Integrating Digital Image Processing and Artificial Neural Network for Estimating Porosity from Thin Section

2013 ◽  
Author(s):  
Pongga Dikdya Wardaya ◽  
Hilfan Khairy ◽  
Chow Weng Sum
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Thin Section ◽  
Digital Image Processing ◽  
Digital Image ◽  
Artificial Neural

scholarly journals APLIKASI PENGENALAN PLAT NOMOR KENDARAAN DI UNIVERSITAS RIAU

2019 ◽  
Vol 9 (3) ◽  
pp. 47-52
Author(s):  
Noprizal ◽  
Feri Candra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Digital Image Processing ◽  
Digital Image ◽  
Training Data ◽  
License Plate ◽  
Backpropagation Method ◽  
Artificial Neural

Abstract Vehicle license plate recognition application has been found in shopping centers, university, and other agency buildings with various methods of recognition. Some examples of methods used such as digital image processing techniques, neural networks and so forth. This study makes an application for the introduction of license plates, especially for student vehicle license plates in the university area. This application is developed with Digital Image Processing Methods and Artificial Neural Networks. In this study, 900 training data are used, taken from 200 photo vehicle number plates, to train 36 characters that contain 26 alphabets and 10 decimal numbers. The training data is used to test 30 photos of vehicle license plates. Plate photos used as training and testing data are the Indonesian standard with black and white plates. Artificial Neural Network used to recognize vehicle license plate by using the Backpropagation method with parameters Epoch 1000, Hidden layer1 with node 60, Hidden layer2 with node 55, Goal 0.001. The final conclusion of this Study shows that the use of Artificial Neural Network Backpropagation method is very good, with the best testing accuracy obtained, namely 98% and 1.25 error. Keywords : digital image processing, artificial neural networks, vehicle license plate Abstrak Aplikasi pengenalan plat nomor kendaraan sudah banyak ditemukan di pusat perbelanjaan, universitas, dan gedung instansi dengan berbagai metode pengenalan. Beberapa contoh metode yang digunakan seperti teknik pengolahan citra digital, jaringan syaraf tiruan dan lain sebagainya. Disini penulis membuat sebuah aplikasi pengenalan plat nomor kendaraan khususnya untuk plat nomor kendaraan mahasiswa yang ada dilikungan Universitas Riau. Aplikasi ini dikembangkan dengan metode pengolahan citra digital dan jaringan syaraf tiruan. Pada penelitian ini, digunakan 700 data pelatihan yang diambil dari 200 foto plat nomor, untuk melatih 36 karakter. Data pelatihan tersebut kemudian digunakan untuk menguji 30 foto plat nomor kendaraan. Foto plat yang dijadikan untuk data pelatihan dan pengujian yaitu plat standar indonesia yang berwarna hitam dan putih. Jaringan syaraf tiruan yang digunakan untuk melakukan pengenalan yaitu dengan Metode Backpropagation dengan parameter Epoch 1000, Hidden layer1 dengan jumlah node 60, Hidden layer2 dengan jumlah node 55, Goal  0,001. Kesimpulan akhir dari penelitian ini yaitu menunjukan bahwa penggunaan Metode Backpropagation jaringan syaraf tiruan ini sangat bagus, dengan akurasi pengujian terbaik yang didapat yaitu 98% dengan eror 1,25. Kata kunci: pengolahan citra digital, jaringan syaraf tiruan, Backpropagation, plat nomor  

AUTOMATIC IDENTIFYING LARYNGOPHARYNGEAL REFLUX USING ARTIFICIAL NEURAL NETWORK

2012 ◽  
Vol 24 (01) ◽  
pp. 47-56 ◽  
Author(s):  
Sheng-Fuu Lin ◽  
Hsien-Tse Chen ◽  
Tung-Lung Tsai
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Gastric Acid ◽  
Digital Image Processing ◽  
Digital Image ◽  
Technical Assistance ◽  
Laryngopharyngeal Reflux ◽  
Precise Diagnosis ◽  
Artificial Neural

Laryngopharyngeal Reflux mostly leads to burns of the pharynx and larynx by reflux from gastric acid and also leads to different degrees of burns in the esophagus and the stomach. This paper aims to develop a technique for analyzing pharyngeal and laryngeal images. With techniques of digital image processing, this paper can choose the suitable images from burns of the pharynx and larynx to obtain the feature zones of burns of the pharynx and larynx. Artificial neural network helps physicians to develop the diagnostic standard about the burns severity of Laryngopharyngeal Reflux. This paper divides the types of the complications into three degrees and compares with other ways (Hanson et al.5 and Ilgner et al.6). The results can be the technical assistance in helping physicians to diagnose the severity of Laryngopharyngeal Reflux and to make a more precise diagnosis.

scholarly journals Gorontalo Medicinal Plants Image Identification System Using Artificial Neural Network with Back Propagation

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Mukhlisulfatih Latief ◽  
Rampi Yusuf
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Medicinal Plants ◽  
Digital Image Processing ◽  
Test Data ◽  
Digital Image ◽  
Back Propagation ◽  
Training Data ◽  
Artificial Neural

The purpose of this research is to design the application of digital image processing system to identify the image of medicinal plants of Gorontalo region using artificial neural network method using back propagation. This research used a digital image processing method with segmentation and extraction techniques. Segmentation process was carried out using thresholding method. Furthermore, a process of characteristic extraction from medicinal plants drawings was carried out using feature and color feature extractions to obtain the value of metric, eccentricity, hue, saturation and value. these five values were used as parameters for input neurons and one output neuron which denoted the class of the medicinal plants image. Data of this research consisted of 91 images which had been divided into two types, training data and test data. The training data consisted of 80 images and the test data consisted of eleven images. A network architecture was obtained from the training result and it provided the highest accuracy level (100%) and least number of iteration with a number of 50 neurons on hidden layer and 143 epochs. The testing result showed a lower accuracy of 54.54%.

Artificial Neural Network Assisted Digital Image Processing to Determine the Hydrophobicity of Polymeric Materials

2014 ◽  
Vol 775-776 ◽  
pp. 743-748
Author(s):  
Daniel Thomazini ◽  
Raphael Ulisses Costa De Resende de Resende ◽  
Daniel Henrique Gueratto ◽  
Maria Virginia Gelfuso
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Transmission Line ◽  
Digital Image Processing ◽  
Digital Image ◽  
Polymer Surface ◽  
Back Propagation ◽  
Polymeric Materials ◽  
Artificial Neural

Hydrophobicity of polymeric insulators of high power transmission line is an important property because it is a good monitor of aging of polymeric outdoor insulator. An employee on the high-voltage transmission line makes this procedure during operation, which can lead to injuries and incorrect estimation of the insulator integrity. In this way, digital image processing is a promising and objective tool to analyze the polymer surface. In this study, two thousand pictures were taken from the wetted insulator surface and analyzed by artificial neural network assisted digital image processing. The neural network used is based on back-propagation method, and Haralicks descriptors were used to quantify the hydrophobic aspect of various polymeric aged surfaces.

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