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Öğe 1D-local binary pattern based feature extraction for classification of epileptic EEG signals(Elsevier Science Inc, 2014) Kaya, Yilmaz; Uyar, Murat; Tekin, Ramazan; Yildirim, SelcukIn this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented. For the importance of making the right decision, the proposed method was performed to be able to get better features of the EEG signals. The proposed method was consisted of two stages: feature extraction by 1D-LBP and classification by classifier algorithms with features extracted. On the classification stage, the several machine learning methods were employed to uniform and non-uniform 1D-LBP features. The proposed method was also compared with other existing techniques in the literature to find out benchmark for an epileptic data set. The implementation results showed that the proposed technique could acquire high accuracy in classification of epileptic EEG signals. Also, the present paper is an attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals. (C) 2014 Elsevier Inc. All rights reserved.Öğe 1D-local binary pattern based feature extraction forclassification of epileptic EEG signals(2014) Kaya, Yılmaz; Uyar, Murat; Tekin, Ramazan; Yıldırım, SelçukIn this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented. For the importance of making the right decision, the proposed method was performed to be able to get better features of the EEG signals. The proposed method was consisted of two stages: feature extraction by 1D-LBP and classification by classifier algorithms with features extracted. On the classification stage, the several machine learning methods were employed to uniform and non-uniform 1D-LBP features. The proposed method was also compared with other existing techniques in the literature to find out benchmark for an epileptic data set. The implementation results showed that the proposed technique could acquire high accuracy in classification of epileptic EEG signals. Also, the present paper is an attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals.Öğe A Computer Vision System for the Automatic Identification of Butterfly Species via GaborFilter-Based Texture Features and Extreme Learning Machine: GF+ELM(Assoc Information Communication Technology Education & Science, 2013) Kaya, Yilmaz; Kayci, Lokman; Tekin, RamazanButterflies are classified first according to their outer morphological qualities. It is required to analyze their genital characters when classification according to their outer morphological qualities is not possible. The genital characters of butterflies can be obtained using various chemical substances and methods; however, these processes can only be carried out with some certain expenses. Furthermore, the preparation of genital slides is time-consuming since it requires specific processes. In this study, a new method based on the extreme learning machine (ELM) and Gabor filters (GFs), which is an image processing technique, was used for the identification of butterfly species as an alternative to conventional diagnostic methods. GFs have been recognized as a very useful tool in texture analysis, due to their optimal localization properties in both the spatial and frequency domains, and have been found to have widespread use in computer vision applications. To obtain the appropriate features from butterfly images in the spatial domain, 20 filters were designed for the various angles and frequencies (5 frequencies and 4 orientations). The diagnosing of butterflies was performed through ELM, with texture features based on GFs. The classification process was performed with a 75%-25% training-test set for different activation functions and the recognition performance value was obtained as 97.00%. In addition, the recognition success rates with ELM were compared to other machine learning methods and it was seen that ELM has a more significant success rate in butterfly identification than other methods. As a result, the proposed method is a suitable machine vision system for detecting butterfly speciesÖğe A New Approach for Congestive Heart Failure and Arrhythmia Classification Using Angle Transformation with LSTM(Springer Heidelberg, 2022) Kaya, Yilmaz; Kuncan, Fatma; Tekin, RamazanElectrocardiogram (ECG) is widely used as a diagnostic method to identify various heart diseases such as heart failure, cardiac and sinus rhythms. The ECG signal analyzes the electrical activity of the heart and shows waveforms that help detect heart irregularities. A new approach is suggested for automatic identification of congestive heart failure (CHF) and arrhythmia (ARR). In this study, long short-term memory neural networks (LSTM) were used to classify ECG signals by combining LSTM and angle transform (AT) methods. The AT uses the angular information of the neighbor signals on both sides of the target signal to classify ECG signals. The new signals obtained as a result of AT conversion vary between 0 and 359. Histogram of new signals determines the inputs to the LSTM method. LSTM uses histograms to distinguish between three different conditions: ARR, CHF, and normal sinus rhythm (NSR). The proposed approach is tested on ECG signals received from MIT-BIH and BIDMC databases. The experimental results have shown that the proposed method, AT + LSTM, has achieved high success rate of classifying ECG signals. The success rate in classifying CHF, ARR, and NSR ECG signals for 70-30% training sets was observed as 98.97%. Further experiments were conducted for varying training-testing dataset ratio to demonstrate the robustness of the proposed approach, and success rates are observed between 98.56 and 100%. Another experiment regarding different values of the dR and dL distance parameters of the AT model has shown that the performance of the proposed method increases while increasing the distance value. The success rates from increasing the distance value were obtained between 98.97 and 100%. To show the effect of segment lengths of ARR, NSR, and CHF signals on classification success, these signals were divided into segments of 10,000, 5000, and 1000 lengths. Achieved success rates ranged from 97.75 to 98.97%. Considering the results, high results were observed with the AT + LSTM approach, which is generally recommended in all scenarios.Öğe A NEW APPROACH FOR DIAGNOSTIC ESTIMATION OF OBSTRUCTIVE SLEEP APNEA SYNDROME BASED ON ONE DIMENSIONAL LOCAL BINARY PATTERN(IEEE, 2014) Kaya, Yilmaz; Sezgin, Necmettin; Tekin, RamazanIn this study, a new approach for estimation of Obstructive Sleep Apnea Syndrome (OSAS) was proposed. OSAS is a sleep disorder that affects the life comfortability in human life. Up to now, the OSAS was diagnosed by Polysomnography (PSG) device by connected to the patients via electrodes. This device is expensive and restricted in the clinics. Since OSAS is serious, it should be diagnosed and treated early. For this purpose, the recorded Electroencephalography (EEG), Electromyography (EMG) and snore data were analyzed and features of them extracted by a proposed method called One Dimensional Local Binary Pattern (1D-LBP). The 1D-LBP extracted features from raw data effectively. The features, then, were fed to classifier's input in order to diagnose OSAS. As a result most of tested classifiers have yielded accuracies over 99%. The best results were obtained by using EEG, EMG and snore signal altogether. It was also shown that while the complexity of signal increase the best accuracy was obtained at the output of the classifier. The results have shown that the 1D-LBP method is an acceptable and has advantageous over conventional methods due to its capable of extract significant features from more complex signal. The results can be used in sleep laboratory for help to experts before put patient to the PSG.Öğe A new approach for diagnostic estimation of Obstructive Sleep Apnea Syndrome based on One Dimensional Local Binary Pattern(IEEE Computer Society, 2014) Kaya, Yilmaz; Sezgin, Necmettin; Tekin, RamazanIn this study, a new approach for estimation of Obstructive Sleep Apnea Syndrome (OSAS) was proposed. OSAS is a sleep disorder that affects the life comfortability in human life. Up to now, the OSAS was diagnosed by Polysomnography (PSG) device by connected to the patients via electrodes. This device is expensive and restricted in the clinics. Since OSAS is serious, it should be diagnosed and treated early. For this purpose, the recorded Electroencephalography (EEG), Electromyography (EMG) and snore data were analyzed and features of them extracted by a proposed method called One Dimensional Local Binary Pattern (1D-LBP). The 1D-LBP extracted features from raw data effectively. The features, then, were fed to classifier's input in order to diagnose OSAS. As a result most of tested classifiers have yielded accuracies over 99%. The best results were obtained by using EEG, EMG and snore signal altogether. It was also shown that while the complexity of signal increase the best accuracy was obtained at the output of the classifier. The results have shown that the 1D-LBP method is an acceptable and has advantageous over conventional methods due to its capable of extract significant features from more complex signal. The results can be used in sleep laboratory for help to experts before put patient to the PSG. © 2014 IEEE.Öğe A new approach for physical human activity recognition based on co-occurrence matrices(Springer, 2022) Kuncan, Fatma; Kaya, Yilmaz; Tekin, Ramazan; Kuncan, MelihIn recent years, it has been observed that many researchers have been working on different areas of detection, recognition and monitoring of human activities. The automatic determination of human physical activities is often referred to as human activity recognition (HAR). One of the most important technology that detects and tracks the activity of the human body is sensor-based HAR technology. In recent days, sensor-based HAR attracts attention in the field of computers due to its wide use in daily life and is a rapidly growing field of research. Activity recognition (AR) application is carried out by evaluating the signals obtained from various sensors placed in the human body. In this study, a new approach is proposed to extract features from sensor signals using HAR. The proposed approach is inspired by the Gray Level Co-Occurrence Matrix (GLCM) method, which is widely used in image processing, but it is applied to one-dimensional signals, unlike GLCM. Two datasets were used to test the proposed approach. The datasets were created from the signals obtained from the accelerometer, gyro and magnetometer sensors. Heralick features were obtained from co-occurrence matrix created after 1D-GLCM (One (1) Dimensional-Gray Level Co-Occurrence Matrix) was applied to the signals. HAR operation has been carried out for different scenarios using these features. Success rates of 96.66 and 93.88% were obtained for two datasets, respectively. It has been observed that the new approach proposed within the scope of the study provides high success rates for HAR applications. It is thought that the proposed approach can be used in the classification of different signals.Öğe A new content-free approach to identification of document language: Angle patterns(Gazi Univ, Fac Engineering Architecture, 2022) Noyan, Tuba; Kuncan, Fatma; Tekin, Ramazan; Kaya, YilmazGraphical/Tabular Abstract Language identification (LI) in text mining is the process of detecting the natural language in which a document or part of it is written. LI aims to mimic a human's ability to recognize certain languages from text by computer algorithms. LI can be defined as a classification problem subject based on the information used in word or character size for any document. When the literature is examined for LI application, it is seen that various linguistic or statistical-based approaches are used. Linguistic methods are methods that perform LI according to a special word or character of a language. These methods are applied based on the special rules of the languages. When we look at the statistical methods, it shows that the words or characters that make up the language depend on their frequency and distribution. The statistical approaches used are content -independent methods. The semantic context of the text is not concerned with its content. According to linguistic methods, it does not provide sufficient information about the content of the text. The proposed model in this study is a statistical approach. Figure A. Proposed block diagram for LI Purpose: In this study, a new LI approach using the angle information between the UTF-8 values of the characters in the text is proposed. The proposed angle pattern method is used for feature extraction from texts. Angle patterns method is a statistical approach. In the angle method, there are two distance parameters, R and L, which express which neighborhood to look at from the reference point to the left and right. Theory and Methods: To test the proposed approach, four datasets, two created by the authors and two publicly available on the Internet, were used. By using the features obtained by the angle pattern method, classification process was carried out with different machine learning methods such as Random Forest, Support Vector Machine, Linear Discriminant Analysis, Naive Bayes and K-nearest neighbor. Language identification performance results determined from four different data sets were observed as 96.81%, 99.39%, 93.31% and 98.60%, respectively. Results: According to the performance results achieved as a result of the study, it has been determined that the proposed angle pattern method provides important distinguishing information in language identification application. It is thought that the proposed approach in this study can be used in many different text mining applications such as spam recognition, text categorization, as well as LI application.Öğe A novel approach for SEMG signal classification with adaptive local binary patterns(Springer Heidelberg, 2016) Ertugrul, Omer Faruk; Kaya, Yilmaz; Tekin, RamazanFeature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.Öğe Deep Learning Based Offline Handwritten Signature Recognition(2024) Çiftçi, Bahar; Tekin, RamazanIn our digitalized world, the need for reliable authentication methods is steadily increasing. Biometric authentication methods are divided into two main categories: physiological and behavioral. While physiological biometrics include features such as face, iris, and fingerprint, behavioral biometrics encompass dynamics such as gait, speech, and signature. Most of these methods require specialized equipment, whereas signatures can be easily obtained without additional tools, making them ideal for verifying the legality of documents. Although manual signature recognition is effective, it is resource-intensive, slow, and susceptible to errors. With advancements in technology, the need to automate the signature recognition process to enhance accuracy and efficiency has become increasingly important. Based on this need, in this study, five different DL techniques (GoogLeNet, MobileNet-V3 Large, Inception-V3, ResNet50 and EfficientNet-B0) are used to classify signature images with detailed analyses. DL methods have outperformed traditional techniques by leveraging the power of CNNs to automatically learn and extract complex features from signature data. The dataset used consists of a total of 12,600 images belonging to 420 individuals, each contributing 30 original signatures. The dataset is divided into training, validation, and test sets in different proportions to analyze classification performance. The pre-trained DL models were fine-tuned to optimize their parameters for the signature dataset. The results demonstrate that DL models achieve high accuracy in signature classification, with the GoogLeNet and Inception-V3 models reaching an accuracy of 98.77% at a 20% test rate. The study also highlights the impact of different test rates on model performance.Öğe Detection of Parkinson's disease by Shifted One Dimensional Local Binary Patterns from gait(Pergamon-Elsevier Science Ltd, 2016) Ertugrul, Omer Faruk; Kaya, Yilmaz; Tekin, Ramazan; Almali, Mehmet NuriThe Parkinson's disease (PD) is one of the most common diseases, especially in elderly people. Although the previous studies showed that the PD can be diagnosed by expert systems through its cardinal symptoms such as the tremor, muscular rigidity, disorders of movements and voice, it was reported that the presented approaches, which utilize simple motor tasks, were limited and lack of standardization. To achieve a standard approach in PD detection, an approach, which is built on shifted one-dimensional local binary patterns (Shifted 1D-LBP) and machine learning methods, was proposed. Shifted 1D-LBP is built on 1D-LBP, which is sensitive to local changes in a signal. In 1D-LBP the positions of neighbors around center data are constant and therefore, the number of patterns that can be exacted by it is limited. This drawback was solved by Shifted 1D-LBP by changeable positions of neighbors. In evaluation and validation stages, the Gait in Parkinson's Disease (gaitpdb) dataset, which consists of three gait datasets that were recorded in different tasks or experiment protocols, were employed. Statistical features were exacted from formed histograms of gait signals transformed by Shifted 1D-LBP. Whole features and selected features were classified by machine learning methods. Obtained results were compared with statistical features exacted from signals in both time and frequency domains and results reported in the literature. Achieved results showed that the proposed approach can be successfully employed in PD detection from gait. This work is not only an attempt to develop a PD detection method, but also a general-purpose approach that is based on detecting local changes in time ordered signals. (C) 2016 Elsevier Ltd. All rights reserved.Öğe Detection of parkinson's disease by shifted one dimensional local binary patterns from gait, expert systems with applications(2016) Ertuğrul, Ömer; Kaya, Yılmaz; Tekin, Ramazan; Almalı, NuriThe Parkinson's disease (PD) is one of the most common diseases, especially in elderly people. Although the previous studies showed that the PD can be diagnosed by expert systems through its cardinal symptoms such as the tremor, muscular rigidity, disorders of movements and voice, it was reported that the presented approaches, which utilize simple motor tasks, were limited and lack of standardization. To achieve a standard approach in PD detection, an approach, which is built on shifted one-dimensional local binary patterns (Shifted 1D-LBP) and machine learning methods, was proposed. Shifted 1D-LBP is built on 1D-LBP, which is sensitive to local changes in a signal. In 1D-LBP the positions of neighbors around center data are constant and therefore, the number of patterns that can be exacted by it is limited. This drawback was solved by Shifted 1D-LBP by changeable positions of neighbors. In evaluation and validation stages, the Gait in Parkinson's Disease (gaitpdb) dataset, which consists of three gait datasets that were recorded in different tasks or experiment protocols, were employed. Statistical features were exacted from formed histograms of gait signals transformed by Shifted 1D-LBP. Whole features and selected features were classified by machine learning methods. Obtained results were compared with statistical features exacted from signals in both time and frequency domains and results reported in the literature. Achieved results showed that the proposed approach can be successfully employed in PD detection from gait. This work is not only an attempt to develop a PD detection method, but also a general-purpose approach that is based on detecting local changes in time ordered signals.Öğe EMG Signal Classification by Extreme Learning Machine(IEEE, 2013) Ertugrul, Omer Faruk; Tagluk, M. Emin; Kaya, Yilmaz; Tekin, RamazanFrom disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.Öğe Epileptik EEG İşaretlerinin Sınıflandırılması İçin Yeni Bir Öznitelik Çıkarım Yöntemi(2018) Kaya, Yılmaz; Tekin, RamazanEpilepsi en sık karşılaşılan nörolojik hastalıklardan biri olup beyinde birgrup nöronun anormal aktivitesi sonucu oluşmaktadır. Epilepsi genellikleelektroansefalografi (EEG) sinyalleri kullanılarak teşhis edilmektedir. Bu sebeple,EEG işaretlerinden etkin özniteliklerin çıkarılması doğru sınıflandırma için önemlibir basamaktır. Bu çalışmada epileptik EEG işaretlerinden kararlı özniteliklerçıkaracak motif algoritması isimli yeni bir yaklaşım önerilmiştir. Bu yaklaşım, EEGişaretlerinde belirli büyüklükteki bir pencere içine giren değerlerin birbirleri ileolan büyüklük/küçüklük ilişkisine bağımlıdır. Pencere içindeki değerlerinbirbirlerine göre oluşturdukları görünüm bir motif olarak ele alınmaktadır. İşaretüzerindeki bu motiflerin frekansları öznitelik vektörü olarak kullanılmıştır. Motifsayısı sinyal üzerinde tanımlanan pencere boyutuna bağlıdır. Motif özniteliklerielde edildikten sonra sınıflama aşamasında RF, YSA, SVM gibi farklı sınıflandırmaalgoritmaları kullanılmıştır. Önerilen yöntemin başarısını test etmek için farklıdurumlarda (nöbet öncesi, nöbet sonrası, gözler açık ve gözler kapalı vb.) kayıtaltına alınmış EEG işaretlerinin birleşimlerinden elde edilen setler kullanılmış veyüksek sınıflandırma başarıları elde edilmiştir.Öğe Evaluation of texture features for automatic detecting butterfly species using extreme learning machine(Taylor & Francis Ltd, 2014) Kaya, Yilmaz; Kayci, Lokman; Tekin, Ramazan; Ertugrul, O. FarukIn this study, we present an application of extreme learning machine (ELM) and image processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. This paper evaluates the capability of butterfly species classification by using texture features of butterfly images. Two texture descriptors such as grey-level co-occurrence matrix (GLCM) and local binary patterns (LBP) were used for comparison purpose. ELM is employed for classification in butterfly-feature space. A total of 190 butterfly images belonging to 19 different species of Pieridae family were used. The identification accuracy of the proposed method was 98.25% and 96.45% with GLCM and LBP butterfly-feature spaces, respectively. The methodology presented herein effectively detected and classified these butterflies. These findings suggested that the proposed GLCM, LBP texture features extraction techniques and ELM algorithm are feasible and excellent in identification and classification of butterfly species.Öğe Evaluation of texture features for automatic detecting butterfly species using extreme learning machine. Journal of Experimental & Theoretical Artificial Intelligence, (2014) 26(2): 267-281(2014) Kaya, Yılmaz; Kaycı, Lokman; Tekin, Ramazan; Ertuğrul, ÖmerIn this study, we present an application of extreme learning machine (ELM) and image processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. This paper evaluates the capability of butterfly species classification by using texture features of butterfly images. Two texture descriptors such as grey-level co-occurrence matrix (GLCM) and local binary patterns (LBP) were used for comparison purpose. ELM is employed for classification in butterfly-feature space. A total of 190 butterfly images belonging to 19 different species of Pieridae family were used. The identification accuracy of the proposed method was 98.25% and 96.45% with GLCM and LBP butterfly-feature spaces, respectively. The methodology presented herein effectively detected and classified these butterflies. These findings suggested that the proposed GLCM, LBP texture features extraction techniques and ELM algorithm are feasible and excellent in identification and classification of butterfly species.Öğe Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease(Int Information & Engineering Technology Assoc, 2021) Ozel, Erdogan; Tekin, Ramazan; Kaya, YilmazParkinson's disease (PD) is a neurological disease that progresses further over time. Individuals suffering from this condition have a deficiency of dopamine, a neurotransmitter found in the brain's nerve cells that is critical for coordinating body movement. In this study, a new approach is proposed for the diagnosis of PD. Common Average Reference (CAR), Median Common Average Reference (MCAR), and Weighted Common Average Reference (WCAR) methods were primarily utilized to eliminate noise from the multichannel recorded walking signals in the resulting PhysioNet dataset. Statistical features were obtained from the clean walking signals following the Local Binary Pattern (LBP) transformation application. Logistic Regression (LR), Random Forest (RF), and K-nearest neighbor (Kim) methods were utilized in the classification stage. A high success rate with a value of 92.96% was observed with Kim. It was also determined that signals on which foot and the signals obtained from which point of the sole of the foot were effective in PD diagnosis in the study. In light of the findings, it was observed that noise reduction methods increased the success rate of PD diagnosis.Öğe Kortikal spindle salinim aktivitesinin oluşumunda ve senkronizasyonunda talamik projeksiyonlarin rolünün model temelli incelenmesi(Institute of Electrical and Electronics Engineers Inc., 2017) Tekin, Ramazan; Kaya, Yilmaz; Sezgin, Necmettin; Ta?luk, Mehmet EminIn this study, mechanisms of formation of thalamocortical spindle oscillations, which are critical for various functions of the brain, have been examined on a model basis. For this, both spike activity and spectral characteristics were investigated by isolating the cortex and thalamus and then connecting them with thalamo-cortical projections. In order to determine the spindle activity in the LFP signal of each cell group, it has been tried to determine slow-wave and spindle frequency components which can coexist with each other on the basis of superposition. For this purpose, power spectral densities of LFP signals were analyzed. According to the results of this study, spindle activity can be seen in thalamus without cortex. It can be said that thalamo-cortical projections provided by the thalamic TC cells enable the spindle activity to be transferred into the cortex and thus display itself in LFP / EEG. At the same time, it has been observed that thalamo-cortical projections increase spike activity, equalize the dominant frequency in the whole system, and also cortico-thalamic projections strengthen spindle activity. © 2017 IEEE.Öğe New local binary pattern approaches based on color channels in texture classification(Springer, 2020) Tekin, Ramazan; Ertugrul, Omer Faruk; Kaya, YilmazIn this paper, four novel, simple and robust approaches, which are left to right local binary patterns (LBPLL2R), top to down local binary patterns (LBPT2D), cube surface local binary pattern (LBPSurfaces), and cube diagonal local binary pattern (LBPDiagonal), were proposed in order to exact texture features in color images. These approaches were based on the local binary pattern (LBP), which is an effective statistical texture descriptor and can be employed in gray images. Proposed approaches were evaluated and validated in four datasets, which are Outex, KTH_TIPS, KTH_TIPS2, and USPtex datasets. The images in these datasets are in RGB, HSV, YIQ, and YCbCr color formats. Achieved results by these approaches were compared with the obtained results by the classical LBP and literature findings. As a result, the proposed approaches performed better than the traditional LBP method and they found effective in the classification of color texture images, especially in images, which are in RGB and HSV formats. Furthermore, noise robustness and time complexity of the proposed approaches were validated.Öğe A Novel Approach for SEMG Signal Classification with Adaptive Local Binary Patterns, Medical & Biological Engineering & Computing,DOI: 10.1007/s11517-015-1443-z(2016) Ertuğrul, Ömer; Kaya, Yılmaz; Tekin, RamazanFeature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.
