Speeded Up Robust Features Research Articles

A hybrid Grasshopper optimization algorithm for skin lesion segmentation and melanoma classification using deep learning

Skin cancer can be detected through visual examination and confirmed through dermoscopic analysis and various diagnostic tests. This is because visual observation enables early detection of unique skin images by artificial intelligence. Promising outcomes are shown by several Convolution Neural Network (CNN)–based skin lesion classification systems that employ tagged skin images. This study suggests a practical approach for identifying skin cancers using dermoscopy pictures, improving specialists' ability to distinguish benign from malignant tumors. The Swarm Intelligence (SI) approach used dermoscopy photographs to locate lesions on the skin areas Region of interest (ROI). The Grasshopper Optimization technique produced the best segmentation outcomes. The Speed-Up Robust Features (SURF) approach is applied to extract features based on these findings. Two groups were created using the ISIC-2017, ISIC-2018, and PH-2 databases to categorize skin tumors. With an estimated accuracy in classification of 98.52%, preciseness of 96.73%, and Matthews Correlation Coefficient (MCC) of 97.04%, the suggested classification and segmentation methodologies have been evaluated for classification efficacy, specificity, sensitivity, F-measure, preciseness, the MCC, the dice coefficient, and Jaccard's index. In every performance indicator, the method we suggest outperformed state-of-the-art methods.

SADCNN-ORBM: a hybrid deep learning model based citrus disease detection and classification

Citrus disease has a significant influence on agricultural productivity these days, so technology based on artificial intelligence has been developed for creating computer vision (CV) models. By spotting disease in its early stages and enabling necessary productivity actions, CV in agriculture improves the production of agricultural goods. In this paper, we developed a CV-based citrus disease detection model called the self-attention dilated convolutional neural network optimized restricted Boltzmann machine (SADCNN-ORBM) model, which consists of two crucial parts: a SADCNN for disease segmentation and an ORBM optimized by the self-adaptive coati optimization (SACO) algorithm to improve the classification performance of diseases, which successfully divides the disease type into three groups: anthracnose, melanose, and brown spot. Numerous feature sets, such as texture features, three-channel red, green, blue (RGB) features, local binary pattern (LBP) features, and speeded-up robust features (SURF) features, are combined and given as input into the classification layer in the proposed model. We compare our proposed model's performance with existing methods by using several evaluation metrics. The findings demonstrate the SADCNN-ORBM model's superiority in precisely recognizing and classifying citrus illnesses, outperforming all available techniques.

DIGITAL IMAGE RESTORATION USING SURF ALGORITHM

In contemporary times, the preservation of scientific and creative endeavours often relies on the utilization of film and image archives, hence emphasizing the significance of image processing as a critical undertaking. Image inpainting refers to the process of digitally altering an image in a manner that renders the adjustments imperceptible to a viewer lacking knowledge of the original image. Image inpainting is a technique mostly employed to restore damaged regions within an image by utilizing information obtained from matching characteristics in relevant images. This process involves filling in the damaged areas and removing undesired objects. The SURF (Speeded Up Robust Feature) algorithm under consideration is partitioned into three primary phases. Firstly, the essential characteristics of the impaired image and the pertinent image are identified. In the second stage, the relationship between the damaged image and the relevant image is determined in terms of translation, scaling, and rotation. Ultimately, the destroyed area is reconstructed through the application of the inverse transformation. The quality assessment of inpainted images can be evaluated using metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The experimental findings provide evidence that the suggested inpainting technique is effective in terms of both speed and quality.

Composite descriptor based on contour and appearance for plant species identification

In this paper, we propose an effective composite descriptor combining contour and appearance features for plant species identification. The composite descriptor uses two different fusion schemes: the first is a method based on the fusion of low-level local contour and appearance features; that is, we use local triangle features and local speeded-up robust features (SURF) to describe leaf images, respectively. Then, we utilize the Hausdorff distance to measure the leaf discrepancy to complete the plant identification task. The second is a method of global feature fusion based on high-level shape and appearance features. Specifically, the Fisher vector encodes the local triangle, and SURF features into high-level shape and appearance features. Then, the Euclidean distance is used to compute the dissimilarity of leaves to realize plant leaf recognition. The composite descriptor based on these two fusion schemes can well describe the characteristics of leaf images and improve the ability to identify plant leaves. This composite descriptor is extensively tested on several standard plant databases, including simple, compound, and natural scene plant leaf datasets. Experimental results demonstrate that the composite descriptor achieves high recognition accuracy, outperforming existing state-of-the-art benchmarks. In addition, we further applied our method directly to shape recognition and achieved good recognition results, demonstrating the wide range of applications of our method.

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures.

Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity through an advanced SHM system. Thus, this study proposes a single UAV for the seismic monitoring and safety assessment of linear infrastructures along with their computer vision-aided procedures. The proposed procedures were implemented in a full-scale shake-table test of a natural gas pipeline assembly. The objectives were to explore the UAV potential for the seismic vibration monitoring of linear infrastructures with the aid of several computer vision algorithms and to investigate the impact of parameter selection for each algorithm on the matching accuracy. The procedure starts by adopting the Maximally Stable Extremal Region (MSER) method to extract covariant regions that remain similar through a certain threshold of image series. The feature of interest is then detected, extracted, and matched using the Speeded-Up Robust Features (SURF) and K-nearest Neighbor (KNN) algorithms. The Maximum Sample Consensus (MSAC) algorithm is applied for model fitting by maximizing the likelihood of the solution. The output of each algorithm is examined for correctness in matching pairs and accuracy, which is a highlight of this procedure, as no studies have ever investigated these properties. The raw data are corrected and scaled to generate displacement data. Finally, a structural safety assessment was performed using several system identification models. These procedures were first validated using an aluminum bar placed on an actuator and tested in three harmonic tests, and then an implementation case study on the pipeline shake-table tests was analyzed. The validation tests show good agreement between the UAV data and reference data. The shake-table test results also generate reasonable seismic performance and assess the pipeline seismic safety, demonstrating the feasibility of the proposed procedure and the prospect of UAV-based SHM for linear infrastructure monitoring.

Enhancing plant leaf disease detection: Integrating krill herd optimization-surf features and deep belief network

A plant disease that attacks the leaf causes significant yield and market value losses. A professional plant pathologist should be able to visually identify the disease by looking at the affected plant leaves, but this is unlikely to result in a more accurate diagnosis. Disease symptoms should be immediately recognisable in order to stop the spread of the illness. To find plant diseases, steps should be taken using computer assisted technologies. Numerous methods for identifying plant diseases using machine learning (ML) and deep learning (DL) have been developed and tested in numerous studies. Machine learning has the disadvantages of having a small dataset, taking longer, and requiring more time for results interpretation. Deep learning is suggested as a solution to this. This study compares the effectiveness of both ML&DL for plant leaf disease identification with more recent investigations. The common deep learning technique involves utilising the Krill Herd Optimisation Algorithm (KHO) to segment images and the Speeded up Robust Features (SURF) to extract the images. The Artificial Bee Colony (ABC) then chooses the features. Then, a Deep Belief Network (DBN) can be used to classify the chosen image. Multiple diseases can be identified on the same leaf using this method. This study demonstrates that deep learning outperforms machine learning in terms of results. The outcome demonstrates that the deep learning method is superior for the diagnosis of plant disease when there is sufficient data available. Using this technique, the validity and consistency were also examined.

A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions

Copy-Move Forgery (CMF) is a common form of image manipulation attack that involves copying and pasting a part of an image to another position within the same image. This study proposes a Deep Learning (DL) model for detecting CMF, particularly in the presence of various malicious attacks. The proposed approach involves several steps, including converting the input image to grayscale, preprocessing the image using the Simple Linear Iterative Clustering (SLIC) algorithm to generate superpixel partitions, and then extracting keypoint features using the Speeded Up Robust Features (SURF) detector. Finally, a Generative Adversarial Network (GAN) is employed for feature description and matching. To assess the effectiveness of the approach, the types of features used for copy-move forgery were addressed. The proposed approach was examined under rotation, blurring, jpg compression, and scaling attacks. Furthermore, experimental results showed that the proposed approach can detect multiple CMFs with high accuracy. Finally, the proposed method was compared with recent state-of-the-art methods.

Navigating Unstructured Space: Deep Action Learning-Based Obstacle Avoidance System for Indoor Automated Guided Vehicles

Automated guided vehicles (AGVs) have become prevalent over the last decade. However, numerous challenges remain, including path planning, security, and the capacity to operate safely in unstructured environments. This study proposes an obstacle avoidance system that leverages deep action learning (DAL) to address these challenges and meet the requirements of Industry 4.0 for AGVs, such as speed, accuracy, and robustness. In the proposed approach, the DAL is integrated into an AGV platform to enhance its visual navigation, object recognition, localization, and decision-making capabilities. Then DAL itself was introduced to combine the work of You Only Look Once (YOLOv4), speeded-up robust features (SURF), and k-nearest neighbor (kNN) and AGV control in indoor visual navigation. The DAL system triggers SURF to differentiate two navigation images, and kNN is used to verify visual distance in real time to avoid obstacles on the floor while searching for the home position. The testing findings show that the suggested system is reliable and fits the needs of advanced AGV operations.

Synergy of digital economy and green economy in sustainable development policy

<p>Sustainability challenges and ICT perspectives are at the heart of current thinking on global economic and social development. The current development and growth process is based on unsustainable foundations due to irresponsible resource consumption and negative environmental impacts as well as greenhouse gas emissions. People need to find ways to integrate the digital economy and the sustainability of the green economy. Therefore, this paper firstly describes the intersection of digital economy and green economy, secondly introduces the security system of digital economy and green economy, then based on this, the SURF (Speeded-up robust features) algorithm is used to locate and improve the data aggregation system of digital economy and green economy, and finally, the algorithm simulation experiment is conducted. The experimental results found that the aggregation algorithm based on digital economy and green economy has 19% higher accuracy than the traditional algorithm. At the same time, the calculation speed is increased by three to five times. The above results show that the SURF algorithm is applied to the sustainable development research of digital economy and green economy with significant effect.</p>

Convex-based lightweight feature descriptor for Augmented Reality Tracking.

Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. We have developed two filters capable of computing pixel intensity variations, followed by the covariance matrix of the polynomial to describe the features. The superiority of CBFD is validated through precision, recall, computation time, and feature location distance. Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. Noteworthy is CBFD's recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. Finally, the plot of location feature distance illustrates that CBFD exhibits minimal distance compared to DITF and Histogram of Oriented Gradients (HOG). These results highlight the speed and robustness of CBFD across various transformations.

A Novel Secured Deep Learning Model for COVID Detection Using Chest X-Rays

Automatic detection of a medical disease is a need of the hour as it helps doctors diagnose diseases and provide fast medical reports. COVID-19 is a deadly disease for which an automated detection system may be helpful. This study proposes a unique hybrid deep learning model, COVIDet, based on CNN and the speeded-up robust features (SURF) extraction approach to diagnose COVID-19 using chest x-ray images. SURF is utilized in this work to extract features, and the output is then transferred to a 25-layer CNN for detection using the extracted features. This investigation employed 4623 COVID-19 positive X-ray pictures or 8055 total. The suggested hybrid model also contrasts with the study's VGG19, Resnet50, Inception, Xception, and traditional CNN models. The proposed model had a 98.01% accuracy, a 97.03% F1-score, a 98.65% sensitivity, a 99% precision, and a 95.65% specificity. The proposed model can be further improved when more datasets are available and can help doctors to diagnose patients quickly and efficiently. Using chest X-ray pictures, a secured web application is also developed to identify COVID-19. The user sends the application a chest X-ray image, and in return, it determines whether an individual is COVID-19 positive or not, cutting down on testing time. In Covid times, when people are standing in long queues and waiting for their turns, this application would greatly help. The application uses the pre-trained COVIDet model in the backend.

A Novel Approach for Single-Shot Target Recognition by Matching Graph of Feature Pixels

Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of artificial intelligence. Since last two decades, different techniques for object recognition were given by researchers from across the world. This paper presents a novel approach for automatic target recognition in autonomous weapons by matching connected graph of feature pixels. The Moving and Stationary Target Acquisition and Recognition (MSTAR) is taken for experimental purpose. The Proposed model has three stages for a target recognition; Image Denoising, Feature extraction & graph generation, and feature matching. Firstly, a sample image of targets taken from the MSTAR is taken as an input. Further, input sample is de-noised and features are extracted using Speeded-Up Robust Features (SURF) algorithm. Thereafter, a graph is created using feature pixels and this complete process is repeated with test image. Further, both graphs are compared using a proposed feature graph matching algorithm which results an recognition accuracy score. Proposed model is tested over the MSTAR dataset and it resulted in varying accuracy at different feature matching thresholds.

InSAR Simulation and Speeded-Up Robust Features Algorithm for Terrain Relative Navigation in PSRs on the Moon

InSAR Simulation and Speeded-Up Robust Features Algorithm for Terrain Relative Navigation in PSRs on the Moon

An Efficient Video Frames Retrieval System Using Speeded Up Robust Features Based Bag of Visual Words

Most studies in content-based image retrieval (CBIR) systems use database images of multiple classes. There is a lack of an automatic video frame retrieval system based on the query image. Low-level features i.e., the shape and colors of most of the objects are almost the same e.g., the sun and an orange are both round and red in color. Features such as speeded up robust features (SURF) used in most of the content-based video retrieval (CBVR) & CBIR research work are non-invariant features which may affect the overall accuracy of the CBIR system. The use of a simple and weak classifier or matching technique may also affect the accuracy of the CBIR system on high scale. The unavailability of datasets for content-based video frames retrieval is also a research gap to be explored in this paper.

An automated detection and classification of brain tumor from MRIs using Water Chaotic Fruitfly Optimization (WChFO) based Deep Recurrent Neural Network (DRNN)

AbstractDue to the complexity of the images and dearth of anatomical models, it is highly difficult to accurately represent the various deformations in each component of the medical images. In recent years, a significant number of children and adults have affected from brain tumors, which is one of the most terrible types of disease affects the people around the world. Moreover, the Magnetic Resonance Imaging (MRI) based brain tumor detection is one of a significant study area in the field of medical imaging. Since, the use of computerized methods aids in the detection and treatment of disease by the medical professionals. The development of an automated method for the accurate detection and classification of tumors from brain MRIs. In this framework, a tanh normalization process is used to smooth out the input brain MRIs with less noise artefacts and improved quality. Then, a group feature extraction model is used to extract the relevant features from the normalized image, which includes both Speeded Up Robust Features (SURF) and Grey Level Co-occurrence Matrix (GLCM) features. The Water Chaotic Fruitfly Optimization (WChFO) method is used to identify the best features for increasing the speed of classifier training and testing processes with less time. Moreover, a Deep Recurrent Neural Network (DRNN) model is used to classify the type of brain tumor for accurate early diagnosis and treatment. The most well-known benchmarking datasets, like BRATS and Kaggle, employed for analysis in order to assess the effectiveness and results of the proposed brain tumor diagnosis system. By using the proposed WChFO-DRNN technique, the accuracy of the tumor detection system is increased to 99.2% with the sensitivity, specificity of 99% and time consumption of 0.2s.

A Rigid Image Registration by Combined Local Features and Genetic Algorithms

Abstract Image registration is an essential pre-processing step required for many image processing applications such as medical imaging and computer vision. The aim is to geometrically align two or more images of the same scene by establishing a mapping that relies on each point from one image to its corresponding point of another image. Scale invariant feature transform (SIFT) and speeded up robust features (SURF) are well-liked local features descriptors that have been extensively utilised for feature-based image registration due to their inherent properties such as invariance, changes in illumination, and noise. Moreover, the task of registration can be viewed as an optimization problem that can be solved by applying genetic algorithms (GAs). This paper presents an efficient feature image registration method based on combined local features and GAs. Firstly, the procedure consists of extracting the local features from the images by combining SIFT and SURF algorithms and matching them to refine the feature set data. Therefore, an adaptive GA based on fitness sharing and elitism techniques is employed to find the optimal rigid transformation parameters that best align the feature points by minimizing a distance metric. The suggested method is applied for registering medical images and the obtained results are significant compared to other feature-based approaches with reasonable computation time.

New approach to similarity detection by combining technique three-patch local binary patterns (TP-LBP) with support vector machine

<p>Recognition systems have received a lot of attention because of their various<br />uses in people's daily lives, for example in robotic intelligence, smart cameras,<br />security surveillance or even criminal identification. Determining the<br />similarity of faces by different face variations is based on robust algorithms.<br />The validation of our experiment is done on two sets of data. In this paper, we<br />compare two facial recognition system techniques according to the<br />recognition rate and the average authentication time: in order to increase the<br />accuracy rate and decrease the processing time. our approach is based on<br />feature extraction by two algorithms principal components analysis scaleinvariant feature transform (PCA-SIFT) and speeded up robust features (SURF), then uses the random sample consensus (RANSAC) technique to cancel outliers. Finally, face recognition is established on the basis of proximity determination. The second technique is based on the association of support vector machine (SVM) classifier with the key point recovery technique. the results obtained by the second technique is better for both databases: The recognition rate of the base olivetti research laboratory (ORL) should be 98.125800 and that of the Grimace base 97.2851500. The evaluation according to the time of the second technique does not exceed 300ms on average.</p>

Automated Plant Disease Diagnosis Using Deep Learning Model

With the world's population growing, it is vital that food crops and medicinal plants are required to be developed in large quantity. A model that can help us understand the process more comprehensively will be explored. Based on a thorough understanding, we are exploring a model that will enable us to extract the precise information and in-depth knowledge we require. There are many plants that contract lethal diseases every year, including food plants. This reduces their output rates. We must employ automated methods to locate the issue inside the facility if we do not want manufacturing prices to rise. The detection of plant diseases can be automated through robotics and image processing. As a result of recent technological advancements, we now have the ability to simplify our artwork. A deep learning and image processing approach can increase the efficiency of detection processes. A great deal of progress has been made in diagnosing plant diseases. An artificial intelligence model was trained to assist in diagnosing flora disorders. Based on the photo information (leaf), the model can be controlled. Identifying the problem and discovering the condition in this study, we propose the use of computer vision technology combined with fuzzy logic to detect and grade leaf diseases. GLCM is performed to extract texture features, and fuzzy logic is applied to grade the disease. K-means clustering is applied for determining defected areas; GLCM is used to determine defected areas; and fuzzy logic is used for diagnosing diseases. About 70% of classifications are accurate according to the model. By using Speed Up Robust Features (SURF), DENSE, and Bag of Visual Words (BOVW) in addition to the global features, the accuracy of the system can be enhanced. A treatment program will help people better understand the illness if one is offered.

Enhancement Panoramic Image Based on Principal Component Analysis (PCA) and SURF Algorithm

Image upgrade is essential for giving improved data to other image handling methods. Also, the requirement for differentiation improvement to all-encompassing image is a usually performed examination by dental specialists and oral specialists in regular practice and is a critical indicative apparatus. It covers a more extensive zone than a traditional intraoral x-beam and, subsequently, gives important data about the maxillary sinuses, tooth situating and other bone variations from the norm. Thusly, it is anything but difficult to distinguish and perceive valuable data. In this pursuit was utilized the calcu lation of Principle Component Analysis (PCA) to upgrade the subtle elements of images and enhance the quality the image, by its joining with strategy for difference improvement, and highlights extraction that take from the upgrade technique to limit mistakes in images, along these lines enhance the execution of the framework, image upgrade strategies used to deliver more reasonable images for identification and extraction. Three principle computerized image process strategies: Contrast Stretching, Histogram Equalization and, Contrast Limited Adaptive Histogram Equalization , were considered to build the improvement image, and evacuat e clamor, separately however low quality and the image is light angle in hues and less clear . In this exploration we will, pre-prepared images were assessed utilizing current image recognition and highlight extraction techniques Speeded Up Robust Features (SURF) calculations after go in improve ment strategies. After that PCA embed the yield vector from SURF. Gotten results demonstrated that upgrade image expa nded after number of separated highlights and diminished mistakes amid info image in PCA. Therefore, the impacts of var ious pre-process procedures were thought about in the paper and the assessment parameters are figured which demonstrate that proposed work gives better execution from the image broke down under PSNR to enhance the dependability of proposed strategy from the outcomes.

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Abstract Epilepsy is one of the most debilitating neurological diseases that abruptly alters a person’s way of life. Manual diagnosis is a laborious and time-consuming task prone to human error. Therefore, automating this task by developing an intelligent system is necessary. Existing deep learning (DL) models require high training time, large datasets, and machines with more memory and processing power. In addition, owing to the black-box nature of DL models, no one can determine the features that the network prefers for classification decisions. To overcome these challenges, this study proposes an accurate, automatic, and fast-intelligent system for epilepsy detection using a computer-aided diagnosis (CAD) -two-dimensional machine learning (ML) framework. Existing ML models struggle to produce reliable and acceptable diagnostic results owing to the low amplitude and nonstationary nature of electroencephalograms (EEGs), particularly in clinical situations where environmental influences are almost impossible to eliminate. The proposed model was built using the Children’s Hospital Boston and the Massachusetts Institute of Technology dataset, and represents the first study that employs the speeded-up robust feature (SURF) bag of features technique for this application, which generates local features from spectrogram images of the respective one-dimensional EEG signal inputs. In addition, DL features were extracted from the spectrogram images for model performance comparison. Both features were used separately to train the ML classifiers. Implementing SURF offers fast computation and makes the model invariant to distortions, noise, scaling, and so on. Therefore, the proposed model is more suitable for real-time applications, and this ML framework provides an enhanced accuracy of 99.78% compared to the support vector machine-RBF classifier, along with 99.56% sensitivity, 100% specificity, and an error rate of 0.22%. The higher detection accuracy demonstrates the effectiveness of the proposed framework for medical disease diagnosis applications.