Writing in the Air Using Kinect and Growing Neural Gas Network

2015 ◽  
Vol 72 (5) ◽  
Author(s):  
Mohammad Reza Aminian Heidari ◽  
Azrulhizam Shapi’i ◽  
Riza Sulaiman
Keyword(s):  
Character Recognition ◽  
English Language ◽  
Noise Tolerance ◽  
Recognition Method ◽  
Accuracy Rate ◽  
Growing Neural Gas ◽  
Gas Network ◽  
Neural Gas ◽  
Kinect Camera ◽  
Lower Case

This paper discusses an approach which helps us to recognize English language characters which are written in the air by hands. This method is done by using Kinect camera and growing neural gas network. The proposed character recognition method has three main steps: preprocessing, training and recognition. The system and the proposed method can be considered from two aspects: (a) runtime, and (b) accuracy. One of the main goals in this method is to provide noise tolerance which is necessary for these kinds of methods. IN addition, it has influence upon accuracy rate because the proposed method can remove more outliers. The results show that the proposed method provides good results with the accuracy rate of 95.54%, 97.86% and 99.08% for lower case letters, upper case letters and digits respectively.

scholarly journals Evaluation of different chrominance models in the detection and reconstruction of faces and hands using the growing neural gas network

2019 ◽  
Vol 22 (4) ◽  
pp. 1667-1685 ◽  
Author(s):  
Anastassia Angelopoulou ◽  
Jose Garcia-Rodriguez ◽  
Sergio Orts-Escolano ◽  
Epaminondas Kapetanios ◽  
Xing Liang ◽  
...  
Keyword(s):  
Growing Neural Gas ◽  
Gas Network ◽  
Neural Gas

scholarly journals Hierarchical Growing Neural Gas Network (HGNG)-Based Semicooperative Feature Classifier for IDS in Vehicular Ad Hoc Network (VANET)

2018 ◽  
Vol 7 (3) ◽  
pp. 41 ◽  
Author(s):  
Ayoob Ayoob ◽  
Gang Su ◽  
Gaith Al
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Detection System ◽  
Vehicular Ad Hoc Network ◽  
Location Information ◽  
Detection Accuracy ◽  
Processing Efficiency ◽  
Growing Neural Gas ◽  
Gas Network ◽  
Neural Gas

In this research, new modeling strategy based hierarchical growing neural gas network (HGNG)-semicooperative for feature classifier of intrusion detection system (IDS) in a vehicular ad hoc network (VANET). The novel IDS mainly presents a new design feature for an extraction mechanism and a HGNG-based classifier. Firstly, the traffic flow features and vehicle location features were extracted in the VANET model. In order to effectively extract location features, a semicooperative feature extraction is used for collecting the current location information for the neighboring vehicles through a cooperative manner and the location features of the historical location information. Secondly, the HGNG-based classifier was designed for evaluating the IDS by using a hierarchy learning process without the limitation of the fix lattice topology. Finally, an additional two-step confirmation mechanism is used to accurately determine the abnormal vehicle messages. In the experiment, the proposed IDS system was evaluated, observed, and compared with the existing IDS. The proposed system performed a remarkable detection accuracy, stability, processing efficiency, and message load.

scholarly journals A Method on The Translation of The Words Written by Ottoman Alphabet to Turkish Alphabet

2018 ◽  
Vol 1 (1) ◽  
pp. 39-46
Author(s):  
Önder ÖZBEK
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Comparison Process ◽  
Recognition Method ◽  
Accuracy Rate ◽  
Optical Character

The Ottoman alphabet was used as a writing language in Turkish with Arabic letters. The Turkish alphabet is used as a writing language in Turkish with Latin letters. There are numerous documents written in the Ottoman alphabet in the archives. In this study, the image of the words written in the Ottoman alphabet was converted into editable text by optical character recognition method. In this way the words are translated into text. Later, the characters in these words were made understandable by using their equivalents in the Turkish alphabet. It was tried to increase the accuracy rate by comparing the words translated into Turkish alphabet and the table where Turkish words were found. An algorithm that gives a similarity value is used for the comparison process.

Automatic Landmarking of 2D Medical Shapes Using the Growing Neural Gas Network

2005 ◽  
pp. 210-219 ◽  
Author(s):  
Anastassia Angelopoulou ◽  
Alexandra Psarrou ◽  
José García Rodríguez ◽  
Kenneth Revett
Keyword(s):  
Growing Neural Gas ◽  
Gas Network ◽  
Neural Gas
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