scholarly journals Recognition of Fetal Facial Ultrasound Standard Plane Based on Texture Feature Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaoli Wang ◽  
Zhonghua Liu ◽  
Yongzhao Du ◽  
Yong Diao ◽  
Peizhong Liu ◽  
...  
Keyword(s):  
Cleft Lip ◽  
Cleft Lip And Palate ◽  
Feature Fusion ◽  
Texture Feature ◽  
Recall Rate ◽  
Support Vector ◽  
Ultrasound Images ◽  
Fusion Method ◽  
Accurate Identification ◽  
Data Set

In the process of prenatal ultrasound diagnosis, accurate identification of fetal facial ultrasound standard plane (FFUSP) is essential for accurate facial deformity detection and disease screening, such as cleft lip and palate detection and Down syndrome screening check. However, the traditional method of obtaining standard planes is manual screening by doctors. Due to different levels of doctors, this method often leads to large errors in the results. Therefore, in this study, we propose a texture feature fusion method (LH-SVM) for automatic recognition and classification of FFUSP. First, extract image’s texture features, including Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG), then perform feature fusion, and finally adopt Support Vector Machine (SVM) for predictive classification. In our study, we used fetal facial ultrasound images from 20 to 24 weeks of gestation as experimental data for a total of 943 standard plane images (221 ocular axial planes, 298 median sagittal planes, 424 nasolabial coronal planes, and 350 nonstandard planes, OAP, MSP, NCP, N-SP). Based on this data set, we performed five-fold cross-validation. The final test results show that the accuracy rate of the proposed method for FFUSP classification is 94.67%, the average precision rate is 94.27%, the average recall rate is 93.88%, and the average F 1 score is 94.08%. The experimental results indicate that the texture feature fusion method can effectively predict and classify FFUSP, which provides an essential basis for clinical research on the automatic detection method of FFUSP.

scholarly journals Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 517
Author(s):  
Xinting Li ◽  
Weijin Cheng ◽  
Chengsheng Yuan ◽  
Wei Gu ◽  
Baochen Yang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Feature Fusion ◽  
Texture Feature ◽  
Fusion Rule ◽  
Support Vector ◽  
Svm Classifier ◽  
Fine Grained ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Intelligent Devices

Currently, intelligent devices with fingerprint identification are widely deployed in our daily life. However, they are vulnerable to attack by fake fingerprints made of special materials. To elevate the security of these intelligent devices, many fingerprint liveness detection (FLD) algorithms have been explored. In this paper, we propose a novel detection structure to discriminate genuine or fake fingerprints. First, to describe the subtle differences between them and take advantage of texture descriptors, three types of different fine-grained texture feature extraction algorithms are used. Next, we develop a feature fusion rule, including five operations, to better integrate the above features. Finally, those fused features are fed into a support vector machine (SVM) classifier for subsequent classification. Data analysis on three standard fingerprint datasets indicates that the performance of our method outperforms other FLD methods proposed in recent literature. Moreover, data analysis results of blind materials are also reported.

Mine Loadometer License Plate Recognition Based on MFF-DS

2013 ◽  
Vol 712-715 ◽  
pp. 2341-2344 ◽  
Author(s):  
Xiu Cai Guo ◽  
Shi Qian Zhang
Keyword(s):  
Feature Fusion ◽  
Recognition Rate ◽  
Evidence Theory ◽  
Support Vector ◽  
License Plate ◽  
Final Decision ◽  
Fusion Method ◽  
License Plate Recognition ◽  
Single Feature ◽  
Basic Probability

The result of license plate recognition with a single feature is unsatisfactory. A multi-feature fusion method based on D-S evidence theory is proposed to improve results of mine loadometer license plate recognition. Firstly, three kinds of features including contour, projection and trellis-coded are extracted from the vehicle plate character image. Then the Basic Probability Assignment (BPA) is defined to get the credibility of recognition results by using the multi-class Support Vector Machine (SVM) with one-against-one method. Finally, D-S evidence theory is employed to integrate the credibility of evidences for making a final decision. The experimental results show that the multi-feature fusion method has higher recognition rate, fault tolerance and robustness.

A New Feature Fusion Method for Handwritten Character Recognition Based on 3D Accelerometer

2010 ◽  
Vol 44-47 ◽  
pp. 1583-1587 ◽  
Author(s):  
Zhen Yu He
Keyword(s):  
Character Recognition ◽  
Feature Fusion ◽  
Consumer Electronics ◽  
Support Vector ◽  
Accelerometer Data ◽  
Fusion Method ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
New Feature ◽  
Fusion Feature

In this paper, a new feature fusion method for Handwritten Character Recognition based on single tri-axis accelerometer has been proposed. The process can be explained as follows: firstly, the short-time energy (STE) features are extracted from accelerometer data. Secondly, the Frequency-domain feature namely Fast Fourier transform Coefficient (FFT) are also extracted. Finally, these two categories features are fused together and the principal component analysis (PCA) is employed to reduce the dimension of the fusion feature. Recognition of the gestures is performed with Multi-class Support Vector Machine. The average recognition results of ten Arabic numerals using the proposed fusion feature are 84.6%, which are better than only using STE or FFT feature. The performance of experimental results show that gesture-based interaction can be used as a novel human computer interaction for consumer electronics and mobile device.

scholarly journals A Shallow–Deep Feature Fusion Method for Pedestrian Detection

2021 ◽  
Vol 11 (19) ◽  
pp. 9202
Author(s):  
Daxue Liu ◽  
Kai Zang ◽  
Jifeng Shen
Keyword(s):  
Detection Method ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Recall Rate ◽  
Fusion Method ◽  
Discriminative Power ◽  
Deep Feature ◽  
Proposal A ◽  
High Recall Rate ◽  
Feature Based

In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection.

scholarly journals Analysis of Feature Fusion Based on HIK SVM and Its Application for Pedestrian Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Song-Zhi Su ◽  
Shu-Yuan Chen
Keyword(s):  
Near Infrared ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Visual Images ◽  
Support Vector ◽  
Fusion Method ◽  
Low Resolution ◽  
Night Time ◽  
Histogram Of Oriented Gradient ◽  
Histogram Intersection

This work presents the fusion of integral channel features to improve the effectiveness and efficiency of pedestrian detection. The proposed method combines the histogram of oriented gradient (HOG) and local binary pattern (LBP) features by a concatenated fusion method. Although neural network (NN) is an efficient tool for classification, the time complexity is heavy. Hence, we choose support vector machine (SVM) with the histogram intersection kernel (HIK) as a classifier. On the other hand, although many datasets have been collected for pedestrian detection, few are designed to detect pedestrians in low-resolution visual images and at night time. This work collects two new pedestrian datasets—one for low-resolution visual images and one for near-infrared images—to evaluate detection performance on various image types and at different times. The proposed fusion method uses only images from the INRIA dataset for training but works on the two newly collected datasets, thereby avoiding the training overhead for cross-datasets. The experimental results verify that the proposed method has high detection accuracies even in the variations of image types and time slots.

scholarly journals A Novel Multi-Feature Fusion Method in Merging Information of Heterogenous-View Data for Oil Painting Image Feature Extraction and Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Tong Chen ◽  
Juan Yang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
Image Feature ◽  
Support Vector ◽  
Fusion Method ◽  
Feature Classification ◽  
Image Feature Extraction ◽  
Oil Painting ◽  
The Way

The art of oil painting reflects on society in the form of vision, while technology constantly explores and provides powerful possibilities to transform the society, which also includes the revolution in the way of art creation and even the way of thinking. The progress of science and technology often provides great changes for the creation of art, and also often changes people's way of appreciation and ideas. The oil painting image feature extraction and recognition is an important field in computer vision, which is widely used in video surveillance, human-computer interaction, sign language recognition and medical, health care. In the past few decades, feature extraction and recognition have focused on the multi-feature fusion method. However, the captured oil painting image is sensitive to light changes and background noise, which limits the robustness of feature extraction and recognition. Oil painting feature extraction is the basis of feature classification. Feature classification based on a single feature is easily affected by the inaccurate detection accuracy of the object area, object angle, scale change, noise interference and other factors, resulting in the reduction of classification accuracy. Therefore, we propose a novel multi-feature fusion method in merging information of heterogenous-view data for oil painting image feature extraction and recognition in this paper. It fuses the width-to-height ratio feature, rotation invariant uniform local binary mode feature and SIFT feature. Meanwhile, we adopt a modified faster RCNN to extract the semantic feature of oil painting. Then the feature is classified based on the support vector machine and K-nearest neighbor method. The experiment results show that the feature extraction method based on multi-feature fusion can significantly improve the average classification accuracy of oil painting and have high recognition efficiency.

scholarly journals Electronic Systems Diagnosis Fault in Gasoline Engines Based on Multi-Information Fusion

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2917 ◽  
Author(s):  
Jie Hu ◽  
Tengfei Huang ◽  
Jiaopeng Zhou ◽  
Jiawei Zeng
Keyword(s):  
Data Fusion ◽  
Information Fusion ◽  
Feature Fusion ◽  
Rapid Development ◽  
Back Propagation ◽  
Evidence Theory ◽  
Decision Fusion ◽  
Support Vector ◽  
Diagnostic Model ◽  
Fusion Method

The rapid development of electronic techniques in automobile has led to an increase of potential safety hazards, thus, a strong on-board diagnostic (OBD) system is desperately needed. To solve the problem of OBD insensitivity to manufacture errors or aging faults, the paper proposes a novel multi information fusion method. The diagnostic model is composed of a data fusion layer, feature fusion layer, and decision fusion layer. They are based on the back propagation (BP) neural network, support vector machine (SVM), and evidence theory, respectively. Algorithms are mainly focused on the reliability allocation of diagnostic results, which come from the data fusion layer and feature fusion layer. A fault simulator system was developed to simulate bias and drift faults of the intake pressure sensor. The real vehicle experiment was carried out to acquire data that are used to verify the availability of the method. Diagnostic results show that the multi-information fusion method improves diagnostic accuracy and reliability effectively. The study will be a promising approach for the diagnosis bias and drift fault of sensors in electronic control systems.

Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model

2018 ◽  
Vol 40 (6) ◽  
pp. 357-379 ◽  
Author(s):  
Puja Bharti ◽  
Deepti Mittal ◽  
Rupa Ananthasivan
Keyword(s):  
Feature Fusion ◽  
Texture Features ◽  
Chronic Liver ◽  
Support Vector ◽  
Ultrasound Images ◽  
Ensemble Model ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Difference Matrix ◽  
Ensemble Strategy

Chronic liver diseases are fifth leading cause of fatality in developing countries. Their early diagnosis is extremely important for timely treatment and salvage life. To examine abnormalities of liver, ultrasound imaging is the most frequently used modality. However, the visual differentiation between chronic liver and cirrhosis, and presence of heptocellular carcinomas (HCC) evolved over cirrhotic liver is difficult, as they appear almost similar in ultrasound images. In this paper, to deal with this difficult visualization problem, a method has been developed for classifying four liver stages, that is, normal, chronic, cirrhosis, and HCC evolved over cirrhosis. The method is formulated with selected set of “handcrafted” texture features obtained after hierarchal feature fusion. These multiresolution and higher order features, which are able to characterize echotexture and roughness of liver surface, are extracted by using ranklet, gray-level difference matrix and gray-level co-occurrence matrix methods. Thereafter, these features are applied on proposed ensemble classifier that is designed with voting algorithm in conjunction with three classifiers, namely, k–nearest neighbor (k-NN), support vector machine (SVM), and rotation forest. The experiments are conducted to evaluate the (a) effectiveness of “handcrafted” texture features, (b) performance of proposed ensemble model, (c) effectiveness of proposed ensemble strategy, (d) performance of different classifiers, and (e) performance of proposed ensemble model based on Convolutional Neural Networks (CNN) features to differentiate four liver stages. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 96.6% is obtained by use of proposed classifier model.

Ovarian Tumor Texture Classification Based on Sparse Auto-Encoder Network Combined with Multi-Feature Fusion and Random Forest in Ultrasound Image

2021 ◽  
Vol 11 (2) ◽  
pp. 424-431
Author(s):  
Yingxin Wang ◽  
Qianqian Zeng
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Texture Classification ◽  
Ultrasound Image ◽  
Texture Features ◽  
Recall Rate ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Feature Extraction Method

Texture analysis has always been active areas of ultrasound image processing research. Using texture features to classify the ultrasound images is the focus of researchers' attention. How to extract representative texture features is an important part of successful texture description. The research goal of this paper is to apply the deep neural network into the ultrasound classification of ovarian tumors, and design a novel type of ovarian cancer diagnosis system. The improved HOG feature extraction method and the gray-level concurrence matrix of LBP image are firstly adopted to extract low-level features; Then, these features are cascaded into a new feature vector, and are input into the auto-encoder neural network to learn the high-level feature. Finally, the SVM classifier is used to achieve the classification of ovarian lesion. A large number of qualitative and quantitative experiments show that the improved method has more performance than the comparisons algorithms for ovarian ultrasound lesion, and it can significantly improve the classification performance while ensuring the accuracy rate and recall rate.

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