Gabor Wavelet Research Articles

Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets

Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep learning-based computer-aided diagnostic (CAD) platforms have become valuable tools for supporting dermatologists. Nevertheless, current CAD tools frequently depend on Convolutional Neural Networks (CNNs) with huge amounts of deep layers and hyperparameters, single CNN model methodologies, large feature space, and exclusively utilise spatial image information, which restricts their effectiveness. This study presents SCaLiNG, an innovative CAD tool specifically developed to address and surpass these constraints. SCaLiNG leverages a collection of three compact CNNs and Gabor Wavelets (GW) to acquire a comprehensive feature vector consisting of spatial–textural–frequency attributes. SCaLiNG gathers a wide range of image details by breaking down these photos into multiple directional sub-bands using GW, and then learning several CNNs using those sub-bands and the original picture. SCaLiNG also combines attributes taken from various CNNs trained with the actual images and subbands derived from GW. This fusion process correspondingly improves diagnostic accuracy due to the thorough representation of attributes. Furthermore, SCaLiNG applies a feature selection approach which further enhances the model’s performance by choosing the most distinguishing features. Experimental findings indicate that SCaLiNG maintains a classification accuracy of 0.9170 in categorising SC subcategories, surpassing conventional single-CNN models. The outstanding performance of SCaLiNG underlines its ability to aid dermatologists in swiftly and precisely recognising and classifying SC, thereby enhancing patient outcomes.

A Hybrid Cloud Approach for Secure Authorized Deduplication System Using Facial Authentication

Over the use of peer-to-peer encryption, businesses are able to establish private communication channels between devices or parts of devices, ensuring that no intermediary devices are able to decipher important data as it travels over the network. To safeguard sensitive information like credit card details, PIN numbers, and passwords, privacy is essential. Rather of being handed down orally, this kind of knowledge may be exchanged digitally using messaging programs such as WhatsApp or Facebook Messenger.Most of these apps use what is known as peer-to-peer encryption, a kind of two-way communication in which no one other than the two parties involved can decipher the communications transmitted and received. It is necessary to remember the master password in order to access the password safe, which stores all the passphrases in one account. Other malevolent bots and hackers are prompted to plot an assault and attempt to breach the system by this strategy.Therefore, we provide an encryption technique in our suggested work that relies on the authentic user's facial landmarks. Since it is very difficult, if not impossible, to replicate all of the face characteristics in order to fool the authentication system, this method is extremely strong. To determine the legitimate user's identity, we use the Localised Binary Pattern method in conjunction with the Gabor Wavelet on the locality preserving projection model. Both the legitimacy on the peer-to-peer network and the encryption of data communicated across the network channel are supported by the face structure that has been extracted. As a result, this forms a very strong component for the authentication and encryption of sent communications. Key Words: peer-to-peer, system, encryption, authentication, breach, transmitted

A Four-Grade Astrocytoma Classification System with Optimal Feature Selection Using Ant Colony Optimization Algorithm

An efficient Four-Grade Astrocytoma brain tumor classification is proposed in this research work. The work highlights the optimal feature selection using a hybrid combination of Ant Colony Optimization (ACO) algorithm and machine learning techniques, leading to better classification accuracy. A diverse set of features including spatial and frequency domain features will provide better discrimination of patterns. ACO provides a robust and efficient mechanism for exploring the vast space of possible feature subsets. A total of 135 features are extracted including Tamura features, Gabor Wavelet Features, Coiflet Wavelet Coefficients, Speeded Up Robust Features, and GLCM features. Ant Colony Optimization is used to find out the optimum set of features. The selected feature set is used for training a Decision Tree classifier, where the Astrocytoma MRI images are categorized into four grades of astrocyoma namely, pilocytic astrocytoma, diffuse astrocytoma, anaplastic astrocytoma and glioblastoma multiforne. Classification is also performed using KNN classifier, SVM classifier and Ensemble Bagged Tree Classifier. Performance metrics like accuracy, sensitivity, specificity, precision and FScore are utilized to evaluate the proposed system’s performance. It is found out that the decision tree classifier outperforms the other classifiers. Thus this integrated approach of ACO with machine learning classifiers results in improved Astrocytoma classification performance aiding clinicians in making accurate treatment decisions.

Digital holographic microscope for high spatial and temporal resolution in situ observation of dynamic phenomena of metals

A digital holographic microscope (DHM) was developed for the purpose of simultaneous observation and measurement of surface relief formation during deformation and phase transformation of steels. It was designed to accommodate the sample mounting component of a custom tensile tester. The Gabor wavelet transform method was used in the image reconstruction. To verify performance, the DHM was used to observe and measure height of slip bands formed during the monotonic loading 316L stainless steel. Data obtained has high temporal resolution (limited only by the camera’s frame rate capability) with lateral resolution approaching the diffraction limit. The results clearly show the evolving microstructures with height information available within each captured image.

RAGE-Net: Enhanced retinal vessel segmentation U-shaped network using Gabor convolution

Extracting vessel morphology from fundus images is pivotal in acquiring pathological insights and enabling early diagnosis of retinal disorders. Manual segmentation of retinal vessels requires a high degree of expertise and is notably time-intensive. Although existing deep learning techniques for retinal vessel segmentation predominantly hinge on U-shaped convolutional neural networks, significant headway has been made, complexities persist in delineating faint, low-contrast vessels amidst noisy backgrounds. To confront these challenges, we propose an innovative U-shaped convolutional neural network fortified with oriented priors, labeled as the Receptive Field Aggregating Gabor Enhance Network (RAGE-Net). We revamp the conventional U-shaped convolutional network with a foundation in Gabor wavelet and Gabor convolutional network, introducing a Gabor Matching Enhance Architecture (GMEA) amalgamated into the U-shaped convolutional network. This architecture comprises two distinct modules. Initially, a Dual-scale Gabor Enhance Module (DGEB) is introduced to bolster vessel continuity and effectively fortify delicate vessels by integrating oriented feature enhancement through Gabor convolution. Subsequently, a Receptive Field Pyramid Module (RPM) is proposed to supplant the escalation of scale count in the Gabor filter bank for vessel alignment, also serving as feature fusion to enhance the network's comprehensive vessel discernment. In comparison to U-Net, our model boasts fewer parameters and surpasses in terms of sensitivity, accuracy, and F1 score on the DRIVE dataset by 3.58%, 0.34%, and 2.29%, respectively. Our model showcases stellar performance across three public datasets: DRIVE, STARE, and CHASE_DB1, with sensitivity values of 0.8172, 0.8126, and 0.8540 and accuracy figures of 0.9708, 0.9725, and 0.9757, respectively. The analysis on three datasets reveals that our model demonstrates distinct strengths in AUC and Se as a result of integrating GMEA, which comprises RPM and DGEB, into U-shaped networks. However, it does not reduce overall accuracy to improve the model's ability to perceive weak contrast and small blood vessels. While maintaining superior Acc, our model also demonstrates advancements in Sp and F1 score, indicating a balanced progression in multiple evaluation metrics.

Precancerous Change Detection Technique on Mammography Breast Cancer Images based on Mean Ratio and Log Ratio using Fuzzy c Mean Classification with Gabor Filter

The growing rate of breast cancer necessitates immediate global attention. Mammography images are used to determine the stage of malignancy. Breast cancer stages must be identified in order to save a person's life. This article's main goal is to identify different techniques to obtain the difference between two breast cancer mammography images taken of the same individual at different times. This is the first effort to identify breast cancer in mammography images using change detection techniques. The Mammogram Image Change Detection (ICD) technique is also a recent advancement to prevent breast cancer in the early stage and precancerous level in medical images. The main purpose of this work is to observe the changes between breast cancer images in different screening periods using different techniques. Mammogram Breast Cancer Image Change Detection (MBCICD) methods usually start with a Difference Image (DI) and classify the pixels in the DI into changed and unaffected classes using unsupervised fuzzy c means (FCM) clustering methods based on texture features taken from the log and mean ratio difference pictures. Two operators, mean ratio and log ratio, were used to check the changes in the images. The Gabor wavelet is utilized as a feature extraction technique among several standards. Using the Gabor wavelet ratio operators is a useful method for altering the detection of breast cancer in mammography images. Currently, it is challenging to obtain real malignant images of the same person for testing or training. In this study, two images are utilized. To clearly see the changes, one is an image from the MIAS breast cancer mammography images dataset, and the other is a self-generated change image. The research aims to examine the image results and other quantitative analysis results of proposed change detection methods on cancer images. The Mean Ratio Accuracy result is 0.9738, and the Log ratio PCC is 0.9737. The classification results are the Log Ratio + Gabor Filter + FCM is 0.9737, and Mean Ratio +Gabor Filter + FCM is 0.9719. The mean Ratio Accuracy result is 0.9738, Log ratio is 0.9737. Log Ratio + Gabor Filter + FCM is 0.9737, Mean Ratio +Gabor Filter + FCM is 0.9719. Comparing the PCC of proposed change detection methods with the FDA-RMG method on the same dataset, the accuracy is 0.9481 only. The study concludes that variations in mammography breast cancer images could be successfully identified using the ratio operators with Gabor wavelet features.

Multi-dimensional CNN Based Feature Extraction with Feature Fusion and SVM for Human Activity Recognition in Surveillance Videos

Background/Objectives: The accurate recognition of human activities from video sequences is very challenging due to low resolution, cluttered background, partial occlusion, and different viewpoints. Machine learning (ML) based automated HAR from surveillance videos is required with the fusion of various feature extraction techniques. Methods: In this paper, SVM with feature fusion is utilized for automatic recognition from surveillance videos. A Histogram of Oriented Gradient (HOG) is used to segment the frame to differentiate humans from other objects or background noise in the input video frames. The multi-feature extraction can be accomplished in terms of Gabor Wavelet Transform (GWT), Autocorrelogram, Gray-Level Co-Occurrence Matrix (GLCM), HSV histogram, and Multi-dimensional CNN. The proposed approach is implemented in MATLAB software and compared with existing approaches like Space-Time Interest Point (STIP) and Histogram of Optical Flow (HOF). Findings: The proposed approach outperforms the existing approaches in terms of reduced time consumption and high accuracy, 99.886% when using the UCF101 dataset and 99.538% when using the UTKinect dataset. Novelty: The most discriminative feature information is obtained with the feature-level fusion technique. From the feature information, various human actions are recognized with the classification algorithm. Keywords: Human activity recognition, Machine Learning, Surveillance Videos, Human detection algorithm, Feature extraction, SVM classifier

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.

Combining contour-based and region-based in image segmentation.

This paper presents an optimized clustering approach applied to image segmentation. Accurate image segmentation impacts many fields like medical, machine vision, object detection. Applications involve tumor detection, face detection and recognition, and video surveillance. The developed approach is based on obtaining an optimum number of clusters and regions of an image. We combined Region-based and contour-based approaches. Initial rough regions are obtained using edge detection. We have used Gabor wavelets for texture classification and spatial resolutions. Color frequencies are also used to determine the number of clusters of the Fuzzy c-means (FCM) algorithm which gave an optimum number of clusters or regions. We have compared our approach with other similar wavelet and clustering techniques. Our algorithm gave better values for segmentation metrics like SNR, PSNR, and MCC. Optimizing the number of clusters or regions has a significant effect on the performance of the image segmentation techniques. This will result in better detection and localization of the segmentation-based application.

Retracted: Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning.

[This retracts the article DOI: 10.1155/2021/6621540.].

Extraction of moiré fringes' phase information based on the Morlet wavelet.

The extraction of phase information is crucial in moiré tomography for achieving accurate results. In this paper, a method for extracting phase information of moiré fringes based on the Morlet continuous wavelet transform is introduced. A detailed exposition of the theoretical deduction and algorithmic procedure of this method is provided. And then, to validate the feasibility and applicability of this approach, four flow fields are conducted as test objects for experiments. Based on that, the phase results provided by the Morlet continuous wavelet transform are compared with those obtained by the reported techniques such as Fourier transform and Gabor wavelet transform. It is evident that Morlet continuous wavelet transform demonstrates superior accuracy and smoothness, which proves the reliability of this method. In summary, the method presented in this study probably offers an effective method with broad applications.

Semiparametric constant false alarm rate method for radar and sonar images

AbstractThis work proposed a novel constant false alarm rate detection method based on Gaussian mixture model (GMM). The main contribution contains two aspects. First, the Gabor wavelet is used to determine the GMM component number. Second, the expression of probability of false alarm is derived based on the GMM parameter.

Deep Learning based Smart Image Search Engine

Abstract: This paper introduces a new reverse search engine integration into content-based image retrieval (CBIR) systems that employs convolutional neural networks (CNNs) for feature extraction. It generates global descriptors using pre-trained CNN architectures such as ResNet50, InceptionV3, and InceptionResNetV2. It retrieves visually similar images without depending on linguistic annotations. Comparative analysis against existing methods, such as Gabor Wavelet, CNN-SVM, Metaheuristic Algorithm, etc., has been tested, and it proves the superiority of the proposed algorithm, the Cartoon Texture Algorithm, in CBIR. As the Internet sees an exponential growth of different data types, the importance of CBIR continues to grow. In order to efficiently retrieve images, solely relying on image features while ignoring metadata is exactly what we need. As such, this paper is a reminder of the need for CBIR in this changing world. They showed that CBIR continues to be quite effective in the age of the Internet. Their proposed model for CBIR, which integrates ResNet-50-based feature extraction, a neural network model trained on different image datasets, and clustering techniques to make retrieval fast, provides a significant improvement in accuracy and efficiency for content-dependent image retrieval. This methodology is likely to be very useful as we work with the increasingly huge data of vision and beyond on the Internet. It provides a good basis for an effective image search and retrieval system

A general and simple automated impervious surface mapping approach based on three-dimensional texture features (3DTF) using fine spatial resolution remotely sensed imagery

The mapping of impervious surfaces using remote sensing techniques offer essential technical support for sustainable development objectives and safeguard the environment. In this study, we developed an automated method without training samples for mapping impervious surfaces using texture features. The different aggregated impervious surface patterns and distributions in study areas of Site A-C in China (Beijing, Huainan, Jinhua) were considered. The Site D-E in Dubai and Tehran, surrounded with deserts in arid areas. They were selected to develop and evaluate the performance of the proposed automated method. The texture features of the Contrast, Gabor wavelets, and secondary texture extraction (Con_Gabor) derived from Sentinel-2 images at each site were used to construct the three-dimensional texture features (3DTF) of impervious surfaces. The 3DTF-combined K-means classifier was used to automatically map the impervious surfaces. The results showed that the overall accuracies of mapping impervious surface were 91.15 %, 89.75 %, and 91.90 % in Site A-C. The overall accuracies of mapping impervious surface were 90.95 %, 91.45 % and 88.23 % in rural areas. The distributions of impervious surface on automated method, GHS-BUILT-S and ESA WorldCover were similar in study areas. The automated method for mapping impervious surfaces performed as well as the artificial neural network (ANN) and Random Forest (RF), and the advantage of not requiring training samples. The automated method was tested in the in Dubai and Tehran. The overall accuracies of the automatic method for mapping impervious surfaces >89 % at Site D-E, and >88 % at rural area. In addition, the 3DTF was proved as the simplest and most effective feature combination to map impervious surfaces. The impervious surface mapped using the automated method was robust across bands, seasons and sensors. However, further evaluation is necessary to assess the effectiveness of automated methods using high spatial resolution images for mapping impervious surface in complex areas.

Numerical investigation of liquid sloshing in 2D flexible tanks subjected to complex external loading

Due to the complexity of fluid–structure interactions (FSI), the majority of studies in the literature dealing with the sloshing problem are restricted to rigid tanks. This paper is devoted to a numerical investigation of the liquid sloshing behavior in a flexible tank subjected to external loading. A numerical methodology is proposed, taking into account the FSI problem by coupling two open-source codes: OpenFOAM for the fluid and FEniCS for the solid, using the preCICE library, a free library for fluid–structure interaction. The Arbitrary Lagrangian–Eulerian formulation is used for the two-phase flow system to solve the Navier–Stokes equations in the fluid domain using the finite volume method. Simultaneously, the linear-elastic equation of the structure is solved using the finite element method. An implicit coupling scheme is considered at the fluid–structure interface. The numerical methodology is validated by the results given in literature for harmonic excitation at different frequencies. Subsequently, an analysis of complex external loading, such as Gabor wavelets and earthquake ground motion, is conducted to highlight the significant impact of the wall flexibility on sloshing, as well as the influence of hydrodynamic forces on the structure’s deformation. The proposed coupling methodology is robust and effective, it can be applied to all types of liquids and materials. A dataset of one of the studied cases is given as a supplement to the paper (Kha et al., 2024).

Gabor wavelet and neural network face detection

One of the most difficult tasks in image processing is facial area detection. This study introduces a new face detection method. To improve detection rates, the system incorporates two facial detection algorithms. Gabor wavelets and neural networks are the two algorithms. Convolutional face images undergo initial transformation using Gabor wavelets, with 8 orientations and 5 scales chosen to extract the grey characteristics of the facial region. When added to the original photos, these 40 Gabor wavelets reveal the full extent of the response. We use a second feedforward neural network specifically designed for facial detection. The neural network is trained by backpropagation using the training set of faces and non-faces. Our experiments show that the suggested Gabor wavelet faces, when combined with the neural network feature space classifier, provide very respectable results. Comparing our proposed system to other face detection systems reveals that it performs better in terms of detection and false negative rates.

Fuzzy information granulation towards benign and malignant lung nodules classification

Lung cancer is the second common cancer with the highest death rate in the world. Cancer diagnosis in the early stages is a critical factor for increasing the treatment speed. This paper proposes a new machine learning method based on a fuzzy approach to detect benign and malignant lung nodules to early-diagnose lung cancer by investigating the computed tomography (CT) images. First, the lung nodule images are pre-processed via the Gabor wavelet transform. Then, some of the texture features are extracted from the transformed domain based on the statistical characteristics and histogram of the local patterns of images. Finally, based on the fuzzy information granulation (FIG) method, which is widely recognized as being able to distinguish between similar textures, a FIG-based classifier is introduced to classify the benign and malignant lung nodules. The clinical data set used for this research are a combination of 150 CT scans of LIDC and SPIE-APPM data sets. Also the LIDC data set is analyzed alone. The results show that the proposed method can be an innovative alternative to classify the benign and malignant nodules in the CT images.

Seismic Attribute Extraction and Application Based on the Gabor Wavelet Transform

Seismic Attribute Extraction and Application Based on the Gabor Wavelet Transform

Exploration of Visual Communication Design Methods Combining Virtual Reality Technology

Abstract As digital media technologies evolve, immersive experiences have increasingly become a focal point in the field of visual communication design. This paper explores the practical implications of integrating visual communication design with cutting-edge virtual reality (VR) technology and proposes a novel method rooted in VR. Our first innovative approach involves a virtual space reconstruction technique that leverages the central eye concept to mitigate discrepancies in user experiences due to varying perceptions of content. Subsequently, we employ the Gabor wavelet transform method to extract key visual features pertinent to human-computer interaction. This allows us to develop a perceptual model of visual targets in human-computer interactions, thereby enhancing the accuracy and efficacy of visual communication by grounding it in sensory cognition and user experience. In practical application, the visual communication design method proposed in this paper has a high signal-to-noise ratio (avg=43.407) and a short computing time (avg=2.24), and its overall recognition rate in multi-sensory virtual experience is as high as 95.6%. The study validates the viability of the virtual reality-based visual communication design method proposed in this paper and encourages the innovative development of visual communication design.

A novel automated system to detect breast cancer from ultrasound images using deep fused features with super resolution

Cancer patients can benefit from early detection and diagnosis. This study proposes a machine vision strategy for detecting breast cancer in ultrasound images and correcting several ultrasound difficulties such artifacts, speckle noise, and blurring. In quantitative evolution, edge preservation criteria were discovered to be superior to standard ones for hybrid anisotropic diffusion. A learnable super-resolution (SR) is inserted in the deep CNN to dig for extra possible information. The feature is fused with a pre-trained deep CNN model utilizing Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP). Machine learning (ML) techniques that are used to create these recommendation systems require well-balanced data in terms of class distribution, however most datasets in the real world are imbalanced. Imbalanced data forces a classifier to concentrate on the majority class while ignoring other classes of interest, lowering the predicted performance of any classification model. We propose a generative adversarial networks (GAN) strategy to overcome the data imbalance problem and improve the performance of recommendation systems in this research. Standard data is used to train this model, which assures a high level of resolution. In the testing phase, generalized data of varied resolution is used to evaluate the model's performance. It is discovered through cross-validation that a 5-fold method can successfully eliminate the overfitting problem. With an accuracy of 99.48 %, this suggested feature fusion technique indicates satisfactory performance when compared to current related works. Finally finding cancer region, researcher used U-Net architecture and extract cancer region from BC ultrasound images.