scholarly journals KLASIFIKASI AMERICAN SIGN LANGUAGE MENGGUNAKAN FITUR SCALE INVARIANT FEATURE TRANSFORM DAN JARINGAN SARAF TIRUAN

2020 ◽  
Vol 1 (1) ◽  
pp. 1-11
Author(s):  
Muhammad Restu Alviando ◽  
Muhammad Ezar Al Rivan ◽  
Yoannita Yoannita
Keyword(s):  
Neural Network ◽  
American Sign Language ◽  
Sign Language ◽  
Test Data ◽  
American Sign ◽  
Scale Invariant ◽  
Invariant Feature ◽  
The Neural Network ◽  
Feature Transform ◽  
Scale Invariant Feature

American Sign Language (ASL) is a sign language in the world. This study uses the neural network method as a classification and the scale invariant feature transform (SIFT) as feature extraction. Training data and test data for ASL images were extracted using the SIFT feature, then ANN training was conducted using 17 training functions with 2 hidden layers. There are architecture used [250-5-10-24], [250-5-15-24] and [250-15-15-24] so there are 3 different ANN architectures. Each architecture is performed 3 times so that there are 9 experiments (3 x 3 trials run the program). Determination of the number of neurons concluded by the training function is selected by the best test results on the test data. Based on the training function and the extraction of SIFT features as input values ​​in the neural network it can be concluded that from 17 training functions, trainb with neuron architecture [250-5-10-24] becomes the best training function producing an accuracy value of 95%, precision of 15 % and recall 5%.  

scholarly journals Dog recognition in public places based on convolutional neural network

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771982967
Author(s):  
Jianquan Ouyang ◽  
Hao He ◽  
Yi He ◽  
Huanrong Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Public Places ◽  
Invariant Feature ◽  
Channel Information ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
The City

With the increase in the number of dogs in the city, the dogs can be seen everywhere in public places. At the same time, more and more stray dogs appear in public places where dogs are prohibited, which has a certain impact on the city environment and personal safety. In view of this, we propose a novel algorithm that combines dense–scale invariant feature transform and convolutional neural network to solve dog recognition problems in public places. First, the image is divided into several grids; then, the dense–scale invariant feature transform algorithm is used to split and combine the descriptors, and the channel information of the eight directions of the image is extracted as the input of the convolutional neural network; and finally, we design a convolutional neural network based on Adam optimization algorithm and cross-entropy to identify the dog species. The experimental results show that the algorithm can fully combine the advantages of dense–scale invariant feature transform and convolutional neural network to achieve dog recognition in public places, and the correct rate is 94.2%.

Thai sign language translation using Scale Invariant Feature Transform and Hidden Markov Models

2013 ◽  
Vol 34 (11) ◽  
pp. 1291-1298 ◽  
Author(s):  
Sansanee Auephanwiriyakul ◽  
Suwannee Phitakwinai ◽  
Wattanapong Suttapak ◽  
Phonkrit Chanda ◽  
Nipon Theera-Umpon
Keyword(s):  
Hidden Markov Models ◽  
Sign Language ◽  
Markov Models ◽  
Hidden Markov ◽  
Language Translation ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature
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