Scale Invariant Feature Transform Algorithm Research Articles

An Efficient Search on Cloud Images with Blockchain Technology

A key task in computer vision is image retrieval, which has wide-ranging applications across multiple domains. Using query picture features, this abstract proposes an image retrieval approach that focuses on the widely used Scale-Invariant Feature Transform (SIFT) algorithm for feature extraction and distance calculation. The suggested method starts by extracting SIFT features from a set of photos, building a keypoints and descriptor database. By capturing the unique qualities of nearby image regions, these features enable reliable matching and retrieval. The security of private cloud data, which includes query queries, the search tree, and outsourced photographs, is another major issue. Use a feature extraction method first for integrated picture features, which are composed of fundamental components like colour and shape. In particular, because the proposed method uses a balancing index tree, it can achieve logarithmic search time. Second, the picture and query feature are encrypted using the secure inner product. Include a system for determining duplicate picture material as well. When given a query image, SIFT feature extraction is applied to it, producing keypoints and descriptors that correspond to its visual characteristics. Next, a distance computation method like Euclidean distance is used to compare the features of the database photos with the query image features. The similarity between the query image and the database images is measured through this comparison. The retrieved photos are ranked in order of similarity to the query image based on the calculated distances. Search results with lesser distances between images are deemed more similar and are displayed first. To locate visually related images in huge databases, the image retrieval system that uses SIFT feature extraction and distance calculation provides a reliable and effective solution. It contributes to developments in multimedia retrieval, visual analytics, and picture understanding by enabling applications like content-based image search, image recommendation systems, and image clustering.

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.

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm.

The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate.

Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer-assisted pattern-identification algorithm.

Individual recognition of animals is an important aspect of ecological sciences. Photograph-based individual recognition options are of particular importance since these represent a non-invasive method to distinguish and identify individual animals. Recent developments and improvements in computer-based approaches make possible a faster semi-automated evaluation of large image databases than was previously possible. We tested the Scale Invariant Feature Transform (SIFT) algorithm, which extracts distinctive invariant features of images robust to illumination, rotation or scaling of images. We applied this algorithm to a dataset of 800 tail pattern images from 100 individual Eurasian beavers (Castor fiber) collected as part of the Norwegian Beaver Project (NBP). Images were taken using a single-lens reflex camera and the pattern of scales on the tail, similar to a human fingerprint, was extracted using freely accessible image processing programs. The focus for individual recognition was not on the shape or the scarring of the tail, but purely on the individual scale pattern on the upper (dorsal) surface of the tail. The images were taken from two different heights above ground, and the largest possible area of the tail was extracted. The available data set was split in a ratio of 80% for training and 20% for testing. Overall, our study achieved an accuracy of 95.7%. We show that it is possible to distinguish individual beavers from their tail scale pattern images using the SIFT algorithm.

Design of remote acquisition system for ecological water demand information of an estuarine wetland

In order to improve data coverage and data collection efficiency, the design method for a remote collection system of ecological water demand information of an estuarine wetland is proposed. The hardware design of the remote information acquisition system is realized through the overall structure design, the Unified Modeling Language (UML), system outline design, system logic design, and system module division. The information is acquired by polling, and the acquisition frequency is adjusted according to the actual situation and user needs. In the system software design, the Scale-invariant feature transform algorithm (SIFT) is used to extract the image features of the estuarine wetland, and a support vector machine is used to cluster the collected information according to the image features to realize the remote acquisition of water demand information of the estuarine wetland. The experimental results show that the maximum data coverage of the proposed method is 95%, and the data acquisition time is less than 0.2 h, indicating that the proposed method has high information acquisition efficiency and high data coverage.

The Application of Improved Scale Invariant Feature Transformation Algorithm in Facial Recognition

The Application of Improved Scale Invariant Feature Transformation Algorithm in Facial Recognition

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.

Research of different feature detection and matching algorithms on panoramic image

Image stitching is the process of combining numerous photos to make a panorama. The technique of image stitching has rapidly advanced and grown to be a significant area of digital image processing. Many image stitching methods have been proposed and studied in prior study. In this paper, the image stitching process is implemented using different algorithms. For keypoints detection, the algorithms of Harris corner detection, SIFT(Scale-Invariant Feature Transform), SURF(Speeded Up Robust Feature) and ORB(Oriented FAST and Rotated BRIEF) algorithms are applied, then use different methods (i.e., Brute Force, etc.) for feature matching. The RANSAC(Random Sample Consensus) method is used to calculate a homography matrix from matched feature vectors and use it to warp the images. Image blending and cropping methods are proposed to enhance the image quality. Given groups of the self-captured images, experiments have been down to shown the performance of different techniques.

Application of Automatic Recognition and Positioning Technology of Industrial Robot based on Monocular Vision Technology in Vehicle Manufacturing

Combining machine vision technology with intelligent algorithms to improve the automatic identification and positioning technology of industrial robots to meet the production needs in different industrial environments is the current research direction of intelligent robots. In this study, an automatic identification and positioning technology for industrial robots based on monocular vision technology is proposed. First, the template matching algorithm and the Scale-invariant feature transform (SIFT) algorithm are introduced. Aiming at the shortcomings of the Hessian matrix in the SIFT algorithm in the process of eliminating boundary effects, an improved SIFT algorithm using Harris corner point detection is further proposed, and the improved SIFT algorithm is used in the automatic recognition and target positioning operations of industrial robots. In order to verify the performance of the proposed improved SIFT algorithm, the recognition accuracy and positioning angle deflection of the algorithm in different plastic sheets were detected. The experimental results show that under the improved SIFT algorithm, the recognition accuracy of five different styles of plastic sheets is above 98%. The improved SIFT algorithm also has less error between the predicted value and the actual value of the positioning angle deflection on the four plastic sheets. The robot under the improved algorithm is applied to the vehicle manufacturing industry and the production efficiency of the vehicle is improved through the automatic recognition and positioning technology of intelligent robot.

Enhancing Water Body Identification in Satellite Images through Comparative Analysis of Canny Edge Detection and Scale-Invariant Feature Transform Algorithms

Aim: The objective of this research is to utilize the Innovative Canny Edge Detection algorithm to precisely identify water bodies in satellite images acquired from the Water Access program. Subsequently, the performance of this approach will be evaluated by comparing it with the SIFT algorithm. The primary objective of this analysis is to contribute to the equilibrium of the water ecosystem. Materials and Methods: Within this study, two distinct groups were compared: the Innovative Canny Edge Detection algorithm (CED) (N=10) and the scale invariant feature transform algorithm (SIFT) (N=10). The total sample size was determined with the help of (g) power technology, considering an alpha of 0.05, an enrollment ratio of 1, a 93% confidence interval, and a power of 80%. Results: The Innovative Canny edge detection algorithm exhibited a noteworthy detection rate, achieving an accuracy of 85% compared to the SIFT algorithm's accuracy of 82%. The achieved results were devoid of errors, as indicated by the accuracy of the significance value (0.0001), substantiated by an 80% pre-test power in SPSS Statistical analysis. A significant statistical distinction between the two algorithms was observed. Conclusion: The Innovative Canny Edge Detection algorithm outperforms the SIFT algorithm with statistical significance.

Domain-Specific Optimisations for Image Processing on FPGAs

Image processing algorithms on FPGAs have increasingly become more pervasive in real-time vision applications. Such algorithms are computationally complex and memory intensive, which can be severely limited by available hardware resources. Optimisations are therefore necessary to achieve better performance and efficiency. We hypothesise that, unlike generic computing optimisations, domain-specific image processing optimisations can improve performance significantly. In this paper, we propose three domain-specific optimisation strategies that can be applied to many image processing algorithms. The optimisations are tested on popular image-processing algorithms and convolution neural networks on CPU/GPU/FPGA and the impact on performance, accuracy and power are measured. Experimental results show major improvements over the baseline non-optimised versions for both convolution neural networks (MobileNetV2 & ResNet50), Scale-Invariant Feature Transform (SIFT) and filter algorithms. Additionally, the optimised FPGA version of SIFT significantly outperformed an optimised GPU implementation when energy consumption statistics are taken into account.

Application of UAVs and Image Processing for Riverbank Inspection

Many rivers are polluted by trash and garbage that can affect the environment. Riverbank inspection usually relies on workers of the environmental protection office, but sometimes the places are unreachable. This study applies unmanned aerial vehicles (UAVs) to perform the inspection task, which can significantly relieve labor work. Two UAVs are used to cover a wide area of riverside and capture riverbank images. The images from different UAVs are stitched using the scale-invariant feature transform (SIFT) algorithm. Static and dynamic image stitching are tested. Different you only look once (YOLO) algorithms are applied to identify riverbank garbage. Modified YOLO algorithms improve the accuracy of riverine waste identification, while the SIFT algorithm stitches the images obtained from the UAV cameras. Then, the stitching results and garbage data are sent to a video streaming server, allowing government officials to check waste information from the real-time multi-camera stitching images. The UAVs utilize 4G communication to transmit the video stream to the server. The transmission distance is long enough for this study, and the reliability is excellent in the test fields that are covered by the 4G communication network. In the automatic reconnection mechanism, we set the timeout to 1.8 s. The UAVs will automatically reconnect to the video streaming server if the disconnection time exceeds the timeout. Based on the energy provided by the onboard battery, the UAV can be operated for 20 min in a mission. The UAV inspection distance along a preplanned path is about 1 km at a speed of 1 m/s. The proposed UAV system can replace inspection labor, successfully identify riverside garbage, and transmit the related information and location on the map to the ground control center in real time.

Microscopic mechanical properties and fabric anisotropic evolution law of open graded gravel permeable base under dynamic loading

Under traffic dynamic load, the microscopic mechanical properties and the fabric anisotropic evolution of open graded gravel permeable base directly determine the ability of open graded gravel permeable base to resist external loads and maintain stability in service. Firstly, this article constructed a discrete element numerical model for open graded gravel permeable base, and verified the accuracy of the model through experiments. Then, the effects of gradation, nominal maximum particle size, friction coefficient between aggregate particles, and effective elastic modulus of aggregate on the microscopic mechanical properties, migration behavior, and the fabric anisotropic evolution of open graded gravel aggregate particles were studied in sequence. The research results indicate that the proposed image moving target tracking algorithm based on the three branches twin network can achieve precise tracking and measurement of particle microscopic migration behavior. The method proposed in this study for constructing a three-dimensional surface model of gravel particles based on the principle of multi view averaging and the scale invariant feature transformation algorithm can achieve rapid numerical reconstruction of the three-dimensional morphology of gravel particles at a low cost. The internal aggregate particles of intermediate grade gravel are more likely to form a stable network force chain topology structure that can effectively resist external loads. As the maximum nominal particle size increases, the topological structure of the contact force of gravel particles becomes sparse, and the proportion of strong contact force increases. Increasing the friction coefficient of the contact interface between aggregate particles can to some extent hinder the migration behavior of aggregate particles inside the graded gravel. As the effective elastic modulus of the aggregate increases, the internal network strong contact force chain topology of open graded gravel gradually forms. Under external loads, the migration speed and displacement of the aggregate particles inside open graded gravel decrease, and the degree of internal normal contact force and branch vector anisotropy also decrease.

Image stitching method based on feature optimization and circular function weighted fusion

To address the issues of ghosting and chromatic gaps in parallax image stitching, this paper proposes a novel method based on feature optimization and circular function weighted fusion. The method employs the Scale-Invariant Feature Transform (SIFT) algorithm with dimensionality reduction optimization to extract point features, while the Grid-based Motion Statistics (GMS) algorithm is utilized to eliminate mismatched points. To further enhance the number of features, line features are also introduced. A local warping model is employed to guide the deformation of the grid image. Finally, a nonlinear fading-in and fading-out fusion model with circular function weighting is proposed for better image transition. Experimental results demonstrate that the proposed method is effective in dealing with ghosting and chromatic gaps compared to several existing methods.

Multi-sensor remote sensing image alignment based on fast algorithms

Abstract Remote sensing image technology to the ground has important guiding significance in disaster assessment and emergency rescue deployment. In order to realize the fast automatic registration of multi-sensor remote sensing images, the remote sensing image block registration idea is introduced, and the image reconstruction is processed by using the conjugate gradient descent (CGD) method. The scale-invariant feature transformation (SIFT) algorithm is improved and optimized by combining the function-fitting method. By this way, it can improve the registration accuracy and efficiency of multi-sensor remote sensing images. The results show that the average peak signal-to-noise ratio of the image processed by the CGD method is 25.428. The average root mean square value is 17.442. The average image processing time is 6.093 s. These indicators are better than the passive filter algorithm and the gradient descent method. The average accuracy of image registration of the improved SIFT registration method is 96.37%, and the average image registration time is 2.14 s. These indicators are significantly better than the traditional SIFT algorithm and speeded-up robust features algorithm. It is proved that the improved SIFT registration method can effectively improve the accuracy and operation efficiency of multi-sensor remote sensing image registration methods. The improved SIFT registration method effectively solves the problems of low accuracy and long time consumption of traditional multi-sensor remote sensing image fast registration methods. While maintaining high registration accuracy, it improves the image registration speed and provides technical support for a rapid disaster assessment after major disasters such as earthquakes and floods. And it has an important value for the development of the efficient post-disaster rescue deployment.

CULTURAL HERITAGE DIGITAL PRESERVATION THROUGH AI-DRIVEN ROBOTICS

Abstract. This paper introduces a novel methodology developed for creating 3D models of archaeological artifacts that reduces the time and effort required by operators. The approach uses a simple vision system mounted on a robotic arm that follows a predetermined path around the object to be reconstructed. The robotic system captures different viewing angles of the object and assigns 3D coordinates corresponding to the robot's pose, allowing it to adjust the trajectory to accommodate objects of various shapes and sizes. The angular displacement between consecutive acquisitions can also be fine-tuned based on the desired final resolution. This flexible approach is suitable for different object sizes, textures, and levels of detail, making it ideal for both large volumes with low detail and small volumes with high detail. The recorded images and assigned coordinates are fed into a constrained implementation of the structure-from-motion (SfM) algorithm, which uses the scale-invariant features transform (SIFT) method to detect key points in each image. By utilising a priori knowledge of the coordinates and SIFT algorithm, low processing time can be ensured while maintaining high accuracy in the final reconstruction.The use of a robotic system to acquire images at a pre-defined pace ensures high repeatability and consistency across different 3D reconstructions, eliminating operator errors in the workflow. This approach not only allows for comparisons between similar objects but also provides the ability to track structural changes of the same object over time.Overall, the proposed methodology provides a significant improvement over current photogrammetry techniques by reducing the time and effort required to create 3D models while maintaining a high level of accuracy and repeatability.

Application of SIFT Algorithm based on the Gabor Features in Multi-source Information Image Monitoring

Under the background of today’s intelligence, the application of intelligent images is becoming popularized, and the detection of multi-source information images is a valuable research topic. Current image detection methods are insensitive and inaccurate. Therefore, the research combines the SIFT (scale-invariant feature transform) algorithm with the Gabor features and CNN (convolutional neural network (Ed note. Acronyms only need to be defined once.)) to form an improved new algorithm. The algorithm divides the features of the image into several different categories. In each category, the features will be fully identified and extracted, and different levels of feature matching will be performed. The properties of the SIFT algorithm are used to form an operational stacking pyramid and combine the Gabor features and CNN with it. The Gabor filter is formed into a filter bank to obtain parameters, including frequency, scale, and direction in various dimensions. The results are fused to obtain a fusion Gabor descriptor. The high sensitivity of CNN to images, particularly colors, is applied to the algorithm to make monitoring the algorithm more accurate. The experimental results show that the average precision rate, average recall rate, and average precision rate of the improved algorithm are 92.35%, 74.79%, and 82.55%, respectively, which are significantly higher than the other two algorithms used for comparison. The improved algorithm shows better performance and has remarkable advantages that can be applied to the image monitoring of image information.

An improved SIFT algorithm for registration between SAR and optical images

Aiming at improving the performance of scale invariant feature transform (SIFT) algorithm during the registration of optical and synthetic aperture radar (SAR) images, a new SIFT algorithm is proposed. Firstly, the nonlinear diffusion scale space of optical and SAR images is constructed by using nonlinear diffusion filtering, the uniform gradient information is calculated by using multi-scale Sobel operator and multi-scale exponential weighted mean ratio operator respectively. Then, after removing the first layer of the scale space with the image blocking strategy, the scale space is partitioned, and Harris feature points are extracted on the basis of consistent gradient information to obtain stable and uniform point features. Descriptors are constructed based on gradient position and direction histogram templates and normalized to overcome nonlinear radiation differences between images. Finally, the correct matching point pairs are obtained by using bilateral fast approximate nearest neighbor (FLANN) search matching method and random sampling consistent (RANSAC) method, and the affine transformation model parameters are obtained. Compared with the other two algorithms, the CMR of this algorithm is improved by 80.53%, 75.61% and 81.74% respectively in the three groups of images, and the RMSE is reduced by 0.6491, 1.0287 and 0.6306 respectively.

Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites

The orientation of fibers in composites reinforced with short fibers can provide insight into the microstructure of the material and considerably affect its macroscopic characteristics. However, the present standard techniques for detecting fiber orientation and length based on microscopic image processing have faults in practical applications, including high effort, low efficiency, and unreliable measurement results. In this study, a method for measuring fiber orientation based on 3D reconstructions of scanning electron microscope (SEM) images is provided. The geodesic active contour (GAC) model is applied to segment the fibers in the SEM images. Matching the fiber contours with the scale-invariant feature transformation (SIFT) algorithm successfully extracts 3D orientation information from the fiber contours. The unit vector of the fiber axis is fitted from the extracted point cloud using the ordinary least squares (OLS) method. With a maximum deviation of 3.83° and an average deviation of 1.50°, the measurement findings of this method are substantially comparable to those of the image-measuring instrument. This paper offers a quantitative approach to studying the microstructure of short fiber-reinforced composites, thereby furnishing objective evidence to support the development and research of such materials.

Sonar glass—Artificial vision: Comprehensive design aspects of a synchronization protocol for vision based sensors

Supporting visually impaired people during their navigation is a challenging task that involves localization, tracking, navigation, obstacle avoidance, and path guidance. Many researchers have experimented with Sonar, RFID, GPS, NFC, walking sticks, waist-based devices, and even computer vision modules for blind navigation. Using five sonar sensors for obstacle detection with direction and timestamp, we developed Sonar Glass. As humans see in left, right, front, top, and bottom directions based on eye angle and the head pose, the sonar glass is designed to provide obstacle information at the same angle. The head movement of the visually impaired person activates a pair of sensors for each module. Understanding human eye movement mechanisms and developing a synchronization protocol for each pair of visual sensors on sonar glass sitting on both eyes is the main goal of this paper. The major challenge lies in understanding and simulating the human vision mechanism and to realize how the field of Artificial Intelligence can be a contributor in producing technologies for the visually impaired. We are using log-polar transform to simulate human retinal image mapping. The Scale Invariant Feature Transform (SIFT) algorithm has also been implemented. It is the first time that both human eyes can be replaced by vision sensors. After comparing the estimated obstacle information from one sensor pair with that of the other sensors, the voice track is activated. The blind person uses the nearest obstacle information to avoid the obstacle and to extract spatial information about obstacles ahead of the user and provide an early warning. Unique in its design, the sonar Glass’s synchronization protocol for a pair of related sensors provides possible object information in that direction. The executions were simulated in MATLAB and the results obtained in real time are found to be promising as the tests were carried out both indoor and outdoor. The sonar Glass design is unique of its kind and the synchronization protocol for the pair of related sensors provides the possible vision about the object information at that particular direction.