Speeded Up Robust Features Research Articles

Skin feature point tracking using deep feature encodings

AbstractFacial feature tracking is a key component of imaging ballistocardiography (BCG) where accurate quantification of the displacement of facial keypoints is needed for good heart rate estimation. Skin feature tracking enables video-based quantification of motor degradation in Parkinson’s disease. While traditional computer vision algorithms like Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Lucas-Kanade method (LK) have been benchmarks due to their efficiency and accuracy, they often struggle with challenges like affine transformations and changes in illumination. In response, we propose a pipeline for feature tracking, that applies a convolutional stacked autoencoder to identify the most similar crop in an image to a reference crop containing the feature of interest. The autoencoder learns to represent image crops into deep feature encodings specific to the object category it is trained upon. We train the autoencoder on facial images and validate its ability to track skin features in general using manually labelled face and hand videos of small and large motion recorded in our lab. Our evaluation protocol is comprehensive, including quantification of errors in human annotation. The tracking errors of distinctive skin features (moles) are so small that we cannot exclude the fact that they stem from the manual labelling based on a $$\chi ^2$$ χ 2 -test. With a mean error of 0.6–3.3 pixels, our method outperformed the other methods in all but one scenario. More importantly, our method was the only one that did not diverge. We also compare our method with the latest state-of-the-art transformer for feature matching by Google—Omnimotion. Our results indicate that our method is superior at tracking different skin features under large motion conditions and that it creates better feature descriptors for tracking, matching, and image registration compared to both traditional algorithms and the latest Omnimotion.

A Zero-Watermarking Algorithm Based on Vortex-like Texture Feature Descriptors

For effective copyright protection of digital images, this paper proposes a zero-watermarking algorithm based on local image feature information. The feature matrix of the algorithm is derived from the keypoint set determined by the Speeded-Up Robust Features (SURF) algorithm, and it calculates both the gradient feature descriptors and the vortex-like texture feature (VTF) descriptors of the keypoint set. Unlike traditional texture feature descriptors, the vortex-like texture feature descriptors proposed in this paper contain richer information and exhibit better stability. The advantage of this algorithm lies in its ability to calculate the keypoints of the digital image and provide a stable vector description of the local features of these keypoints, thereby reducing the amount of erroneous information introduced during attacks. Analysis of experimental data shows that the algorithm has good effectiveness, distinguishability, security, and robustness.

Estimating the drift velocity of plasma bubbles in airglow images using the scale invariant feature transform and the speeded up robust feature algorithms

Equatorial plasma bubbles (EPBs) are ionospheric irregularities that can significantly impact radio communication, causing signal scintillation that leads to signal loss and error in position calculations. In this study, we introduced a new technique to estimate the eastward and northward drift velocities of the EPB from ASI data by tracking the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Features (SURF) points. The technique has been tested by studying multiple EPBs events detected over the Brazilian sector during the autumn equinox, spring equinox and summer solstice. The chosen events occurred during quiet nights (Kp index ≤ 3) and were observable through two All-Sky imager (ASI) stations. Moreover, the study shows the performance of the technique under different circumstances. It achieves optimal performance on clear-sky nights; however, cloud presence within the ASI field of view increases the error within the estimated values. The estimated velocities were found to be within the range reported by previous studies in the Brazilian sector.

Comparative Analysis of Image Stitching Algorithms for Bridge Inspection Imaging Systems

This paper discusses an innovative vehicle-mounted device designed for efficient inspection of bridge undersides, aiming to address the challenges of manual inspections, such as being labor-intensive and inefficient. The device uses high-precision cameras to capture detailed images of bridge bottoms, facilitating a thorough condition assessment. It evaluates three feature point extraction algorithms for image stitching—Scale-Invariant Feature Transform (SIFT), Harris corner detection, and Speeded Up Robust Features (SURF)—to create complete visual representations of bridge undersides. Field tests on a prefabricated I girder bridge demonstrated the device's effectiveness in gathering and stitching images, with the SIFT algorithm performing best for girder bottoms, Harris corner algorithm for web and flange surfaces, and SURF for rapid image processing. This research provides a foundation for the rapid identification of visible defects on bridge superstructures, significantly benefiting bridge maintenance and safety evaluations.

Comparative Analysis for Bag of Features (BoF) Performance

The accuracy of the Bag of Features (BoF) is greatly affected by the discriminatory power of feature extraction techniques. This paper presented a proposed method designed to find the best features technique for constructing a BoF model according to the image classification. It consists of four stages: feature extraction, where detectors and descriptor feature techniques have been exploited to generate different BoF models. Each BoF model is generated depending on what detector and descriptor are used. The BoF models are constructed to represent the images as feature vectors. The classification process is then performed on two image datasets. Finally, the efficiency of BoF models is analyzed and evaluated with respect to the accuracy of their classification performance. Experimental results indicated that the best level of accuracy was provided by the proposed BoF model with the KAZE features method. The results also showed that the BoF model with Speeded Up Robust Features (SURF) was superior to other feature methods in terms of execution time, which was 0.01218 seconds. Moreover, the BoF model generated by the SURF detector combined with the KAZE descriptor achieved a high level of accuracy of 0.99 and kept the time complexity low (0.01948 seconds).

RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing.

The aim and objective of the research are to develop an automated diagnosis system for the prediction of rheumatoid arthritis (RA) based on artificial intelligence (AI) and quantum computing for hand radiographs and thermal images. The hand radiographs and thermal images were segmented using a UNet++ model and color-based k-means clustering technique, respectively. The attributes from the segmented regions were generated using the Speeded-Up Robust Features (SURF) feature extractor and classification was performed using k-star and Hoeffding classifiers. For the ground truth and the predicted test image, the study utilizing UNet++ segmentation achieved a pixel-wise accuracy of 98.75%, an intersection over union (IoU) of 0.87, and a dice coefficient of 0.86, indicating a high level of similarity. The custom RA-X-ray thermal imaging (XTNet) surpassed all the models for the detection of RA with a classification accuracy of 90% and 93% for X-ray and thermal imaging modalities, respectively. Furthermore, the study employed quantum support vector machine (QSVM) as a quantum computing approach which yielded an accuracy of 93.75% and 87.5% for the detection of RA from hand X-ray and thermal images. In addition, vision transformer (ViT) was employed to classify RA which obtained an accuracy of 80% for hand X-rays and 90% for thermal images. Thus, depending on the performance measures, the RA-XTNet model can be used as an effective automated diagnostic method to diagnose RA accurately and rapidly in hand radiographs and thermal images.

A Novel Method for Heat Haze-Induced Error Mitigation in Vision-Based Bridge Displacement Measurement.

Vision-based techniques have become widely applied in structural displacement monitoring. However, heat haze poses a great threat to the precision of vision systems by creating distortions in the images. This paper proposes a vision-based bridge displacement measurement technique with heat haze mitigation capability. The properties of heat haze-induced errors are illustrated. A dual-tree complex wavelet transform (DT-CWT) is used to mitigate the heat haze in images, and the speeded-up robust features (SURF) algorithm is employed to extract the displacement. The proposed method is validated through indoor experiments on a bridge model. The designed vision system achieves high measurement accuracy in a heat haze-free condition. The proposed mitigation method successfully corrects 61.05% of heat haze-induced errors in static experiments and 95.31% in dynamic experiments.

FAULT DETECTION AND CLASSIFICATION USING DENSENET, SURF-BASED ANN, AND INFRARED THERMOGRAPHY

The reliability and efficiency of electric motors are critical in industrial applications, where unexpected faults can lead to costly downtime and safety hazards. Traditional fault detection methods often require extensive manual inspection and may not capture subtle anomalies in motor behavior. Infrared thermography has emerged as a non-invasive technique to detect temperature variations in motor components, which can indicate potential faults. However, the challenge lies in accurately classifying these faults to prevent failures. Current methods lack the precision needed to classify various motor faults accurately and quickly, especially when dealing with complex thermal patterns. The integration of advanced deep learning architectures with feature extraction techniques presents an opportunity to enhance the detection and classification of motor faults. This study proposes a hybrid model combining DenseNet, a deep learning architecture known for its high performance in image analysis, with a Speeded-Up Robust Features (SURF)-based Artificial Neural Network (ANN) for feature extraction and classification. Infrared thermography images of motors were first processed through DenseNet for initial feature extraction. The SURF algorithm further refined these features, which were then classified using ANN. The model was trained and validated on a dataset of infrared thermography images, representing various motor fault conditions, including bearing wear, misalignment, and insulation failure. The proposed model achieved an overall accuracy of 98.7% in detecting and classifying motor faults, outperforming traditional methods by 5.2%. The model also demonstrated high sensitivity (97.8%) and specificity (99.1%) in identifying subtle temperature variations indicative of early-stage faults. These results highlight the effectiveness of the DenseNet and SURF-based ANN approach in enhancing the reliability of motor fault detection using infrared thermography.

Close-range imaging for green roofs: Feature detection, band matching, and image registration for mixed plant communities

Green roofs offer ecological benefits but harsh rooftop environment stressors like heat, water scarcity, and wind can limit optimal plant growth. Monitoring plant health is crucial for optimizing green roof performance and remote sensing provides a non-destructive approach, yet challenges persist in tight, urban settings. This allows for close-range ground monitoring as a viable solution in these emerging environments. Recent studies explored automatic image registration techniques, showing promise in homogeneous settings, but faced uncertainties in mixed species systems or early plant growth stages. Feature detection techniques such as Speeded up Robust Features (SURF) using fixed and moving image spaces have been proposed for alignment. This study aimed to assess feature detection, matching, and image registration techniques in mixed species plant communities on green roofs throughout the growing season. This study developed a sophisticated monitoring system using close-range multispectral sensors for aligning bands in mixed species plant communities, contributing to enhanced green roof management and sustainability. The results of the study showed no significant differences in band alignment accuracy between growth stages for mixed species bush bean/sedum and single species sedum modules, but significant differences were found in single species bush bean systems. Additionally, mixed species modules showed better accuracy compared to single species bush bean modules, indicating that heterogenous systems provide better alignment accuracy due to more diverse feature extractions.

HYBRID OPTIMIZATION-ENABLED FUNCTIONAL CONNECTIVITY-BASED PIVOTAL REGION EXTRACTION AND TRANSFER LEARNING FOR AUTISM SPECTRUM DISORDER DETECTION

Autism Spectrum Disorder (ASD) is a neuro development-based disability caused by variations in the brain which occur during the early stages of human life. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. However, diagnosing ASD is difficult due to its complex symptoms as well as an inadequate number of neurobiological indications. This paper aims to detect ASD based on pivotal region extraction and Feedback-Henry Jellyfish Optimization-Convolution Neural Network with Transfer Learning (FHJO_CNN with TL) is developed in this work. Here, an autistic brain image is acquired from the data sample and it is fed to the pre-processing stage. By utilizing the Kalman filter and Region of Interest (RoI), selection pre-processing is done. After that, functional connectivity-based pivotal region extraction is performed based on the proposed FHJO, where the FHJO is an incorporation of Feedback-Henry Gas Optimization (FHGO) and Jelly Fish (JS) algorithms, where, FHGO is the combination of Henry Gas Solubility Optimization (HGSO), and Feedback Artificial Tree (FAT) algorithm. Thereafter in feature extraction, features like Local Binary Pattern (LBP), and Speeded Up Robust Features (SURF), statistical features are excerpted. Finally, ASD classification is done utilizing the proposed FHJO_CNN with TL. The performance of the proposed method is evaluated using the ACERTA-ABIDE dataset and the evaluation reveals that FHJO_CNN with TL attained values of accuracy, sensitivity and specificity which are 94.08%, 95.09%, and 93.59% respectively.

Fast and robust feature-based stitching algorithm for microscopic images

The limited field of view of high-resolution microscopic images hinders the study of biological samples in a single shot. Stitching of microscope images (tiles) captured by the whole-slide imaging (WSI) technique solves this problem. However, stitching is challenging due to the repetitive textures of tissues, the non-informative background part of the slide, and the large number of tiles that impact performance and computational time. To address these challenges, we proposed the Fast and Robust Microscopic Image Stitching (FRMIS) algorithm, which relies on pairwise and global alignment. The speeded up robust features (SURF) were extracted and matched within a small part of the overlapping region to compute the transformation and align two neighboring tiles. In cases where the transformation could not be computed due to an insufficient number of matched features, features were extracted from the entire overlapping region. This enhances the efficiency of the algorithm since most of the computational load is related to pairwise registration and reduces misalignment that may occur by matching duplicated features in tiles with repetitive textures. Then, global alignment was achieved by constructing a weighted graph where the weight of each edge is determined by the normalized inverse of the number of matched features between two tiles. FRMIS has been evaluated on experimental and synthetic datasets from different modalities with different numbers of tiles and overlaps, demonstrating faster stitching time compared to existing algorithms such as the Microscopy Image Stitching Tool (MIST) toolbox. FRMIS outperforms MIST by 481% for bright-field, 259% for phase-contrast, and 282% for fluorescence modalities, while also being robust to uneven illumination.

Crack Detection of Curved Surface Structure Based on Multi-Image Stitching Method

The crack detection method based on image processing has been a new achievement in the field of civil engineering inspection in recent years. Column piers are generally used in bridge structures. When a digital camera collects cracks on the pier surface, the loss of crack dimension information leads to errors in crack detection results. In this paper, an image stitching method based on Speed-Up Robust Features (SURFs) is adopted to stitch the surface crack images captured from different angles into a complete crack image to improve the accuracy of the crack detection method based on image processing in curved structures. Based on the proposed method, simulated crack tests of vertical, inclined, and transverse cracks on five different structural surfaces were conducted. The results showed that the influence of structural curvature on the measurement results of vertical cracks is very small and can be ignored. Nevertheless, the loss of depth information at both ends of curved cracks will lead to errors in crack measurement outcomes, and the factors that affect the precision of crack detection include the curvature of the surface and the length of the crack. Compared with inclined cracks, the structural curvature significantly influences the measurement results of transverse cracks, especially the length measurement results of transverse cracks. The image stitching method can effectively reduce the errors caused by the structural curved surface, and the stitching effect of three images is better than that of two images.

A Robust Coverless Image Steganography Algorithm Based on Image Retrieval with SURF Features

With the advancement of image steganography, coverless image steganography has gained widespread attention due to its ability to hide information without modifying the carrier of images. However, existing coverless image steganography methods often require both communicating parties to transmit an amount of additional information including image blocks’ locations or a large number of parameters, which will raise a serious suspicion. In light of this issue, we propose a robust coverless image steganography algorithm based on Speeded-Up Robust Features (SURF). Firstly, the proposed method allows both communicating parties to independently create multiple coverless image datasets (CIDs) using random seeds. Then, a mapping rule is designed for creating one-to-one correspondence between hash sequences and images in CIDs. Finally, the secret information will be carried by the images whose hash sequences are equal to the secret segments. At the receiver side, the robust SURF of images is utilized to retrieve the secret information. Experimental results demonstrate that the proposed algorithm outperforms other methods in terms of capacity, robustness, and security. Furthermore, it is worth noting that the proposed method eliminates the need to transmit a large amount of additional information, which is a significant security issue in existing coverless image steganography algorithms.

Object recognition based on shape interest points descriptor

AbstractConsidering the defect of object recognition with single global feature or local feature, in this letter the authors propose a shape interest points descriptor (SIPD) for object recognition. Particularly, the authors extract the shape features by the improved HU moments and then interest points by Speeded up Robust Feature (SURF). Object recognition is carried out by similarity measure. Because the influence of the improved HU moments and SURF for object recognition is different, the two different similarity measures are fused effectively by using different weight factors. Experimental results show that the author’ proposed method is effective and robust to the changes of scale, viewing angle, illumination and noise when compared with the other representative methods.

Visual place recognition for autonomous mobile robot navigation using LoFTR and MAGSAC++

Autonomous mobile robots are defined as robotic entities capable of independent movement and intelligent decision-making, relying on their ability to perceive and analyze their surroundings, including objects in their environment. In Simultaneous Localization and Mapping (SLAM) systems, loop closure is often achieved through visual place recognition techniques, where the system compares the current visual input with previously observed scenes to identify matches. In computer vision applications, Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) are popular feature extraction algorithms used for such as key point detection, matching, and image registration tasks. The choice of inlier threshold should be based on the specific characteristics of the application and the nature of the images being processed. It often requires experimentation and tuning to find the optimal balance between robustness and accuracy. It Utilizes the pre-trained Local Feature Transformer (LoFTR) and MAGSAC++ estimator to address these drawbacks by employing the number of inliers to determine the similarity between two images for visual place recognition. Our experiment demonstrates that the number of inliers can determine the similarity of locations between two images. Scale variations and translation in location significantly influence the resulting number of inliers. Comparing images from the same location and from different locations yields varying numbers of inliers. The number of inliers significantly influences the similarity of locations. At the same location, the number of inliers is above 150, while at different locations, the number is below 150.

Analysis of Music Brand Similarity Levels Using a Visual Computing Approach

Currently, many trademarks have emerged, especially for music equipment products circulating in the community. Especially in Indonesia, musical instrument brands with various models have emerged. The problem occurs when the brand is considered to resemble the original brand, which makes it uncomfortable for musicians. Even though the quality of the tone produced is not assessed from a brand perspective, it is felt by brand owners to be quite a violation of the code of ethics. This has an impact on marketing products that are considered genuine. In this paper, a concept will be proposed in determining whether a trademark is considered authentic in terms of the logo. Use of Visual Computing with SIFT and SURF algorithms. SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features) are two popular algorithms for feature extraction and matching in image processing. Both are frequently used in computer vision applications such as object detection, image matching, and object recognition. The results of this analysis will be used for musicians who want to buy musical equipment to be able to detect the logo to compare the success of a musical instrument product. The appropriateness of the quality itself will be discussed in a different discussion. The benefits that can be generated will make musicians, especially in Indonesia, more conscientious. The possibility of sorting so that you can like your own product will be very possible.

ACCURACY ASSESSMENT OF THE EFFECT OF DIFFERENT FEATURE DESCRIPTORS ON THE AUTOMATIC CO-REGISTRATION OF OVERLAPPING IMAGES

This research seeks to assess the effect of different selected feature descriptors on the accuracy of an automatic image registration scheme. Three different feature descriptors were selected based on their peculiar characteristics, and implemented in the process of developing the image registration scheme. These feature descriptors (Modified Harris and Stephens corner detector (MHCD), the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Feature (SURF)) were used to automatically extract the conjugate points common to the overlapping image pairs used for the registration. Random Sampling Consensus (RANSAC) algorithm was used to exclude outliers and to fit the matched correspondences, Sum of Absolute Differences (SAD) which is a correlation-based feature matching metric was used for the feature match, while projective transformation function was used for the computation of the transformation matrix (T). The obtained overall result proved that the SURF algorithm outperforms the other two feature descriptors with an accuracy measure of -0.0009 pixels, while SIFT with a cumulative signed distance of 0.0328 pixels also proved to be more accurate than MHCD with a cumulative signed distance of 0.0457 pixels. The findings affirmed the importance of choosing the right feature descriptor in the overall accuracy of an automatic image registration scheme.

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.