are smoother and the image error is smaller. It effectively resolves the lack of edges in the reconstruction image and reduces the image edge artifacts in the ECT system.

scholarly journals Dual-channel convolutional neural network for power edge image recognition

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fangrong Zhou ◽  
Yi Ma ◽  
Bo Wang ◽  
Gang Lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Classification Method ◽  
Power Equipment ◽  
Recognition Method ◽  
Edge Image ◽  
Dual Channel

AbstractIn view of the low accuracy and poor processing capacity of traditional power equipment image recognition methods, this paper proposes a power equipment image recognition method based on a dual-channel convolutional neural network (DC-CNN) model and random forest (RF) classification. In the aspect of feature extraction, the DC-CNN model extracts the characteristics of power equipment through two independent CNN models. In the aspect of the recognition algorithm, by referring to the advantages of the traditional machine learning method and incorporating the advantages of the RF, an RF classification method incorporating deep learning is proposed. Finally, the proposed DC-CNN model and RF classification method are used to classify images of various types of power equipment. The results show that the proposed methods can be effectively applied to the image recognition of various types of power equipment, and they greatly improve the recognition rate of power equipment images.

Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

2020 ◽  
Vol 65 (6) ◽  
pp. 759-773
Author(s):  
Segu Praveena ◽  
Sohan Pal Singh
Keyword(s):  
Neural Network ◽  
Acute Lymphoblastic Leukaemia ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Lymphoblastic Leukaemia ◽  
Input Image ◽  
Deep Convolutional Neural Network ◽  
Grey Wolf ◽  
Deep Cnn

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

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