Search Results (573)

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks. Full article

Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate. Full article

This paper proposes a hybrid method for skin lesion classification combining deep learning features with conventional descriptors such as HOG, Gabor, SIFT, and LBP. Feature extraction was performed by extracting features of interest within the tumor area using suggested fusion methods. We tested and compared features obtained from different deep learning models coupled to HOG-based features. Dimensionality reduction and performance improvement were achieved by Principal Component Analysis, after which SVM was used for classification. The compared methods were tested on the reference database skin cancer-malignant-vs-benign. The results show a significant improvement in terms of accuracy due to complementarity between the conventional and deep learning-based methods. Specifically, the addition of HOG descriptors led to an accuracy increase of 5% for EfficientNetB0, 7% for ResNet50, 5% for ResNet101, 1% for NASNetMobile, 1% for DenseNet201, and 1% for MobileNetV2. These findings confirm that feature fusion significantly enhances performance compared to the individual application of each method. Full article

Image matching between spaceborne synthetic aperture radar (SAR) images are frequently interfered with by speckle noise, resulting in low matching accuracy, and the vast coverage of SAR images renders the direct matching approach inefficient. To address this issue, the study puts forward a multi-scale adaptive improved SAR image block matching method (called STSU–SAR–SIFT). To improve accuracy, this method addresses the issue of the number of feature points under different thresholds by using the SAR–Shi–Tomasi response function in a multi-scale space. Then, the SUSAN function is used to constrain the effect of coherent noise on the initial feature points, and the multi-scale and multi-directional GLOH descriptor construction approach is used to boost the robustness of descriptors. To improve efficiency, the method adopts the main and additional image overlapping area matching method to reduce the search range and uses multi-core CPU+GPU collaborative parallel computing to boost the efficiency of the SAR–SIFT algorithm by block processing the overlapping area. The experimental results demonstrate that the STSU–SAR–SIFT approach presented in this paper has better accuracy and distribution. After the algorithm acceleration, the efficiency is obviously improved. Full article

Multi-modal remote sensing images often exhibit complex and nonlinear radiation differences which significantly hinder the performance of traditional feature-based image registration methods such as Scale-Invariant Feature Transform (SIFT). In contrast, structural features—such as edges and contours—remain relatively consistent across modalities. To address this challenge, we propose a novel multi-modal image registration method, Cof-SIFT, which integrates a co-occurrence filter with SIFT. By replacing the traditional Gaussian filter with a co-occurrence filter, Cof-SIFT effectively suppresses texture variations while preserving structural information, thereby enhancing robustness to cross-modal differences. To further improve image registration accuracy, we introduce an extended approach, Cof-SIFT_HOG, which extracts Histogram of Oriented Gradients (HOG) features from the image gradient magnitude map of corresponding points and refines their positions based on HOG similarity. This refinement yields more precise alignment between the reference and image to be registered. We evaluated Cof-SIFT and Cof-SIFT_HOG on a diverse set of multi-modal remote sensing image pairs. The experimental results demonstrate that both methods outperform existing approaches, including SIFT, COFSM, SAR-SIFT, PSO-SIFT, and OS-SIFT, in terms of robustness and registration accuracy. Notably, Cof-SIFT_HOG achieves the highest overall performance, confirming the effectiveness of the proposed structural-preserving and corresponding point location refinement strategies in cross-modal registration tasks. Full article

This paper presents the implementation of a vision system for a collaborative robot equipped with a web camera and a Python-based control algorithm for automated object-sorting tasks. The vision system aims to detect, classify, and manipulate objects within the robot’s workspace using only 2D camera images. The vision system was integrated with the Universal Robots UR5 cobot and designed for object sorting based on shape recognition. The software stack includes OpenCV for image processing, NumPy for numerical operations, and scikit-learn for multilayer perceptron (MLP) models. The paper outlines the calibration process, including lens distortion correction and camera-to-robot calibration in a hand-in-eye configuration to establish the spatial relationship between the camera and the cobot. Object localization relied on a virtual plane aligned with the robot’s workspace. Object classification was conducted using contour similarity with Hu moments, SIFT-based descriptors with FLANN matching, and MLP-based neural models trained on preprocessed images. Conducted performance evaluations encompassed accuracy metrics for used identification methods (MLP classifier, contour similarity, and feature descriptor matching) and the effectiveness of the vision system in controlling the cobot for sorting tasks. The evaluation focused on classification accuracy and sorting effectiveness, using sensitivity, specificity, precision, accuracy, and F1-score metrics. Results showed that neural network-based methods outperformed traditional methods in all categories, concurrently offering more straightforward implementation. Full article

Accurate image registration is essential for synthetic aperture radar (SAR) applications such as change detection, image fusion, and deformation monitoring. However, SAR image registration faces challenges including speckle noise, low-texture regions, and the geometric transformation caused by topographic relief due to side-looking radar imaging. To address these issues, this paper proposes a novel two-stage registration method, consisting of pre-registration and fine registration. In the pre-registration stage, the scale-invariant feature transform for the synthetic aperture radar (SAR-SIFT) algorithm is integrated into an iterative optimization framework to eliminate large-scale geometric discrepancies, ensuring a coarse but reliable initial alignment. In the fine registration stage, a novel similarity measure is introduced by combining frequency-domain phase congruency and spatial-domain gradient features, which enhances the robustness and accuracy of template matching, especially in edge-rich regions. For the topographic relief in the SAR images, an adaptive local stretching transformation strategy is proposed to correct the undulating areas. Experiments on five pairs of SAR images containing flat and undulating regions show that the proposed method achieves initial alignment errors below 10 pixels and final registration errors below 1 pixel. Compared with other methods, our approach obtains more correct matching pairs (up to 100+ per image pair), higher registration precision, and improved robustness under complex terrains. These results validate the accuracy and effectiveness of the proposed registration framework. Full article

This study aims to address the critical lack of standardized guidelines for unmanned aerial vehicle (UAV) image acquisition strategies utilizing structure-from-motion (SfM) by focusing on 3D building exterior modeling. A comprehensive experimental analysis was conducted to systematically investigate and quantitatively evaluate the effects of various shooting patterns and parameters on SfM reconstruction quality and processing efficiency. This study implemented a systematic experimental framework to test various UAV flight patterns, including circular, surface, and aerial configurations. Under controlled environmental conditions on representative building structures, key variables were manipulated, and all collected data were processed through a consistent SfM pipeline based on the SIFT algorithm. Quantitative evaluation results using various analytical methodologies (multiple regression analysis, Kruskal–Wallis test, random forest feature importance, principal component analysis including K-means clustering, response surface methodology (RSM), preference ranking technique based on similarity to the ideal solution (TOPSIS), and Pareto optimization) revealed that the basic shooting pattern ‘type’ has a significant and statistically significant influence on all major SfM performance metrics (reprojection error, final point count, computation time, reconstruction completeness; Kruskal–Wallis p < 0.001). Additionally, within the patterns, clear parameter sensitivity and complex nonlinear relationships were identified (e.g., overlapping variables play a decisive role in determining the point count and completeness of surface patterns, with an adjusted R² ≈ 0.70; the results of circular patterns are strongly influenced by the interaction between radius and tilt angle on reprojection error and point count, with an adjusted R² ≈ 0.80). Furthermore, composite pattern analysis using TOPSIS identified excellent combinations that balanced multiple criteria, and Pareto optimization explicitly quantified the inherent trade-offs between conflicting objectives (e.g., time vs. accuracy, number of points vs. completeness). In conclusion, this study clearly demonstrates that hierarchical strategic approaches are essential for optimizing UAV-SfM data collection. Additionally, it provides important empirical data, a validated methodological framework, and specific quantitative guidelines for standardizing UAV data collection workflows, thereby improving existing empirical or case-specific approaches. Full article

This study aimed to produce invertase with characteristics comparable to commercial formulations using a low-cost raw material, employing environmentally friendly extraction and refinement methods. Sifted yeast, the residual baker’s yeast in industrial production, was used as raw material, and invertase in the yeast cell was extracted by ultrasonication and purified by micro- and ultra-filtration (MF and UF) methods. Subjecting the crude enzyme extract to MF followed by UF resulted in increased activity and specific activity. Through this process, the enzyme activity increased from 153 IU/mL to 691 IU/mL. The purified enzyme was lyophilized and the production of invertase at the laboratory scale was accomplished. The obtained enzyme was found to be stable at pH 5 and within the temperature range of 30–40 °C. It retained its activity for 60 days at −18 °C, whereas a 20% loss in activity was observed at +4 °C over the same period. As a result, it was demonstrated that invertase enzyme can be produced from a low-cost raw material which is considered as waste in the industry. To pass to the commercial production stage, processing of higher amounts of raw material by preventing foaming and heating problems in ultrasonication and scale-up studies testing the permeability properties of different membrane systems at a pilot-scale are necessary. Full article

MPS I (Mucopolysaccharidosis type I) is a rare lysosomal storage disease originating from the deficiency of the enzyme alpha-L-iduronidase, encoded by the IDUA gene, which impairs the degradation of glycosaminoglycans (GAGs) and diminishes biological functioning across several organs. Background: Out of the eleven MPS disorders, MPS I includes three syndromes, of which the first, named Hurler syndrome, affects the most. Methods: Several in silico tools were used, such as ConSurf (73 variants), Mutation Assessor (69 variants), PredictSNP, MAPP, PhDSNP, Polyphen-1, Polyphen-2, SIFT, SNAP, PANTHER, MetaSNP (24 variants); Missense 3D-DB (11 variants) and AlignGVGD (eight variants) for physicochemical properties; and I-Mutant, Mupro, CUPSAT, and INPS for stability predictions (four variants). Results: A molecular docking study was performed for the two variants: L238Q and P385R scored −7.22 and −7.05 kcal/mol, respectively, and the native scored −7.14 kcal/mol with IDR as the ligand. Molecular dynamics anticipated how these molecules fluctuate over a period of 100 nanoseconds. Conclusions: Alpha-L-iduronidase enzyme has a critical role in the lysosomal degradation of glycosaminoglycans. According to the comparative analysis of the three structures by MDS, P385R had the least stability in all aspects of the plots. Our study demonstrates that the mutation significantly alters protein stability and binding efficiency with the ligands. Full article

A key component in visual Simultaneous Location And Mapping (SLAM) systems is feature extraction and description. One common algorithm that accomplishes this purpose is Oriented FAST and Rotated BRIEF (ORB), which is used in state-of-the-art SLAM systems like ORB-SLAM. While it is faster than other feature detectors like SIFT (340 times faster) or SURF (15 times faster), it is one of the most computationally expensive algorithms in these types of systems. This problem has commonly been solved by delegating this task to hardware-accelerated solutions like FPGAs or ASICs. While this solution is useful, it incurs a greater economical cost. This work proposes a solution for feature extraction and description based on a modern low-power mainstream microcontroller. The execution time of ORB, along with power consumption, are analyzed in relation to the number of feature points and internal variables. The results show a maximum of 0.6 s for ORB execution in 1241 × 376 resolution images, which is significantly slower than other hardware-accelerated solutions but remains viable for certain applications. Additionally, the power consumption ranges between 30 and 40 milliwatts, which is lower than FPGA solutions. This work also allows for future optimizations that will improve the results of this paper. Full article

Plant 3D reconstruction plays a critical role in precision agriculture and plant growth monitoring, yet it faces challenges such as complex background interference, difficulties in capturing intricate plant structures, and a slow reconstruction speed. In this study, we propose PlantSamGaussianReconstruction (PSGR), a novel method that integrates Grounding SAM with 3D Gaussian Splatting (3DGS) techniques. PSGR employs Grounding DINO and SAM for accurate plant–background segmentation, utilizes algorithms such as Scale-Invariant Feature Transform (SIFT) for camera pose estimation and sparse point cloud generation, and leverages 3DGS for plant reconstruction. Furthermore, a 3D–2D projection-guided optimization strategy is introduced to enhance segmentation precision. The experimental results of various multi-view plant image datasets demonstrate that PSGR effectively removes background noise under diverse environments, accurately captures plant details, and achieves peak signal-to-noise ratio (PSNR) values exceeding 30 in most scenarios, outperforming the original 3DGS approach. Moreover, PSGR reduces training time by up to 26.9%, significantly improving reconstruction efficiency. These results suggest that PSGR is an efficient, scalable, and high-precision solution for plant modeling. Full article

Background and Objectives: Type 2 diabetes mellitus (T2DM) is a health problem all over the world due to its serious complications such as diabetic nephropathy, diabetic neuropathy, diabetic retinopathy, cardiovascular diseases, and limb amputation. The risk factors for T2DM are environmental, lifestyle, and genetic. The genome-wide association studies (GWASs) have revealed the linkage of certain loci with diabetes mellitus (DM) and its complications. The objective of this study was to examine the association of genetic loci with diabetic nephropathy (DN) in the Saudi population. Materials and Methods: Whole exome sequencing (WES) and bioinformatics analysis, such as Genome Analysis Toolkit, Samtools, SnpEff, Polymorphism Phenotyping v2, and Sorting Intolerant from Tolerant (SIFT), were used to examine the association of gene variations with DN in 26 Saudi patients (18 males and 8 females). Results: The present study showed that there are loci that are probably linked to DM and DN. The genes showed variations that include COCH, PRPF31, PIEZO2, RABL5, CCT5, PLIN3, PDE4A, SH3BP2, GPR108, GPR108, MUC6, CACNA1D, and MAFA. The physiological processes that are potentially affected by these gene variations include insulin signaling and secretion, the inflammatory pathway, and mitochondrial function. Conclusion: The variations in these genes and the dysregulation of these processes may be linked to the development of DM and DN. These findings require further verification in future studies with larger sample sizes and protein functional studies. The results of this study will assist in identifying the genes involved in DM and DN (for example, through genetic counseling) and help in prevention and treatment of individuals or populations at risk of this disease and its complications. Full article

Autoimmune gastritis (AIG) has a strong correlation with gastric neuroendocrine tumors (NETs) and gastric cancer, making its timely and accurate diagnosis crucial for tumor prevention. The endoscopic manifestations of AIG differ from those of gastritis caused by Helicobacter pylori (H. pylori) infection in terms of the affected gastric anatomical regions and the pathological characteristics observed in biopsy samples. Therefore, when diagnosing AIG based on endoscopic images, it is essential not only to distinguish between normal and atrophic gastric mucosa but also to accurately identify the anatomical region in which the atrophic mucosa is located. In this study, we propose a patient-based multitask gastroscopy image classification network that analyzes all images obtained during the endoscopic procedure. First, we employ the Scale-Invariant Feature Transform (SIFT) algorithm for image registration, generating an image similarity matrix. Next, we use a hierarchical clustering algorithm to group images based on this matrix. Finally, we apply the RepLKNet model, which utilizes large-kernel convolution, to each image group to perform two tasks: anatomical region classification and lesion recognition. Our method achieves an accuracy of 93.4 ± 0.5% (95% CI) and a precision of 92.6 ± 0.4% (95% CI) in the anatomical region classification task, which categorizes images into the fundus, body, and antrum. Additionally, it attains an accuracy of 90.2 ± 1.0% (95% CI) and a precision of 90.5 ± 0.8% (95% CI) in the lesion recognition task, which identifies the presence of gastric mucosal atrophic lesions in gastroscopy images. These results demonstrate that the proposed multitask patient-based gastroscopy image analysis method holds significant practical value for advancing computer-aided diagnosis systems for atrophic gastritis and enhancing the diagnostic accuracy and efficiency of AIG. Full article

To address the challenges posed by significant parallax, dynamic changes in monitoring camera positions, and the need for rapid wide-field image stitching in underground coal mine tunneling faces, this paper proposes a fast image stitching method for tunneling face images based on navigation and positioning data. First, using a pixel-based calculation approach, the tunneling face scene is partitioned into the cutting section and the ground, enhancing the reliability of scene segmentation. Then, the spatial distance between the camera and the cutting plane is computed based on the tunneling machine’s navigation and positioning data, and a plane-induced homography model is employed to efficiently determine the dynamic transformation matrix of the cutting section. Finally, the Dual-Homography Warping (DHW) method is applied to achieve fast panoramic image stitching of the tunneling face. Comparative experiments with three classical stitching methods, SURF, SIFT, and BRISK, demonstrate that the proposed method reduces stitching time by 60%. Field experiments in underground environments verify that this method can generate a complete panoramic stitched image of the tunneling face, providing an unobstructed perspective beyond the machine body and cutting head to clearly observe the shovel plate and surrounding ground conditions, significantly enhancing the visibility and convenience of remote operation. Full article