Digital image processing with dynamical neural networks for resource management: simulation experiments

2006 ◽  
Author(s):  
L. Morales-Mendoza ◽  
O. Ibarra-Manzano ◽  
M. Ibarra-Manzano ◽  
Y. Shmaliy
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Resource Management ◽  
Digital Image Processing ◽  
Digital Image ◽  
Simulation Experiments ◽  
Dynamical Neural Networks

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  

scholarly journals Sign Language Recognition using CNN

2021 ◽  
Vol 9 (9) ◽  
pp. 826-830
Author(s):  
Aniket Wattamwar
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Image Segmentation ◽  
American Sign Language ◽  
Sign Language ◽  
Digital Image Processing ◽  
Gesture Recognition ◽  
Digital Image ◽  
Research Work ◽  
American Sign

Abstract: This research work presents a prototype system that helps to recognize hand gesture to normal people in order to communicate more effectively with the special people. Aforesaid research work focuses on the problem of gesture recognition in real time that sign language used by the community of deaf people. The problem addressed is based on Digital Image Processing using CNN (Convolutional Neural Networks), Skin Detection and Image Segmentation techniques. This system recognizes gestures of ASL (American Sign Language) including the alphabet and a subset of its words. Keywords: gesture recognition, digital image processing, CNN (Convolutional Neural Networks), image segmentation, ASL (American Sign Language), alphabet

ASSESSMENT AND CLASSIFICATION OF MECHANICAL STRENGTH COMPONENTS OF HUMAN FEMUR TRABECULAR BONE USING DIGITAL IMAGE PROCESSING AND NEURAL NETWORKS

2007 ◽  
Vol 07 (03) ◽  
pp. 315-324 ◽  
Author(s):  
JOSEPH JESU CHRISTOPHER ◽  
SWAMINATHAN RAMAKRISHNAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Mechanical Strength ◽  
Trabecular Bone ◽  
Sensitivity And Specificity ◽  
Digital Image Processing ◽  
Digital Image ◽  
Human Femur

In this work, the assessment of the mechanical strength of human femur trabecular bone and its classification into normal or abnormal are carried out using digital image processing and neural networks. The mechanical strength components of femur trabeculae, such as primary compressive (PC), primary tensile (PT), secondary tensile (ST), and Ward's triangle (WT), are delineated by the semiautomatic image processing procedure from the planar radiographic images (N = 90) of subjects that are acquired under controlled clinical settings. Parameters such as apparent mineralization and total area of the individual mechanical strength components are calculated for normal and abnormal samples. The data are trained with neural networks and validated. The classifications are carried out using feed-forward neural networks trained with the standard backpropagation algorithm. The abnormal and normal outputs are validated by sensitivity and specificity measurements. The observation shows that the investigation of bone mechanical strength at the various strength components is useful in classifying normal and abnormal human femur trabeculae from conventional radiographs. Furthermore, the results confirm the effectiveness of the neural network–based classification of femur trabeculae into normal and abnormal conditions. The sensitivity and specificity are found to be 100% and 80%, respectively. In this paper, the methodology, data collection procedures, and neural network–based analysis and results are discussed in detail.

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