scholarly journals Improved ORB Algorithm Using Three-Patch Method and Local Gray Difference

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 975
Author(s):  
Chaoqun Ma ◽  
Xiaoguang Hu ◽  
Jin Xiao ◽  
Huanchao Du ◽  
Guofeng Zhang
Keyword(s):  
Feature Fusion ◽  
Comparison Method ◽  
The Other ◽  
Binary String ◽  
Fusion Method ◽  
Feature Description ◽  
Binary Description ◽  
New Feature ◽  
Patch Group ◽  
Binary Strings

This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray difference (ORB-TPLGD). ORB takes a breakthrough in real-time aspect. However, subtle changes of the image may greatly affect its final binary description. In this paper, the feature description generation is focused. On one hand, instead of pixel patch pairs comparison method used in present ORB algorithm, a three-pixel patch group comparison method is adopted to generate the binary string. In each group, the gray value of the main patch is compared with that of the other two companion patches to determine the corresponding bit of the binary description. On the other hand, the present ORB algorithm simply uses the gray size comparison between pixel patch pairs, while ignoring the information of the gray difference value. In this paper, another binary string based on the gray difference information mentioned above is generated. Finally, the feature fusion method is adopted to combine the binary strings generated in the above two steps to generate a new feature description. Experiment results indicate that our improved ORB algorithm can achieve greater performance than ORB and some other related algorithms.

A new Feature-Fusion method based on training dataset prototype for surface defect recognition

2021 ◽  
Vol 50 ◽  
pp. 101392
Author(s):  
Yucheng Wang ◽  
Xinyu Li ◽  
Yiping Gao ◽  
Lijian Wang ◽  
Liang Gao
Keyword(s):  
Surface Defect ◽  
Feature Fusion ◽  
Training Dataset ◽  
Fusion Method ◽  
New Feature ◽  
Defect Recognition

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.

Applying a new feature fusion method to classify breast lesions

2021 ◽  
Author(s):  
Abolfazl Zargari Khuzani ◽  
Najmeh Mashhadi ◽  
Morteza Heidari ◽  
Donya Khaledyan ◽  
Sam Teymoori
Keyword(s):  
Feature Fusion ◽  
Breast Lesions ◽  
Fusion Method ◽  
New Feature

scholarly journals Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition

2020 ◽  
Vol 10 (9) ◽  
pp. 3166 ◽  
Author(s):  
Cheng-Jian Lin ◽  
Cheng-Hsien Lin ◽  
Shiou-Yun Jeng
Keyword(s):  
Feature Fusion ◽  
Data Sets ◽  
Gender Recognition ◽  
Fusion Method ◽  
Single Image ◽  
Recognition Error ◽  
Average Accuracy ◽  
New Feature ◽  
Accuracy Rates

In recent years, convolutional neural networks (CNNs) have been successfully used in image recognition and image classification. General CNNs only use a single image as feature extraction. If the quality of the obtained image is not good, it is easy to cause misjudgment or recognition error. Therefore, this study proposes the feature fusion of a dual-input CNN for the application of face gender classification. In order to improve the traditional feature fusion method, this paper also proposes a new feature fusion method, called the weighting fusion method, which can effectively improve the overall accuracy. In addition, in order to avoid the parameters of the traditional CNN being determined by the user, this paper uses a uniform experimental design (UED) instead of the user to set the network parameters. The experimental results show that in the dual-input CNN experiment, average accuracy rates of 99.98% and 99.11% on the CIA and MORPH data sets are achieved, respectively, which is superior to the traditional feature fusion method.

A new feature fusion method at decision level and its application

2010 ◽  
Vol 6 (2) ◽  
pp. 129-132 ◽  
Author(s):  
Guang Han ◽  
Chun-xia Zhao ◽  
Hao-feng Zhang ◽  
Xia Yuan
Keyword(s):  
Feature Fusion ◽  
Fusion Method ◽  
Decision Level ◽  
New Feature
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