Virtual Metrology of Visualizing Copper Microstructure Featured with Computer Vision and Artificial Neural Network

2021 ◽  
Author(s):  
Lingyen Yeh ◽  
Rencheng Chen
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Virtual Metrology ◽  
Artificial Neural

scholarly journals RANCANG BANGUN APLIKASI PENGENALAN WAJAH UNTUK PASSWORD FILE DENGAN METODE JARINGAN SYARAF TIRUAN

2019 ◽  
Vol 21 (1) ◽  
pp. 11-21
Author(s):  
Genrawan Hoendarto ◽  
Vicni Iskandar
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Data Security ◽  
Facial Recognition ◽  
Back Propagation ◽  
Computer Users ◽  
Artificial Neural ◽  
Access Rights ◽  
Facial Images

Data security for computer users is increasingly becoming a concern because it is increasingly vulnerable to illegal access even though the file has been protected with a password. This is possible with the increasing number of applications aimed at hacking owner protection. Artificial neural network that was appointed in this study is one part of computer vision, which in this study is intended to make computers able to "see" through a webcam and recognize that face has access rights to the selected file. So that computers can distinguish facial images, it needs to be trained by applying the back propagation method. The reason for choosing facial recognition is because each person has a different face, so that it can be a more effective security key than conventional methods of making or accessing files that are on a computer.

scholarly journals A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 113 ◽  
Author(s):  
Razieh Pourdarbani ◽  
Sajad Sabzi ◽  
Davood Kalantari ◽  
José Luis Hernández-Hernández ◽  
Juan Ignacio Arribas
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Vision System ◽  
Color Space ◽  
Majority Voting ◽  
Hsi Color Space ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

scholarly journals Sistem Identifikasi Tingkat Kematangan Buah Nanas Secara Non-Destruktif Berbasis Computer Vision

2021 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Nevalen Aginda Prasetyo ◽  
◽  
Arif Surtono ◽  
Junaidi Junaidi ◽  
Gurum Ahmad Pauzi
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Pearson Correlation ◽  
Statistical Parameter ◽  
Identification System ◽  
Maturity Level ◽  
Validation Data ◽  
Artificial Neural ◽  
Level Identification

A computer vision-based non-destructive pineapple maturity level identification system has been realized. This research was conducted to create a system capable of identifying six indexes of pineapple maturity level. An artificial neural network is used as a classifier for the level of maturity pineapples. Artificial neural network input is a statistical parameter consisting of mean, standard deviation, variance, kurtosis, and skewness of RGB and HSV color models pineapple images. Statistical parameters of the color model with a Pearson correlation value greater than 0.5 were used to characterize pineapple images. A total of 360 pineapple images were used in the training process with a percentage of 75% of training data and 25% of validation data. An image segmentation process is applied to separate the pineapple image from the image background. The result of this research is a pineapple maturity level identification system consisting of software and hardware which is able to identify six indexes of pineapple maturity level with average accuracy value of 98,4%.

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