Gabor Wavelet Research Articles

Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Visually impaired people use braille letters to write and read, which not all people with normal vision can read. It causes parents of children with impaired vision to have difficulties in assisting them in learning at home. At the same time, the involvement of parents in children's learning assistance is needed to monitor their learning progress. In this research, braille letters are identified through images taken using a scanner as a tool to input the images. Then, the Canny edge detection method is used obtain all the edges of each braille dot. Feature extraction is applied to obtain all characteristics of each letter, and the method used is the Gabor Wavelet. The features which are utilized include standard deviation, mean, variance, and median with a theta angle of 00, 300, 450, 600, 1200, 1350, 1800 and wavelengths of 3, 6, 13, 28, and 58. These features are combined and used as test data and training data for the Support Vector Machine (SVM) classification stage and produce letters and words in alphabetic letter forms. Braille letters that can be identified in this research are small letters, capital letters, punctuation marks, and numbers. Tests are carried out using a multi-class confusion matrix scenario to determine the level of accuracy, precision, and recall. Based on the results of the tests conducted using 758 braille letters, the accuracy value is 98.15%; the precision value is 97.66%; and the recall value is 98.28%.

Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height

Obtaining crop above-ground biomass (AGB) information quickly and accurately is beneficial to farmland production management and the optimization of planting patterns. Many studies have confirmed that, due to canopy spectral saturation, AGB is underestimated in the multi-growth period of crops when using only optical vegetation indices. To solve this problem, this study obtains textures and crop height directly from ultrahigh-ground-resolution (GDS) red-green-blue (RGB) images to estimate the potato AGB in three key growth periods. Textures include a grayscale co-occurrence matrix texture (GLCM) and a Gabor wavelet texture. GLCM-based textures were extracted from seven-GDS (1, 5, 10, 30, 40, 50, and 60 cm) RGB images. Gabor-based textures were obtained from magnitude images on five scales (scales 1–5, labeled S1–S5, respectively). Potato crop height was extracted based on the generated crop height model. Finally, to estimate potato AGB, we used (i) GLCM-based textures from different GDS and their combinations, (ii) Gabor-based textures from different scales and their combinations, (iii) all GLCM-based textures combined with crop height, (iv) all Gabor-based textures combined with crop height, and (v) two types of textures combined with crop height by least-squares support vector machine (LSSVM), extreme learning machine, and partial least squares regression techniques. The results show that (i) potato crop height and AGB first increase and then decrease over the growth period; (ii) GDS and scales mainly affect the correlation between GLCM- and Gabor-based textures and AGB; (iii) to estimate AGB, GLCM-based textures of GDS1 and GDS30 work best when the GDS is between 1 and 5 cm and 10 and 60 cm, respectively (however, estimating potato AGB based on Gabor-based textures gradually deteriorates as the Gabor convolution kernel scale increases); (iv) the AGB estimation based on a single-type texture is not as good as estimates based on multi-resolution GLCM-based and multiscale Gabor-based textures (with the latter being the best); (v) different forms of textures combined with crop height using the LSSVM technique improved by 22.97, 14.63, 9.74, and 8.18% (normalized root mean square error) compared with using only all GLCM-based textures, all Gabor-based textures, the former combined with crop height, and the latter combined with crop height, respectively. Therefore, different forms of texture features obtained from RGB images acquired from unmanned aerial vehicles and combined with crop height improve the accuracy of potato AGB estimates under high coverage.

Nonintrusive Load Monitoring Method Based on Color Encoding and Improved Twin Support Vector Machine

In the process of traditional power load identification, the load information of V-I track is missing, the image similarity of V-I track of some power loads is high and the recognition effect is not good, and the training time of recognition model is too long. In view of the abovementioned situation, this study proposes a power load recognition method based on color image coding and the improved twin support vector machine (ITWSVM). First, based on the traditional voltage–current gray trajectory method, the bilinear interpolation technique is used to solve the pixel discontinuity problem effectively. Considering the complementarity of features, the numerical features are embedded into the gray V-I trajectory by constructing three channels, namely, current (R), voltage (G), and phase (B), so the color V-I image with rich electrical features is obtained. Second, the two-dimension Gabor wavelet is used to extract the texture features of the image, and the dimension is reduced by means of local linear embedding (LLE). Finally, the artificial fish swarm algorithm (AFSA) is used to optimize the twin support vector machine (TWSVM), and the ITWSM is used to train the load recognition model, which greatly enhances the model training speed. Experimental results show that the proposed color V-I image coding method and the ITWSVM classification method, compared with the traditional V-I track image construction method and image classification algorithm, improve the accuracy by 6.12% and reduce the model training time by 1071.23 s.

A biologically-inspired hybrid deep learning approach for brain tumor classification from magnetic resonance imaging using improved gabor wavelet transform and Elmann-BiLSTM network

Brain tumor represents the unnatural growth of cells in the brain and is identified to be one of the deadliest cancers around the globe. The survival rate of this disease varies with the stage at which the cancer is identified. Therefore, it is important to identify accurately the tumor region in the brain and also the tumor type as early as possible to improve the survival rate by appropriate treatment plans. One of the most significant ways to analyze the tumor is through the examination of magnetic resonance imaging (MRI) images of the patients. Since the amount of data being generated is huge, manual techniques are found to be inappropriate with several misclassifications. To reduce misclassifications and to cope with the large amount of data, a deep learning-based classification model is formulated in the proposed work that functions based on five major modules. Initially, the images are skull-stripped and filtered using the guided bilateral filter (GBF). Then, the tumor regions are segmented using thresholding scheme and then the major texture and edge features are collected using the improved Gabor wavelet transform (IGWT). The optimal features are selected using the black widow adaptive red deer optimization (BWARD) algorithm and then the features are fed to the hybrid Elman bidirectional long short term memory (EBiLSTM) network model for classification. The proposed model is simulated in Matlab platform using the brain tumor MRI dataset and the results proved that the proposed model is effective in classifications with an accuracy of 98.4%.

Deteksi Pembuluh Darah pada Citra Fundus Retina Menggunakan Gabungan Metode Segementasi Pembuluh Darah Lebar dan Tipis

Organs in the human body provide a lot of knowledge about human body health. One of them is the organ in the human eye, more specifically the retina.Furthermoreeven some accute diseases can be detected through the retina image. One of the diseases that can be detected through retinal images is diabetic retinopathy. This disease can be identified through segmentation to find abnormalities in the retinal blood vessels. These abnormalities include enlarged blood vessels, abnormal branching, and so on. Manual detection of blood vessels on the retina is less accurate, so a comperhensive program is required in order to segment the blood vessels in eye fundus images, more accurately. In this research, some method and algorithms are approached by combining the segmentation process for wide vessels and the segmentation process for thin vessels. There are three stages in this research. First, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and 2D Gabor Wavelet are applied as the preprocessing stage. Then, active contour and region growing methods are required as the processing to segment the thin and wide vessels in fundus retina image. And the last stage is postprocessing using the logical operator method or. The trial in this research uses color eye fundus images on the DRIVE dataset consisting of 20 retinal photos. By using this dataset, the average accuracy is 94.32%, sensitivity is 77.09% and specificity is 96.04% in 20 trials.

Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation

Fundus imaging is one of the crucial methods that help ophthalmologists for diagnosing the various eye diseases in modern medicine. An accurate vessel segmentation method can be a convenient tool to foresee and analyze fatal diseases, including hypertension or diabetes, which damage the retinal vessel’s appearance. This work suggests an unsupervised approach for vessels segmentation out of retinal images. The proposed method includes multiple steps. Firstly, from the colored retinal image, green channel is extracted and preprocessed utilizing Contrast Limited Histogram Equalization as well as Fuzzy Histogram Based Equalization for contrast enhancement. To expel geometrical articles (macula, optic disk) and noise, top-hat morphological operations are used. On the resulted enhanced image, matched filter and Gabor wavelet filter are applied, and the outputs from both is added to extract vessels pixels. The resulting image with the now noticeable blood vessel is binarized using human visual system (HVS). A final image of segmented blood vessel is obtained by applying post-processing. The suggested method is assessed on two public datasets (DRIVE and STARE) and showed comparable results with regard to sensitivity, specificity and accuracy. The results we achieved with respect to sensitivity, specificity together with accuracy on DRIVE database are 0.7271, 0.9798 and 0.9573, and on STARE database these are 0.7164, 0.9760, and 0.9560, respectively, in less than 3.17 s on average per image.

Frames with Several Generators Associated with Weyl–Heisenberg Group and Extended Affine Group

We study the construction of Gabor frames and wavelet frames for Weyl–Heisenberg group and extended affine group by using contraction between the affine group and the Weyl–Heisenberg group due to Subag, Baruch, Birman and Mann. Firstly, we give construction of Gabor frames with several generators from a unitary irreducible representation associated with the Weyl–Heisenberg group. Wavelet frames with several generators associated with an extended affine group have been obtained. A relation between frames for Weyl–Heisenberg group and extended affine group is also discussed. Finally, we show that frames of Gabor and wavelet structure are stable under small perturbations.

Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning

Identification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood smear images were being used. This study created an intelligent framework for identifying healthy blood cells from leukemic blood cells in blood smear images. The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)). As the training set, the ALL-IDB2 database was utilized to create a balanced database with 260 blood smear images. Consequently, to generate the optimum feature set, a recommended model was established by using numerous individual and combined feature extraction methodologies. The investigational consequences demonstrate that the developed feature fusion strategy surpassed previous existing techniques, with an overall accuracy of 97.49 ± 1.02% utilizing Ensemble classifier.

Cultural Tourism Industry Feature Extraction Based on Multiscale Feature Fusion Algorithm and Artificial Intelligence

The cultural tourism industry combines the similarities of the cultural industry and tourism industry, which can be the most effective way to meet people's spiritual and cultural needs as well as their leisure needs simultaneously, and has a vast development potential. However, there are numerous and dispersed areas where the market value of cultural tourism resources is clustered, and frequently, each city has the clustering area with the highest market value concentration of cultural tourism resources. This feature of the spatial distribution of the market value of cultural tourism resources is significant for promoting the development of cultural tourism as a whole and constructing the industry's overall structure. It has broad application potential for extracting and differentiating cultural tourism industry characteristics. Texture feature extraction is typically performed using dual-tree complex wavelet transform (DT-CWT) and Gabor wavelet. In this paper, we propose a multiscale DT-CWT and Gabor-based method for identifying the cultural tourism industry. The method first decomposes the images of cultural tourism into multiscale space using a Gaussian pyramid, then extracts the multiscale features of the images using DT-CWT and Gabor, and lastly achieves feature fusion. Using a support vector machine (SVM) classifier to achieve classification, the effectiveness of the feature extraction method is determined. The experimental findings demonstrate that the method proposed in this paper can achieve a high rate of recognition.

Human height and weight classification based on footprint using gabor wavelet and K-NN methods

Height and weight are parameters to identify a person, especially for a forensic. To identify height and weight is usually done manually. In addition to manually using height measuring devices and scales, you can also use information related to the foot length. There is a relationship between height and foot length can be expressed in the correlation coefficient (r) as same as for weight. Therefore, in this study, a system for measuring human height and weight based on images of the footprint is implemented on Android. The methods used in this study are Gabor Wavelet and k-Nearest Neighbor (k-NN). The simulation results generate the best accuracy of 75%. The system can also used to categorize the ideal body level according to the Body Mass Index (BMI). The system is able to process images with an average computation time of 8.92 seconds.

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier

Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger–knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.

Lung cancer diagnosis in CT images based on Alexnet optimized by modified Bowerbird optimization algorithm

ObjectiveCancer is the uncontrolled growth of abnormal cells that do not function as normal cells. Lung cancer is the leading cause of cancer death in the world, so early detection of lung disease will have a major impact on the likelihood of a definitive cure. Computed Tomography (CT) has been identified as one of the best imaging techniques. Various tools available for medical image processing include data collection in the form of images and algorithms for image analysis and system testing. MethodsThis study proposes a new diagnosis system for lung cancer based on image processing and artificial intelligence from CT-scan images. In the present study, after noise reduction based on wiener Filtering, Alexnet has been utilized for diagnosing healthy and cancerous cases. The system also uses optimum terms of different features, including Gabor wavelet transform, GLCM, and GLRM to be used in replacing with the network feature extraction part. The study also uses a new modified version of the Satin Bowerbird Optimization Algorithm for optimal designing of the Alexnet architecture and optimal selection of the features. ResultsSimulation results of the proposed method on the RIDER Lung CT collection database and the comparison results with some other state-of-the-art methods show that the proposed method provides a satisfying tool for lung cancer diagnosis. The comparison results show that the proposed method with 95.96% accuracy shows the highest value toward the others. The results also show that a higher harmonic mean value for the proposed method with higher F1-score of the method toward the others. Plus, the highest test recall results (98.06%) of the proposed method indicate its higher rate of relevant instances that are retrieved for the images. ConclusionTherefore, using the proposed method can provide an efficient tool for optimal diagnosis of the Lung Cancer from the CT Images. Significancethis shows that using the proposed method as a new deep-learning-based methodology, can provide higher accuracy and can resolve the big problem of optimal hyperparameters selection of the deep-learning-based methodology techniques for the aimed case.

Study on steel plate scratch detection based on improved MSR and phase consistency

In the casting process of the steel plate, due to the influence of rolling equipment and technology, the defects such as cracks and scratches appear on the surface of steel plate, which affect the performance of steel plate and even cause production accidents. In this paper, an automatic detection method for steel plate scratch is proposed. Firstly, the steel plate image is decomposed by channel and the enhanced image is obtained by the improved MSR (Multi-Scale Retinex) enhancement algorithm. Then, the phase consistency is detected after the Log Gabor wavelet transform and the scratch areas are obtained by the threshold segmentation and intersection of three channels. Finally, the scratch position is identified and the scratch characteristics such as width and length can be calculated. The results show that the minimum error of the characteristics measurement is only 2.28% in the experimental environment and 4.15% in the field environment, and the mean running time is 0.2826 s in the experimental environment and 0.3193 s in the field environment. It verifies that the proposed method is effective and practical.

Extraction information of moiré fringes based on Gabor wavelet

Extraction information of moiré fringes based on Gabor wavelet

An approach for brain tumour detection based on dual-tree complex Gabor wavelet transform and neural network using Hadoop big data analysis

An approach for brain tumour detection based on dual-tree complex Gabor wavelet transform and neural network using Hadoop big data analysis

Classification of North and South Indian Handwritten Scripts using Gabor Wavelet Features

Objectives: Handwritten script identification plays a vital role in processing handwritten data electronically. Most of the methods fail to provide accuracy due to variation in handwriting, hence the classification of the Indic script before providing it to OCR is crucial. The anticipated work helps increase the accuracy by categorizing the handwritten documents as north or South Indic script before further classification. Methods: This study has proposed a method, using Gabor filters to extract features from the text image for recognizing the kind of script, and seven widely used Indian scripts were considered for this experiment. The handwritten documents were collected from distinct individuals on request, under supervision. The database was manually created by extracting portions of lines from the scanned document images. Findings: A recognition accuracy of 100% was obtained for classifying North and South scripts while an average accuracy of 92% was obtained for biscript classification using KNN classifier at a portion of the line level. Novelty: The proposed method improves the accuracy by acting as a pre-processor to the OCR system by classifying the script according to North Indian script or South Indian Script. Further, it can be processed to find out the script type within the North or South Indian Scripts. Keywords: Handwritten Script; Gabor Filter; KNN Classifier; OCR; Indic Script

Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm

AbstractAiming at the problem of long time‐consuming and low accuracy of existing age estimation approaches, a new age estimation method using Gabor feature fusion, and an improved atomic search algorithm for feature selection is proposed. Firstly, texture features of five scales and eight directions in the face region are extracted by Gabor wavelet transform. The statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor scales. Secondly, a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing (CIASO‐SA) is presented, which is based on an improved atomic search algorithm and the simulated annealing algorithm. Besides, the CIASO‐SA algorithm introduces a chaos mechanism during atomic initialisation, significantly improving the convergence speed and accuracy of the algorithm. Finally, a support vector machine (SVM) is used to get classification results of the age group. To verify the performance of the proposed algorithm, face images with three resolutions in the Adience dataset are tested. Using the Gabor real part fusion feature at 48 × 48 resolution, the average accuracy and 1‐off accuracy of age classification exhibit a maximum of 60.4% and 85.9%, respectively. Obtained results prove the superiority of the proposed algorithm over the state‐of‐the‐art methods, which is of great referential value for application to the mobile terminals.

Methods of local features extraction in person authentication task by face thermographic image

The paper presents a methods of image local features extraction research in relation to people authentication problem by face thermogram. As a part of the study, there were formed two datasets for methods training and testing: photographic images and face images in the long-wavelength infrared specter (LWIR) with various factors. The novelty of this study is due to the approach to collecting datasets to verify the accuracy of authentication methods. The dataset was collected with more realistic conditions that affect the quality of authentication, such as changing facial expressions, wearing glasses, medical masks, applying make-up/cosmetics, changing the lighting and temperature conditions of the environment, rotating the head. The methods core is based on the idea of constructing a vector of image features while reducing the dimension and highlighting the boundaries. Likewise, the methods of this group cope well with extracting features task on images and are widely used in the tasks of authentication by 2D face image, as well as in other computer vision tasks. In this paper, four classical methods of local feature extraction are considered: the method of locally binary templates, Gabor wavelets, scale-invariant feature transformation, and Weber’s local descriptor. The classifiers for the feature vectors comparison in this research are SVM and the simplest Perceptron — the basic methods of machine learning. As part of the study, a comparative analysis of each method was carried out in relation to the collected datasets. The methods were trained and tested on a collected face dataset of over 632,000 images of 152 people. As a result of the comparative analysis, it can be concluded that the method of local binary features demonstrates the best result among the considered methods for both types of data: for face thermograms (for SVM — 0,57, for Perceptron — 0,58), for photographic images (for SVM — 0,71, for Perceptron — 0,73). Furthermore, the SIFT method showed similar results: for face thermograms (for SVM — 0,58, for Perceptron — 0,55), for photographic images (for SVM — 0,72, for Perceptron — 0,74). Gabor filters and Weber local descriptor application demonstrate a low accuracy rate in the authentication task by both types of data. The results of the work can be used in access control and management systems to increase the fault tolerance of person authentication. The appliance of the considered methods are effective in the tasks such as processing thermograms for authentication a person by so-called “secondary” signs, for example, by the veins and vessels on face patterns, in cases of facial expressions and appearance changes.

Research on damage monitoring of sandwich armor composite structure based on time reversal principle

Abstract With the wide application of lightweight armor composite, it is of great significance to monitor delamination damage in laminated materials. Ultrasonic testing often needs to be immersed in liquid for monitoring, which is inconvenient for large-size specimens and cannot be monitored in real time. In structural health monitoring, the corresponding relationship between damage and response signal characteristics (such as amplitude and energy) is established, which is greatly affected by various environmental factors (such as temperature and load). Due to the particularity of the multi-layer composite structure with large thickness, the problem of Lamb wave multi-mode and frequency dispersion affects the accuracy of damage monitoring, which brings great difficulty to the accuracy of damage monitoring. Four piezoelectric sensors were arranged on the surface of the 5 mm sandwich armor composite composed of alumina ceramic, T700 carbon fiber and TC4 titanium alloy. The signal-to-noise ratio of the obtained damage scattering signal was improved by time reversal method and Gabor wavelet transform. Monitoring and imaging of single damage and multiple damage on the reverse side of armor composite were studied. The experimental results show that the radial error of single damage location is 4.24 mm, and the radial error of multiple damage location is 5.39 mm.

Iris recognition approach for identity verification with DWT and multiclass SVM.

The iris has been proven to be one of the most stable and accurate biometrics. It has been widely used in recognition systems to determine the identity of the individual who attempts to access secured or restricted areas (e.g., airports, ATM, datacenters). An iris recognition (IR) technique for identity authentication/verification is proposed in this research. Iris image pre-processing, which includes iris segmentation, normalization, and enhancement, is followed by feature extraction, and matching. First, the iris image is segmented using the Hough Transform technique. The Daugman’s rubber sheet model is the used to normalize the segmented iris area. Then, using enhancing techniques (such as histogram equalization), Gabor wavelets and Discrete Wavelets Transform should be used to precisely extract the prominent characteristics. A multiclass Support Vector Machine (SVM) is used to assess the similarity of the images. The suggested method is evaluated using the IITD iris dataset, which is one of the most often used iris datasets. The benefit of the suggested method is that it reduces the number of features in each image to only 88. Experiments revealed that the proposed method was capable of collecting a moderate quantity of useful features and outperformed other methods. Furthermore, the proposed method's recognition accuracy was found to be 98.92% on tested data.