SIFT Method Research Articles

Local-Peak Scale-Invariant Feature Transform for Fast and Random Image Stitching.

Image stitching aims to construct a wide field of view with high spatial resolution, which cannot be achieved in a single exposure. Typically, conventional image stitching techniques, other than deep learning, require complex computation and are thus computationally expensive, especially for stitching large raw images. In this study, inspired by the multiscale feature of fluid turbulence, we developed a fast feature point detection algorithm named local-peak scale-invariant feature transform (LP-SIFT), based on the multiscale local peaks and scale-invariant feature transform method. By combining LP-SIFT and RANSAC in image stitching, the stitching speed can be improved by orders compared with the original SIFT method. Benefiting from the adjustable size of the interrogation window, the LP-SIFT algorithm demonstrates comparable or even less stitching time than the other commonly used algorithms, while achieving comparable or even better stitching results. Nine large images (over 2600 × 1600 pixels), arranged randomly without prior knowledge, can be stitched within 158.94 s. The algorithm is highly practical for applications requiring a wide field of view in diverse application scenes, e.g., terrain mapping, biological analysis, and even criminal investigation.

Autonomous Vehicle Safety through the SIFT Method: A Conceptual Analysis

This study aims to provide a conceptual analysis of the dynamic transformations occurring in an autonomous vehicle (AV), placing a specific emphasis on the safety implications for pedestrians and passengers. AV, also known as self-driving automobiles, are positioned as potential disruptors in the contemporary transportation landscape, offering heightened safety and improved traffic efficiency. Despite these promises, the intricate nature of road scenarios and the looming specter of misinformation pose challenges that can compromise the efficacy of AV decision-making. A crucial aspect of the proposed verification process is the incorporation of the stop, investigate the source, find better coverage, trace claims, quotes, and media to the original context (SIFT) method. The SIFT method, originally designed to combat misinformation, emerges as a valuable mechanism for enhancing AV safety by ensuring the accuracy and reliability of information influencing autonomous decision-making processes.

Myrmecofauna (Hymenoptera, Formicidae) of Gökçeada Island, Türkiye

Türkiye is a very important country for faunal and floral studies due to its geographical location, the zoogeographic importance of its location and the biological richness it brings. This important area has been the subject of research on ants since second half of 19th century. However, studies on the ant fauna of the islands of Türkiye are very old and insufficient. For this reason, the ant fauna of Gökçeada, the largest island of Türkiye, was investigated by direct collecting and litter sifting methods in 2017-2018. 72 species belonging to 4 subfamilies and 23 genera were identified from 1105 ant samples obtained from 73 different localities and 16 different habitats. Among these species, Messor maculifrons Santschi, 1927 and Tetramorium sahlbergi Finzi, 1936 are new records for the ant fauna of Türkiye. In addition, the ergatoid queen of Hypoponera eduardi (Forel, 1894) was recorded for the first time outside the Afrotropical region.

Design of an English vocabulary e-learning recommendation system based on word bag model and recurrent neural network algorithm

This article takes learning English vocabulary as the foundation, constructs precise classification strategies, and designs a recommendation system for English vocabulary learning. This article constructs a recommendation system for English vocabulary learning based on the word bag calculation model and recursive neural network calculation method, which has great significance, and also conducts in-depth research on this system. Thus, a classification calculation method based on the word bag calculation model was proposed. The calculation method in this paper is different from traditional calculation methods, and the classification accuracy in this paper is higher. Meanwhile, recurrent neural networks themselves have one or more feedback loops that can feed back information from the neural network to other neural networks, resulting in recurrent networks with different structures. After a series of research results, it has been found that the SIFT method takes longer to extract than the SURF method, and the number of extracted features is relatively large. Therefore, the recommendation system for English vocabulary learning proposed in this article can be improved according to the different needs of users, in order to better meet their individual requirements.

Analysis of the vehicle's flow based on the neural network and the SIFT method

The article presents a vehicle counting system based on TensorFlow neural network models and the SIFT machine vision method. An experimental comparison was made of five detectors consisting of metaarchitecture (Faster R-CNN, SSD) and neural networks extracting features (Resnet V1 100, Inception V2, Inception Resnet V2 and Mobilenet V1). The main aspects of these detectors are analyzed, such as accuracy, speed, memory consumption, the number of floating point operations per second and the number of trainable parameters of convolutional neural networks. The calculation of vehicles is carried out by an algorithm based on the SIFT method. This algorithm compares the descriptors of all vehicles in the frame at the current time with the descriptors at the previous time. Based on the maximum match of the descriptors, the algorithm assigns the vehicle identification number from the previous frame, and in the absence of matches creates a new identification number. This approach will make it possible to calculate vehicles more accurately and assess their trajectory and speed.

Three-dimensional model of normal human dermal tissue using serial tissue sections.

Background: This study aims to construct a three-dimensional model of skin dermis utilizing continuous tissue sections, with the primary objective of obtaining anatomical structure data for normal human dermal tissues. Methods: Normal skin tissue specimens were acquired, paraffin-embedded, and subjected to HE staining. Panoramic images of skin sections were captured using a microscope. Tissue section images were aligned using the SIFT and StackReg image alignment methods, with analysis conducted using the OpenCV module. Mimics17 software facilitated the reconstruction of the skin dermal 3D model, enabling the calculation of dermal porosity and the void diameter. Results: Panoramic skin slices exhibited high-resolution differentiation of dermal fibers and cellular structures. Both SIFT and StackReg image alignment methods yielded similar results, although the SIFT method demonstrated greater robustness. Successful reconstruction of the three-dimensional dermal structure was achieved. Quantitative analysis revealed a dermal porosity of 18.96 ± 4.41% and an average pore diameter of 219.29 ± 34.27 μm. Interestingly, the porosity of the dermis exhibited a gradual increase from the papillary layer to the fourth layer, followed by a transient decrease and then a gradual increase. The distribution of the mean pore diameter mirrored the pattern observed in porosity distribution. Conclusion: Utilizing the continuous skin tissue slice reconstruction technique, this study successfully reconstructed a high-precision three-dimensional tissue structure of the skin. The quantitative analysis of dermal tissue porosity and average pore diameter provides a standardized dataset for the development of biomimetic tissue-engineered skin.

Diversity Design of Traditional Cultural Elements in Interior Design Based on Combinatorial Optimization Algorithm

Abstract There are great limitations in the imagination and play space of traditional interior design methods, and it is very costly to make changes after the design is completed. For this reason, this paper combines and optimizes SIFT and image splicing methods to recognize traditional cultural elements in images. Gray scale processing is performed on the image, unstable edge response points are removed and low contrast feature points are filtered, and the Gaussian pyramid is established by convolving the image with the Gaussian kernel function under different scale factors to establish the dimensions of the feature points. On this basis, an image splicing optimization algorithm that combines the two-dimensional information entropy of the image is proposed to divide the image into detail regions and flat regions, assign corresponding weights to the feature points in the optimization stage of the uni-responsive matrix, and minimize the residual sum to obtain the optimal uni-responsive matrix using the Levenberg-Marquardt algorithm, to improve the splicing quality. In the actual design test, the Chinese chair has the lowest success rate of 94.1%, and the output quality is 0.9215 and 0.9527. The output quality decreases as the scene becomes more complex. The SSIM and DoEM values for the Chinese chair are 0.9582 and 0.9682 for a simple scenario and decrease to a minimum of 0.8543 and 0.8472 as the number of interior pieces and other items in the scenario increases. By proposing the approach in this paper, labor costs and expenses can be saved and a new direction for interior design development can be taken.

Face Detection and Recognition Using Raspberry PI Computer

This paper presents a face detection and recognition system utilizing a Raspberry Pi computer that is built on a predefined framework. The theoretical section of this article shows several techniques that can be used for face detection, including Haar cascades, Histograms of Oriented Gradients, Support Vector Machine and Deep Learning Methods. The paper also provides examples of some commonly used face recognition techniques, including Fisherfaces, Eigenfaces, Histogram of Local Binary Patterns, SIFT and SURF descriptor-based methods and Deep Learning Methods. The practical aspect of this paper demonstrates use of a Raspberry Pi computer, along with supplementary tools and software, to detect and recognize faces using a pre-defined dataset.

Lattice Boltzmann model for diffusion equation with reduced truncation errors: Applications to Gaussian filtering and image processing

Several image processing approaches require blurring an image using the Gaussian filter. In the present study the filtering method based on a novel four-velocity lattice Boltzmann model with non-local collision kernel is proposed. By applying the Chapman–Enskog expansion up to super-Burnett terms it is shown that the present scheme is equivalent to the diffusion equation with relatively small hyper-viscous defects (truncation errors). Compared to the conventional lattice Boltzmann model the proposed scheme has a small computational overhead but significantly improves accuracy due to the reduced hyper-viscosity. To validate the model the following test problems are considered: diffusion in time of a Gaussian bell, sinusoidal wave, image filtering, feature detection with the application of the modified SIFT method. The numerical experiments indicate that the novel model gives more precise results than the standard lattice Boltzmann model for non-small values of diffusion coefficient and in several cases its computational cost is competitive with OpenCV blurring techniques. In addition, the model can be efficiently used in feature detection algorithms like SIFT.

Seasonal and Periodical Assessment of the Abundance and Diversity of Epigaeic Invertebrates in an Urban Forest Remnant, Dar es Salaam, Tanzania

Urban Forest (UF) refers to a complex human-environment system encompassing urban and peri-urban vegetation such as gardens, rivers and coastal corridors, and uninhabited yards. Human beings benefit from a healthy UF in a variety of ways such as recreation, disasters management, and the lessening of the effects of environmental deterioration (caused by the ongoing anthropogenic activities) like air pollution and ozone concentration affecting many epigaeic fauna. Epigaeic Invertebrates (EIs) are above ground foraging or litter dwelling invertebrates carrying important roles in UFs ecosystems such as ecosystem engineers and pests’ natural enemies. The present study assessed the influence of dry and wet seasons and different periods of a day (morning, afternoon, and evening hours) towards the abundance and diversity of ground dwelling invertebrates around urban forest remnant in Dar es salaam city, Tanzania. Data collection used pitfall trap, baited traps, and dry leaf litter sifting methods. A total of 10,363 EIs individuals were collected with 133 morpho species, 87 families and 18 orders (Hymenoptera dominated by 71.4%). Wet season had significantly higher abundance (6,360 individuals with 121 morpho species) than dry season (4,003 individuals and 88 morpho species), possibly due to the higher availability of food resources during rainy times. However, quite unexpectedly dry season had higher species diversity, probably due to the over dominance of a very aggressive Formicidae species in wet season that displaced some intolerable species. Also, the overall variations of species diversity and abundance between morning, afternoon, and evening hours were significantly different but higher in the morning. This suggested that EIs were active at different times of the day but were more attracted to the morning sunshine and decreased as the land became hotter. This study depicts urban forests to be among world ecosystems with relatively high levels of EIs biodiversity and hence an urgent call for their conservation efforts before it is too late.

Tools, Tests, and Checklists: The Evolution and Future of Source Evaluation Frameworks

In this article, we connect the scholarly conversations around established and emerging source evaluation frameworks such as the ABCs of website evaluation, CRAAP Test, DIG Method, RADAR approach, SIFT Method, the Proactive Evaluation approach proposed by the Project Information Literacy Team. We then explain each evaluation framework, delineating which framework is appropriate for a particular format, such as scholarly sources, online sources, image-based sources, and/or networked information sources. Lastly, we showcase the ways we have incorporated these source evaluation frameworks into our own classroom instruction and provide readers with ideas on how to do the same within their own institutional contexts.

COMPARISON OF SIFT AND ORB METHODS IN IDENTIFYING THE FACE OF BUDDHA STATUE

The statue is part of the heritage facial recognition process which is immobile and artistically stylized. Identifying the similarities between the statues can help provide an important reference for tourism in recognizing the faces of the statues which are different and have almost the same characteristics in every country, especially in Indonesia, among the facial recognition of the statues based on the condition, color, and shape of the face. The purpose of this study is to apply the original images that have characteristics, partially done manually to various types of transformations and calculate matching evaluation parameters such as the number of key points in the image, the level of matching, and the required execution time for each algorithm. To confirm the efficiency of the proposed method, experiments were carried out on private data sets obtained from statues under low light conditions and in different poses. The data was taken based on the image of the Buddha's face and matched with the facial image of the Buddha statue available in the database using comparisons resulting from data processing using the Sift and ORB methods with various types of transformations. The result will be seen in the image that is matched with the best algorithm for each type of distortion. The faces tested are images of the faces of the Buddha statues that are recognized, and photos of some of the original statues that were not saved due to unclear lighting and camera distance factors. The results show that the number of key points generated is the number of key points, the ORB method gives fewer results compared to the SIFT method and the average SIFT recognition and processing time shows better performance for an average of 100% at a SIFT matching rate of 2% with time 0.400285 and the ORB method is 1% for the time 0.400961

AUTOMATIC EXTRACTION OF CONTROL POINTS FROM 3D LIDAR MOBILE MAPPING AND UAV IMAGERY FOR AERIAL TRIANGULATION

Abstract. Installing targets and measuring them as ground control points (GCPs) are time consuming and cost inefficient tasks in a UAV photogrammetry project. This research aims to automatically extract GCPs from 3D LiDAR mobile mapping system (L-MMS) measurements and UAV imagery to perform aerial triangulation in a UAV photogrammetric network. The L-MMS allows to acquire 3D point clouds of an urban environment including floors and facades of buildings with an accuracy of a few centimetres. Integration of UAV imagery, as complementary information enables to reduce the acquisition time of measurement as well as increasing the automation level in a production line. Therefore, a higher quality measurements and more diverse products are obtained. This research hypothesises that the spatial accuracy of the L-MMS is higher than that of the UAV photogrammetric point clouds. The tie points are extracted from the UAV imagery based on the well-known SIFT method, and then matched. The structure from motion (SfM) algorithm is applied to estimate the 3D object coordinates of the matched tie points. Rigid registration is carried out between the point clouds obtained from the L-MMS and the SfM. For each tie point extracted from the SfM point clouds, their corresponding neighbouring points are selected from the L-MMS point clouds, and then a plane is fitted and then a tie point was projected on the plane, and this is how the LiDAR-based control points (LCPs) are calculated. The re-projection error of the analyses carried out on a test data sets of the Glian area in Iran show a half pixel size accuracy standing for a few centimetres range accuracy. Finally, a significant increasing of speed up in survey operations besides improving the spatial accuracy of the extracted LCPs are achieved.

High precision registration of monochrome and color video based on improved SIFT and mutual information method

High precision registration of monochrome and color video based on improved SIFT and mutual information method

Identification of Lines Based on Form Factor and Geometrical Descriptors

The problems of releasing and identifying features of key elements and their identification in images are shown. The basic algorithms and methods for forming descriptors for key elements in the image are considered. The main disadvantages of existing methods in aerospace images are the loss of stability and the presence of a large number of false key elements when changing recording to theconditions (for example, brightness and contrast of the received images). To eliminate these drawbacks, it is proposed to use selected lines as key elements, and their geometrical characteristics to form a descriptor. To analyze the proposed algorithm andthe SIFT method, fragments of aerospace images changed in brightness and contrast in a graphic editor from -50 to +50 percent of the original were used, which made it possible to evaluate the operation of these algorithms in conditions close to the real survey. It is shown that the proposed algorithm is more stable than the SIFT method with increasing contrast and decreasing the brightness of the test aerospace image. Also, the proposed algorithm is characterized by a smaller number of detected false key elements compared to the SIFT method.

Structural characterization and conformational dynamics of alpha-1 antitrypsin pathogenic variants causing alpha-1-antitrypsin deficiency.

Background: Alpha-1 antitrypsin deficiency (A1ATD) is a progressive lung disease caused by inherited pathogenic variants in the SERPINA1 gene. However, their actual role in maintenance of structural and functional characteristics of the corresponding α-1 anti-trypsin (A1AT) protein is not well characterized. Methods: The A1ATD causative SERPINA1 missense variants were initially collected from variant databases, and they were filtered based on their pathogenicity potential. Then, the tertiary protein models were constructed and the impact of individual variants on secondary structure, stability, protein-protein interactions, and molecular dynamic (MD) features of the A1AT protein was studied using diverse computational methods. Results: We identified that A1ATD linked SERPINA1 missense variants like F76S, S77F, L278P, E288V, G216C, and H358R are highly deleterious as per the consensual prediction scores of SIFT, PolyPhen, FATHMM, M-CAP and REVEL computational methods. All these variants were predicted to alter free energy dynamics and destabilize the A1AT protein. These variants were seen to cause minor structural drifts at residue level (RMSD = <2Å) of the protein. Interestingly, S77F and L278P variants subtly alter the size of secondary structural elements like beta pleated sheets and loops. The residue level fluctuations at 100ns simulation confirm the highly damaging structural consequences of all the six missense variants on the conformation dynamics of the A1AT protein. Moreover, these variants were also predicted to cause functional deformities by negatively impacting the binding energy of A1AT protein with NE ligand molecule. Conclusion: This study adds a new computational biology dimension to interpret the genotype-protein phenotype relationship between SERPINA1 pathogenic variants with its structural plasticity and functional behavior with NE ligand molecule contributing to the Alpha-1-antitrypsin deficiency. Our results support that A1ATD complications correlates with the conformational flexibility and its propensity of A1AT protein polymerization when misfolded.

Diversity and Spatial Distribution of Leaf Litter Curculionidae (Coleoptera: Curculionoidea) in Two Ecuadorian Tropical Forests

Litter weevil communities were investigated in two different types of montane forest in Ecuador: foothill evergreen forest, present in the Otongachi Integral Reserve between 800 and 1000 m, and tropical montane cloud forest (TMCF), present in the Otonga Integral Reserve between 1600 and 2300 m. Sampling was conducted along elevation gradients, applying the entomological sifting method to 19 sampling transects (11 in Otonga and 8 in Otongachi). The taxa collected were identified as morphospecies, since the majority of them are still undescribed. A total of 510 specimens were sampled, belonging to 100 different morphospecies, 85 of which were found in Otonga and 15 in Otongachi. No species in common between the two areas were found, despite the fact that the distance is extremely small (approximately 12 km). The Otonga area, regardless of primary or secondary forest habitats, had higher mean species richness. In both areas, the majority of species were found in a small number of stations and were sampled in a small number of specimens. A dominant and ubiquitous species was present only in Otongachi. The analysis of the community composition was carried out according to different categories of environmental variables (Otonga vs. Otongachi, forest type, elevation, litter and canopy coverage characteristics). Two well-differentiated coenoses were found, influenced firstly by the differences between the two reserves, secondarily by the elevation gradient and, to a lesser extent, by the forest type and other environmental variables. Some morphospecies characteristic of a specific type of biocoenosis could be identified. The study highlighted how most litter weevil species are strictly associated with a peculiar microhabitat and have a very narrow elevation and spatial range; the weevil communities can be strongly affected by heavy human impact. The results confirm that the tropical forests, in particular the TMCFs, host great biodiversity and that the majority of species are strictly associated with a single site; therefore, particular protection for these habitats should be granted. However, the non-significant variation between primary and secondary TMCFs indicates that, when correct management is carried out, a limited disturbance can be tolerated with only a limited loss of biodiversity.

Molecular dynamics study of tropical calcific pancreatitis (TCP) associated calcium-sensing receptor single nucleotide variation.

Tropical Calcific Pancreatitis (TCP) is a chronic non-alcoholic pancreatitis characterised by extensive calcification. The disease usually appears at a younger age and is more common in tropical regions. This disease’s progression can lead to pancreatic diabetes, which can subsequently lead to pancreatic cancer. The CASR gene encodes a calcium-sensing receptor (CaSR), which is a GPCR protein of class C. It is expressed in the islets of Langerhans, the parathyroid gland, and other tissues. It primarily detects small gradients in circulating calcium concentrations and couples this information to intracellular signalling, which helps to regulate PTH (parathyroid hormone) secretion and mineral ion homeostasis. From co-leading insulin release, CaSR modulates ductal HCO3− secretion, Ca2+ concentration, cell-cell communication, β-cell proliferation, and intracellular Ca2+ release. In pancreatic cancer, the CaSR limits cell proliferation. TCP-related four novel missense mutations P163R, I427S, D433H and V477A, found in CaSR extracellular domain (ECD) protein, which were reported in the mutTCPdb Database (https://lms.snu.edu.in/mutTCPDB/index.php). P163R mutation occurs in ligand-binding domain 1 (LBD-1) of the CaSR ECD. To investigate the influence of these variations on protein function and structural activity multiple in-silico prediction techniques such as SIFT, PolyPhen, CADD scores, and other methods have been utilized. A 500 ns molecular dynamic simulation was performed on the CaSR ECD crystal structure and the corresponding mutated models. Furthermore, Principal Component Analysis (PCA) and Essential Dynamics analysis were used to forecast collective motions, thermodynamic stabilities, and the critical subspace crucial to CaSR functions. The results of molecular dynamic simulations showed that the mutations P163R, I427S, D433H, and V477A caused conformational changes and decreased the stability of protein structures. This study also demonstrates the significance of TCP associated mutations. As a result of our findings, we hypothesised that the investigated mutations may have an effect on the protein’s structure and ability to interact with other molecules, which may be related to the protein’s functional impairment.

Large Aerial Image Tie Point Matching in Real and Difficult Survey Areas via Deep Learning Method

Image tie point matching is an essential task in real aerial photogrammetry, especially for model tie points. In current photogrammetry production, SIFT is still the main matching algorithm because of the high robustness for most aerial image tie points matching. However, when there is a certain number of weak texture images in a surveying area (mountain, grassland, woodland, etc.), these models often lack tie points, resulting in the failure of building an airline network. Some studies have shown that the image matching method based on deep learning is better than the SIFT method and other traditional methods to some extent (even for weak texture images). Unfortunately, these methods are often only used in small images, and they cannot be directly applied to large image tie point matching in real photogrammetry. Considering the actual photogrammetry needs and motivated by the Block-SIFT and SuperGlue, this paper proposes a SuperGlue-based LR-Superglue matching method for large aerial image tie points matching, which makes learned image matching possible in photogrammetry application and promotes the photogrammetry towards artificial intelligence. Experiments on real and difficult aerial surveying areas show that LR-Superglue obtains more model tie points in forward direction (on average, there are 60 more model points in each model) and more image tie points between airline(on average, there are 36 more model points in each adjacent images). Most importantly, the LR-Superglue method requires a certain number of points between each adjacent model, while the Block-SIFT method made a few models have no tie points. At the same time, the relative orientation accuracy of the image tie points matched by the proposed method is significantly better than block-SIFT, which reduced from 3.64 μm to 2.85 μm on average in each model (the camera pixel is 4.6 μm).

Kinetics of reactions of NH4 + with some biogenic organic molecules and monoterpenes in helium and nitrogen carrier gases: A potential reagent ion for selected ion flow tube mass spectrometry.

To assess the suitability of NH4 + as a reagent ion for trace gas analysis by selected ion flow tube mass spectrometry, SIFT-MS, its ion chemistry must be understood. Thus, rate coefficients and product ions for its reactions with typical biogenic molecules and monoterpenes need to be experimentally determined in both helium, He, and nitrogen, N2 , carrier gases. NH4 + and H3 O+ were generated in a microwave gas discharge through an NH3 and H2 O vapour mixture and, after m/z selection, injected into He and N2 carrier gas. Using the conventional SIFT method, NH4 + reactions were then studied with M, the biogenic molecules acetone, 1-propanol, 2-butenal, trans-2-heptenal, heptanal, 2-heptanone, 2,3-heptanedione and 15 monoterpene isomers to obtain rate coefficients, k, and product ion branching ratios. Polarisabilities and dipole moments of the reactant molecules and the enthalpy changes in proton transfer reactions were calculated using density functional theory. The k values for the reactions of the biogenic molecules were invariably faster in N2 than in He but similar in both bath gases for the monoterpenes. Adducts NH4 + M were the dominant product ions in He and N2 for the biogenic molecules, whereas both MH+ and NH4 + M product ions were observed in the monoterpene reactions; the monoterpene ratio correlating (R2 =0.7) with the proton affinity, PA, of the monoterpene molecule as calculated. The data indicate that this adduct ion formation is the result of bimolecular rather than termolecular association. NH4 + can be a useful reagent ion for SIFT-MS analyses of molecules with PA(M) < PA(NH3 ) when the dominant single product ion is the adduct NH4 + M. For molecules with PA(M) > PA(NH3 ), such as monoterpenes, both MH+ and NH4 + M ions are likely products, which must be determined along with k by experiment.