Comparative Analysis Of Efficient Image Segmentation Technique For Text Recognition And Human Skin Recognition

Computer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in realtime from the camera device. Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book. Meanwhile, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98% Key word: Computer Vision, Segmentation, Object Recognition, Text Recognition, Skin Color Detection, Motion Detection, Disability Application

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An Innovative Face Detection Based on YCgCr Color Space

10.31227/osf.io/x3syk ◽

2018 ◽

Author(s):

Solly Aryza

Keyword(s):

Face Detection ◽

Skin Color ◽

Color Image ◽

Color Space ◽

Gaussian Model ◽

Detection Rates ◽

Detection Algorithms ◽

Lighting Conditions ◽

Wide Range ◽

Human Faces

It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. The research on detecting human faces in color image and in video sequence has been attracted with more and more people. In this paper, we propose a novel face detection method that achieves better detection rates. The new face detection algorithms based on skin color model in YCgCr chrominance space. Firstly, we build a skin Gaussian model in Cg-Cr color space. Secondly, a calculation of correlation coefficient is performed between the given template and the candidates. Experimental results demonstrate that our system has achieved high detection rates and low false positives over a wide range of facial variations in color, position and varying lighting conditions.

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A Computer Vision System for the Categorization of Citrus Fruits Using Convolutional Neural Network

2021 International Symposium on Electronics and Smart Devices (ISESD) ◽

10.1109/isesd53023.2021.9501790 ◽

2021 ◽

Author(s):

Md. Mehedi Hasan ◽

Imrus Salehin ◽

Nazmun Nessa Moon ◽

T. M. Kamruzzaman ◽

Baki-Ul-Islam ◽

...

Keyword(s):

Neural Network ◽

Computer Vision ◽

Convolutional Neural Network ◽

Vision System ◽

Citrus Fruits ◽

Computer Vision System

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Convolutional Neural Network Model in Machine Learning Methods and Computer Vision for Image Recognition: A Review

Journal of Applied Sciences Research ◽

10.22587/jasr.2018.14.6.5 ◽

2018 ◽

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Convolutional Neural Network ◽

Network Model ◽

Image Recognition ◽

Neural Network Model ◽

Learning Methods ◽

Machine Learning Methods

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AlexNet convolutional neural network to classify the types of Indonesian coffee beans

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/905/1/012059 ◽

2021 ◽

Vol 905 (1) ◽

pp. 012059

Author(s):

Y Hendrawan ◽

B Rohmatulloh ◽

F I Ilmi ◽

M R Fauzy ◽

R Damayanti ◽

...

Keyword(s):

Neural Network ◽

Computer Vision ◽

Sensitivity Analysis ◽

Convolutional Neural Network ◽

Confusion Matrix ◽

Learning Rate ◽

Agricultural Products ◽

Coffee Bean ◽

Coffee Beans ◽

Non Destructive

Abstract Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.

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Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

10.36227/techrxiv.17072852 ◽

2021 ◽

Author(s):

Yuki Shimizu ◽

Shigeo Morimoto ◽

Masayuki Sanada ◽

Yukinori Inoue

Keyword(s):

Neural Network ◽

Prediction Model ◽

Convolutional Neural Network ◽

Permanent Magnet ◽

Optimization Design ◽

Design System ◽

Automatic Design ◽

Wide Range ◽

Interior Permanent Magnet ◽

Long Time

The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

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