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 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

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 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

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

scholarly journals See clearly on rainy days: Hybrid multiscale loss guided multi-feature fusion network for single image rain removal

2021 ◽  
Author(s):  
Huiyuan Fu ◽  
Yu Zhang ◽  
Huadong Ma
Keyword(s):  
Feature Fusion ◽  
Heavy Rain ◽  
Single Image ◽  
Multiple Feature ◽  
Rain Removal ◽  
Visual Tasks ◽  
Multiple Feature Fusion ◽  
Real World Datasets ◽  
Content Information

AbstractThe quality of photos is highly susceptible to severe weather such as heavy rain; it can also degrade the performance of various visual tasks like object detection. Rain removal is a challenging problem because rain streaks have different appearances even in one image. Regions where rain accumulates appear foggy or misty, while rain streaks can be clearly seen in areas where rain is less heavy. We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network (MSGMFFNet). Specially, to deal with rain streaks, our method generates a rain streak attention map, while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation. Using these tools, the model can restore a result with abundant details. Furthermore, a hybrid multiscale loss combining L1 loss and edge loss is used to guide the training process to pay attention to edge and content information. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.

Trust in the Police: Cross-country Comparisons

2012 ◽  
pp. 24-47
Author(s):  
V. Gimpelson ◽  
G. Monusova
Keyword(s):  
Public Opinion ◽  
Public Attitudes ◽  
Crime Rates ◽  
Authoritarian Regimes ◽  
Data Sets ◽  
The Public ◽  
Positive Attitudes ◽  
Cross Country ◽  
Police Activity

Using different cross-country data sets and simple econometric techniques we study public attitudes towards the police. More positive attitudes are more likely to emerge in the countries that have better functioning democratic institutions, less prone to corruption but enjoy more transparent and accountable police activity. This has a stronger impact on the public opinion (trust and attitudes) than objective crime rates or density of policemen. Citizens tend to trust more in those (policemen) with whom they share common values and can have some control over. The latter is a function of democracy. In authoritarian countries — “police states” — this tendency may not work directly. When we move from semi-authoritarian countries to openly authoritarian ones the trust in the police measured by surveys can also rise. As a result, the trust appears to be U-shaped along the quality of government axis. This phenomenon can be explained with two simple facts. First, publicly spread information concerning police activity in authoritarian countries is strongly controlled; second, the police itself is better controlled by authoritarian regimes which are afraid of dangerous (for them) erosion of this institution.

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

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