scholarly journals OCR FOR ENGLISH CHARACTERS BASED ON POLAR HISTOGRAM FEATURE EXTRACTION AND EUCLIDEAN DISTANCE

2021 ◽  
Author(s):  
Saleh Ali Alshehri
Keyword(s):  
Feature Extraction ◽  
Euclidean Distance ◽  
Histogram Feature

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

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 Colour recognition using colour histogram feature extraction and K-nearest neighbour classifier

2020 ◽  
pp. 08-14
Author(s):  
Rabia Bayraktar ◽  
Batur Alp Akgul ◽  
Kadir Sercan Bayram
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Feature Extraction ◽  
Video Camera ◽  
Training Data ◽  
Machine Learning Classification ◽  
The Neural Network ◽  
Knn Classifier ◽  
Nearest Neighbour Classifier ◽  
Histogram Feature

K-nearest neighbours (KNN) is a widely used neural network and machine learning classification algorithm. Recently, it has been used in the neural network and digital image processing fields. In this study, the KNN classifier is used to distinguish 12 different colours. These colours are black, blue, brown, forest green, green, navy, orange, pink, red, violet, white and yellow. Using colour histogram feature extraction, which is one of the image processing techniques, the features that distinguish these colours are determined. These features increase the effectiveness of the KNN classifier. The training data consist of saved frames and the test data are obtained from the video camera in real-time. The video consists of consecutive frames. The frames are 100 × 70 in size. Each frame is tested with K = 3,5,7,9 and the obtained results are recorded. In general, the best results are obtained when used K = 5.   Keywords: KNN algorithm, classifier, application, neural network, image processing, developed, colour, dataset, colour recognition.

The Research of the Rolling Element Bearing Fault Signal Frequency Domain Feature Extraction Method Based on the Wavelet Packet Decomposition

2014 ◽  
Vol 937 ◽  
pp. 351-356 ◽  
Author(s):  
Shi Yin Qiu ◽  
Rui Bo Yuan
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Euclidean Distance ◽  
Wavelet Packet ◽  
Feature Space ◽  
Rolling Element Bearing ◽  
Signal Spectrum ◽  
Wavelet Packet Decomposition ◽  
Bearing Fault ◽  
Optimal Feature

The wavelet packet decomposition can be used to extract the frequency band containing bearing fault feature, because the fault signal can be decomposed into different frequency bands by using the wavelet packet decomposition, that is to say the optimal wavelet packet decomposition node needs to be found. A method applying the average Euclidean distance to find the optimal wavelet packet decomposition node was presented. First of all, the bearing fault signals were decomposed into three layers wavelet coefficients by which the bearing fault signals were reconstructed. The peak values extracted from the reconstructing signal spectrum constructed a feature space. Then, the minimum average Euclidean distance calculated from the feature space indicated the optimal wavelet packet node. The optimal feature space could be constructed by the feature points extracted from the signals reconstructed by the optimal wavelet packet nodes. Finally, the optimal feature space was used for the K-means clustering. The feature extraction and pattern recognition test of the four kinds of bearing conditions under four kinds of rotation speeds was detailed. The test results show this method, which can extract the bearing fault feature efficiently and make the fault feature space have the lowest within-class scatter, wons a high pattern recognition accuracy.

An Improved Locally Linear Embedding Method for Feature Extraction

2011 ◽  
Vol 467-469 ◽  
pp. 487-492
Author(s):  
Wei Zhang ◽  
Wei Jia Zhou
Keyword(s):  
Feature Extraction ◽  
Tangent Space ◽  
Euclidean Distance ◽  
Classical Method ◽  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Simulation Results ◽  
Space Distance ◽  
Better Than ◽  
Locally Linear

In this work, a feature extraction approach based on improved Locally Linear Embedding(LLE) is proposed. In the algorithm, tangent space distance is introduced to LLE, which overcomes the shortcoming of original LLE method based on Euclidean distance. It can satisfy the requirement of locally linear much better and so can express the I/O mapping quality better than classical method. Simulation results are given to demonstrate the effectiveness of the improved LLE method.

scholarly journals IR-HF-WED: IMAGE RETRIEVAL USING HYBRID FEATURE EXTRACTION WITH WEIGHTED EUCLIDEAN DISTANCE

2020 ◽  
Vol 9 (6) ◽  
pp. 3987-3999
Author(s):  
K. Venkataravana Nayak ◽  
M. Geetanjali ◽  
J. S. Arunalatha ◽  
K. R. Venugopal
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Euclidean Distance ◽  
Hybrid Feature Extraction

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 SEGMENTASI PASAR KOPI MENGGUNAKAN METODE KNN DI INDONESIA

2020 ◽  
Vol 1 (1) ◽  
pp. 34-38
Author(s):  
Muhamad Maksum Hidayat ◽  
Wahyu Nugroho ◽  
Arief Setyanto
Keyword(s):  
Feature Extraction ◽  
Market Segmentation ◽  
Nearest Neighbor ◽  
Image Data ◽  
Market Development ◽  
K Nearest Neighbor ◽  
K Nearest Neighbor Algorithm ◽  
Coffee Market ◽  
The Right ◽  
Histogram Feature

Coffee is one of the most widely consumed plantation commodities in Indonesia and has great opportunities to be developed, especially in the field of market development. The thing that becomes an obstacle for coffee entrepreneurs is the right segmentation of the coffee market with the vast Indonesian state. This study aims to segment the coffee market in Indonesia by utilizing coffee image data available on Instagram social media with image classification using the K-Nearest Neighbor algorithm and histogram feature extraction, so as to determine the exact coffee market segmentation in Indonesia.

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