Object recognition from human tactile image using artificial neural network

2016 ◽  
Author(s):  
Somchai Pohtongkam ◽  
Jakkree Srinonchat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Object Recognition ◽  
Tactile Image ◽  
Artificial Neural

scholarly journals Identifikasi Jenis Mangga Berdasarkan Bentuk Menggunakan Fitur HOG dan Jaringan Syaraf Tiruan

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Edgar Utama ◽  
Fikario Yapputra ◽  
Gasim Gasim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Object Recognition ◽  
Test Data ◽  
Training Data ◽  
White Background ◽  
Histogram Of Oriented Gradients ◽  
Back Propagation Algorithm ◽  
Recognition Method ◽  
Artificial Neural

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Many searches have been done for object recognition based on shape using artificial intelligence with various feature extraction. also a lot of research has been done for the introduction of fruit types using various features of extraction and various methods of recognition. This research is identification of mango variant based on shape. Mango is one of the popular fruits and is often consumed by the community. Mango has many variants, each variant of mango usually has a different shape. Mango shape, among others, round with its variants and oval with its variants. the shape of mango is one of the differentiator of each type. Mango used is five variants of mango. The training data comprised 16 images for each variant (a total of 80 images for five mango variants). The test data consisted of 4 images for each variant (a total of 20 images for five mango variants). Train data and test data were obtained by photographing using optical camera, 5 MP secsor resolution, 20 cm photo spacing, and with white background. resize the image used size 50 x 50 pixels on the mango object only. This research uses feature from Histogram of Oriented Gradients (HOG) as training input and testing of recognition method. Recognition method using Artificial Neural Network with back propagation algorithm. Accuracy of recognition that can be achieved in this study is 90%.</em></p><p><strong><em>Keywords </em></strong><em>:<strong> </strong>Object recognition, Shape, HOG, Artificial Neural Network</em></p><p align="center"><strong> </strong></p><p align="center"><strong><em>Abstrak</em></strong><em></em></p><p><em>Banyak penelian yang pernah dilakukan untuk objek recognition berdasarkan shape menggunakan kecerdasan buatan dengan berbagai ekstraksi ciri. juga banyak penelitian yang pernah dilakukan untuk pengenalan jenis buah menggunakan berbagai fitur ekstraksi dan berbagai metode recognition. Penelitian ini adalah identifikasi varian mangga berdasarkan shape. Mangga merupakan salah satu buah yang popular dan sering dikonsumsi oleh masyarakat. Mangga memiliki banyak varian,  tiap varian dari mangga biasanya memiliki bentuk yang berbeda. Bentuk mangga antara lain bulat dengan variannya dan lonjong dengan variannya. the shape mangga merupakan salah satu pembeda dari masing-masing jenis. Mangga yang digunakan adalah lima varian mangga. Data latih terdiri 16 citra untuk tiap variannya (total 80 citra untuk lima varian mangga). Data uji terdiri dari 4 citra untuk tiap variannya (total 20 citra untuk lima varian mangga). Data latih dan data uji didapatkan dengan cara difoto menggunakan kamera optik, resolusi secsor 5 MP, jarak foto 20 cm, dan dengan latar putih. resize citra yang digunakan berukuran 50 x 50 piksel pada bagian objek mangga saja. Research ini menggunakan feature from Histogram of Oriented Gradients (HOG) sebagai input pelatihan dan pengujian metode recognition. Metode recognition menggunakan Jaringan Saraf Tiruan dengan algoritma propagasi balik. Akurasi pengenalan yang dapat dicapai dalam penelitian ini adalah sebesar 90%.</em></p><strong><em>Kata kunci </em></strong><em>:<strong> </strong>Pengenalan<strong> </strong>Objek, bentuk , HOG, Jaringan Saraf Tiruan</em>

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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