scholarly journals IMPLEMENTASI METODE CHAIN CODE UNTUK PENGENALAN RAMBU LALU LINTAS

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Ryan Agustian ◽  
Nugroho Agus H. ◽  
Junius Karel
Keyword(s):  
Feature Extraction ◽  
Euclidean Distance ◽  
Extraction Process ◽  
Test Results ◽  
Traffic Sign ◽  
Distance Method ◽  
Traffic Signs ◽  
Chain Code ◽  
Image Objects

Traffic sign is needed to give information to users so they can be aware in roads. There are many types of traffic signs and each has many forms and different from each other so users sometimes have difficuty in recognizing traffic signs. In this research, the signs used are signs based on Peraturan Menteri Perhubungan Republik Indonesia Nomor PM 13 Tahun 2014. Modified Chain Code method was implemented for feature extraction process and Euclidean Distance method is used to calculating the similarity. Testing is done with 5 types of tests i.e. resize image, objects truncated, added a few objects to image, added many objects to image and noise spots. The test results showed the accuracy of the image of traffic signs to be recognized is 92.5%.

Traffic Sign Recognition Based on SIFT Features

2010 ◽  
Vol 121-122 ◽  
pp. 596-599 ◽  
Author(s):  
Ni An Cai ◽  
Wen Zhao Liang ◽  
Shao Qiu Xu ◽  
Fang Zhen Li
Keyword(s):  
Traditional Method ◽  
Euclidean Distance ◽  
Experimental Results ◽  
Traffic Sign Recognition ◽  
Recognition Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Good Ability ◽  
Sift Features

A recognition method for traffic signs based on an SIFT features is proposed to solve the problems of distortion and occlusion. SIFT features are first extracted from traffic signs and matched by using the Euclidean distance. Then the recognition is implemented based on the similarity. Experimental results show that the proposed method, superior to traditional method, can excellently recognize traffic signs with the transformation of scale, rotation, and distortion and has a good ability of anti-noise and anti-occlusion.

scholarly journals Analisis Speaker Recognition Menggunakan Metode Dynamic Time Warping (DTW) Berbasis Matlab

AVITEC ◽  
2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Noor Fita Indri Prayoga
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Dynamic Time Warping ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Extraction Process ◽  
Test Results ◽  
Time Warping ◽  
Dynamic Time ◽  
The Voice

Voice is one of  way to communicate and express yourself. Speaker recognition is a process carried out by a device to recognize the speaker through the voice. This study designed a speaker recognition system that was able to identify speakers based on what was said by using dynamic time warping (DTW) method based in matlab. To design a speaker recognition system begins with the process of reference data and test data. Both processes have the same process, which starts with sound recording, preprocessing, and feature extraction. In this system, the Fast Fourier Transform (FFT) method is used to extract the features. The results of the feature extraction process from the two data will be compared using the DTW method. Calculations using DTW that produce the smallest value will be determined as the output. The test results show that the system can identify the voice with the best level of recognition accuracy of 90%, and the average recognition accuracy of 80%. The results were obtained from 50 tests, carried out by 5 people consisting of 3 men and 2 women, each speaker said a predetermined word

scholarly journals Klasifikasi Kakao Berbasis e-nose dengan Metode Neuro Fuzzy

2018 ◽  
Vol 8 (1) ◽  
pp. 49
Author(s):  
Ikhsan Nur Rahman ◽  
Danang Lelono ◽  
Kuwat Triyana
Keyword(s):  
Feature Extraction ◽  
Electronic Nose ◽  
Extraction Process ◽  
Differential Method ◽  
Test Results ◽  
Optimal Parameters ◽  
Accuracy Rate ◽  
Gas Sensor Array ◽  
Neuro Fuzzy

During this time to clasify quality of cacao based on color and aroma involving human taster. But this cacao tester still has weaknesses such as subjective. Besides that, the standard chemical analytical methods requires a high cost and need expertise to analyzing it. Basically aroma of cacao is determined by volatile compounds such aldehid and alcohol. Electronic nose based on unselected gas sensor array has the ability to analyze samples with complex compositions that can be known characteristics and qualitative analysis of the samples. Stimulus aroma is transformed by electronic nose into fingerprint data then it is used by feature extraction process using the differential method. The results of feature extraction is used to process the neuro fuzzy training to obtain optimal parameters. The parameters have been optimized is then tested on cacao. Based on test results, neuro fuzzy can clasify samples with 95,21% accuracy rate so that the clasification of cacao quality with electronic nose using neuro fuzzy has been successfully carried out.

scholarly journals Klasifikasi Rambu Lalu Lintas Menggunakan Ekstraksi Ciri Wavelet Dan Jarak Euclidean

2019 ◽  
Vol 3 (1) ◽  
pp. 26-35
Author(s):  
Vincentius Abdi Gunawan ◽  
Ignatia Imelda Fitriani ◽  
Leonardus Sandy Ade Putra
Keyword(s):  
Feature Extraction ◽  
Euclidean Distance ◽  
Daily Life ◽  
Human Mobility ◽  
Haar Wavelet ◽  
Surrounding Area ◽  
Traffic Signs ◽  
Road Users ◽  
Traffic Area ◽  
Very High

Driving is one of the human activities in which daily life is often done.  Driving can be done by land, air, and sea.  Human mobility in driving is very high on land routes using various means of transportation.  For the sake of smooth driving, roads are often equipped with traffic signs in each traffic area.  Traffic signs are a means for road users to provide information and guidance for motorists about the situation in the surrounding area.  The number of motorists who lack awareness of the knowledge of reading traffic signs is one of the biggest causes of accidents in Indonesia.  So that a system is needed that can help in recognizing traffic signs, especially prohibited signs.  The system designed using Haar Wavelet feature extraction and Euclidean distance as a classification.  From the data that has been tested, the level of recognition in reading traffic signs is prohibited by 92%.

scholarly journals Identifikasi Emosi Manusia Berdasarkan Ucapan Menggunakan Metode Ekstraksi Ciri LPC dan Metode Euclidean Distance

2020 ◽  
Vol 7 (6) ◽  
pp. 1177
Author(s):  
Siti Helmiyah ◽  
Imam Riadi ◽  
Rusydi Umar ◽  
Abdullah Hanif ◽  
Anton Yudhana ◽  
...  
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Speech Processing ◽  
Euclidean Distance ◽  
Predictive Coding ◽  
Digital Signal ◽  
Linear Predictive Coding ◽  
Distance Method ◽  
Average Accuracy ◽  
Voice Data

<p class="Abstrak">Ucapan merupakan sinyal yang memiliki kompleksitas tinggi terdiri dari berbagai informasi. Informasi yang dapat ditangkap dari ucapan dapat berupa pesan terhadap lawan bicara, pembicara, bahasa, bahkan emosi pembicara itu sendiri tanpa disadari oleh si pembicara. Speech Processing adalah cabang dari pemrosesan sinyal digital yang bertujuan untuk terwujudnya interaksi yang natural antar manusia dan mesin. Karakteristik emosional adalah fitur yang terdapat dalam ucapan yang membawa ciri-ciri dari emosi pembicara. Linear Predictive Coding (LPC) adalah sebuah metode untuk mengekstraksi ciri dalam pemrosesan sinyal. Penelitian ini, menggunakan LPC sebagai ekstraksi ciri dan Metode Euclidean Distance untuk identifikasi emosi berdasarkan ciri yang didapatkan dari LPC.  Penelitian ini menggunakan data emosi marah, sedih, bahagia, netral dan bosan. Data yang digunakan diambil dari Berlin Emo DB, dengan menggunakan tiga kalimat berbeda dan aktor yang berbeda juga. Penelitian ini menghasilkan akurasi pada emosi sedih 58,33%, emosi netral 50%, emosi marah 41,67%, emosi bahagia 8,33% dan untuk emosi bosan tidak dapat dikenali. Penggunaan Metode LPC sebagai ekstraksi ciri memberikan hasil yang kurang baik pada penelitian ini karena akurasi rata-rata hanya sebesar 31,67% untuk identifikasi semua emosi. Data suara yang digunakan dengan kalimat, aktor, umur dan aksen yang berbeda dapat mempengaruhi dalam pengenalan emosi, maka dari itu ekstraksi ciri dalam pengenalan pola ucapan emosi manusia sangat penting. Hasil akurasi pada penelitian ini masih sangat kecil dan dapat ditingkatkan dengan menggunakan ekstraksi ciri yang lain seperti prosidis, spektral, dan kualitas suara, penggunaan parameter <em>max, min, mean, median, kurtosis dan skewenes.</em> Selain itu penggunaan metode klasifikasi juga dapat mempengaruhi hasil pengenalan emosi.</p><p class="Judul2" align="left"> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Abstrak"><em>Speech is a signal that has a high complexity consisting of various information. Information that can be captured from speech can be in the form of messages to interlocutor, the speaker, the language, even the speaker's emotions themselves without the speaker realizing it. Speech Processing is a branch of digital signal processing aimed at the realization of natural interactions between humans and machines. Emotional characteristics are features contained in the speech that carry the characteristics of the speaker's emotions. Linear Predictive Coding (LPC) is a method for extracting features in signal processing. This research uses LPC as a feature extraction and Euclidean Distance Method to identify emotions based on features obtained from LPC. This study uses data on emotions of anger, sadness, happiness, neutrality, and boredom. The data used was taken from Berlin Emo DB, using three different sentences and different actors. This research resulted in inaccuracy in sad emotions 58.33%, neutral emotions 50%, angry emotions 41.67%, happy emotions 8.33% and bored emotions could not be recognized. The use of the LPC method as feature extraction gave unfavorable results in this study because the average accuracy was only 31.67% for the identification of all emotions. Voice data used with different sentences, actors, ages, and accents</em><em> </em><em>can influence the recognition of emotions, therefore the extraction of features in the recognition of speech patterns of human emotions is very important. Accuracy results in this study are still very small and can be improved by using other feature extractions such as provides, spectral, and sound quality, using parameters max, min, mean, median, kurtosis, and skewness. Besides the use of classification methods can also affect the results of emotional recognition.</em></p><p class="Abstrak"> </p>

scholarly journals Identifikasi Tulisan Tangan Huruf Katakana Jepang Dengan Metode Euclidean

2020 ◽  
Vol 4 (1) ◽  
pp. 29
Author(s):  
Imam Riadi ◽  
Abdul Fadlil ◽  
Putri Annisa
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Learning Process ◽  
Euclidean Distance ◽  
Image Data ◽  
Gray Level ◽  
Accuracy Rate ◽  
Distance Method ◽  
Object A ◽  
Data Acquisition Process

Katakana is one of the traditional Japanese letters used to absorption words from other languanges. In the inttroduction of an object a learning process is needed, which is obtained through the characteristics and experience of observing similar objects after being acquired. But manually it is quite difficult to distinguish between 5 hiragana vowels starting from the image data acquisition process, image processing, feature extraction using Gray Level Co-occurance Matrix (GLCM) while classifiers use the euclidean distance method. The results of the tests carried out showed an accuracy rate of around 78% using the euclidean method.

scholarly journals Traffic Sign Detection Using Region And Corner Feature Extraction Method

2021 ◽  
Vol 3 (1) ◽  
pp. 21-24
Author(s):  
Hendra Maulana ◽  
Dhian Satria Yudha Kartika ◽  
Agung Mustika Riski ◽  
Afina Lina Nurlaili
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Safety Information ◽  
Extraction Methods ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Feature Extraction Method ◽  
Analysis Process ◽  
Sign Detection ◽  
Traffic Sign Detection

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.

scholarly journals Sketch-Based Image Retrieval with Histogram of Oriented Gradients and Hierarchical Centroid Methods

2020 ◽  
Vol 188 ◽  
pp. 00026
Author(s):  
Viny Christanti Mawardi ◽  
Yoferen Yoferen ◽  
Stéphane Bressan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Euclidean Distance ◽  
High Threshold ◽  
Histogram Of Oriented Gradients ◽  
Test Results ◽  
Average Precision ◽  
Canny Edge Detection ◽  
Image Dataset ◽  
Canny Edge

Searching images from digital image dataset can be done using sketch-based image retrieval that performs retrieval based on the similarity between dataset images and sketch image input. Preprocessing is done by using Canny Edge Detection to detect edges of dataset images. Feature extraction will be done using Histogram of Oriented Gradients and Hierarchical Centroid on the sketch image and all the preprocessed dataset images. The features distance between sketch image and all dataset images is calculated by Euclidean Distance. Dataset images used in the test consist of 10 classes. The test results show Histogram of Oriented Gradients, Hierarchical Centroid, and combination of both methods with low and high threshold of 0.05 and 0.5 have average precision and recall values of 90.8 % and 13.45 %, 70 % and 10.64 %, 91.4 % and 13.58 %. The average precision and recall values with low and high threshold of 0.01 and 0.1, 0.3 and 0.7 are 87.2 % and 13.19 %, 86.7 % and 12.57 %. Combination of the Histogram of Oriented Gradients and Hierarchical Centroid methods with low and high threshold of 0.05 and 0.5 produce better retrieval results than using the method individually or using other low and high threshold.

scholarly journals Texture Feature Extraction by Using Local Binary Pattern

Jurnal INKOM ◽  
2016 ◽  
Vol 9 (2) ◽  
pp. 45 ◽  
Author(s):  
Esa Prakasa
Keyword(s):  
Feature Extraction ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Distance Method ◽  
Texture Pattern ◽  
Distribution Shape ◽  
Texture Characteristics ◽  
Image Pixels

Local Binary Pattern (LBP) is a method that used to describe texture characteristics of the surfaces. By applying LBP, texture pattern probability can be summarised into a histogram. LBP values need to be determined for all of the image pixels. Texture regularity might be determined based on the distribution shape of the LBP histogram. The implementation results of LBP on two texture types - synthetic and natural textures - shows that extracted texture feature can be used as input for pattern classification. Euclidean distance method is applied to classify the texture pattern obtained from LBPcomputation.

scholarly journals Finely Crafted Features for Traffic Sign Recognition

2022 ◽  
Vol 16 ◽  
pp. 159-170
Author(s):  
Wei Li ◽  
Haiyu Song ◽  
Pengjie Wang
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Image Features ◽  
Training Dataset ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Discriminative Ability ◽  
Traffic Signs ◽  
Feature Extraction Method ◽  
Sign Recognition

Traffic sign recognition (TSR) is the basic technology of the Advanced Driving Assistance System (ADAS) and intelligent automobile, whileas high-qualified feature vector plays a key role in TSR. Therefore, the feature extraction of TSR has become an active research in the fields of computer vision and intelligent automobiles. Although deep learning features have made a breakthrough in image classification, it is difficult to apply to TSR because of its large scale of training dataset and high space-time complexity of model training. Considering visual characteristics of traffic signs and external factors such as weather, light, and blur in real scenes, an efficient method to extract high-qualified image features is proposed. As a result, the lower-dimension feature can accurately depict the visual feature of TSR due to powerful descriptive and discriminative ability. In addition, benefiting from a simple feature extraction method and lower time cost, our method is suitable to recognize traffic signs online in real-world applications scenarios. Extensive quantitative experimental results demonstrate the effectiveness and efficiency of our method.

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