Scale Invariant Feature Transform Algorithm Research Articles

Real-Time Object Detection and Tracking for Occupational Health and Safety (OHS) in Mining Operations Using Streamed CCTV Data

Abstract Occupational Safety and Health (OHS) is very important in the mining industry which is prone to accident risks. Automatic detection of violations through CCTV streaming with the concept of a perimeter area is needed to prevent accidents. The purpose of this research is to create a violation detection system without re-modeling training that can adapt to different objects and regions. The proposed violation detection model includes four computational steps, namely (i) data reading, (ii) data pre-processing, (iii) object detection, and (iv) object tracking and violation detection. The computer vision technique uses the SIFT (Scale Invariant Feature Transform) algorithm and heuristics implemented with OpenCV. We used five CCTV video recordings of mining operations as examples. The results showed an average precision of 61.14%, a precision of 80.41%, and a recall of 69.38%. The average computation time of the model is 0.3 seconds per frame with heuristics that allow the model to be used in real-time. The model can be widely used in various fields and locations without having to retrain the model to improve OHS in the mining industry and other sectors with similar risks. This study will contribute to the development of a flexible, fast, and real-time automatic detection system.

CNN Algorithm with SIFT to Enhance the Arabic Sign Language Recognition

Sign language is used as a primary means of communication by millions of people who suffer from hearing problems. The unhearing people used visual language to interact with each other, Represented in sign language. There are features that the hearing impaired use to understand each other, which are difficult for normal people to understand. Therefore, deaf people will struggle to interact with society. This research aims to introduce a system for recognizing hand gestures in Arabic Sign Language (ArSL) through training the Convolutional Neural Network (CNN) on the images of ArSL gestures launched by the University of Prince Mohammad Bin Fahd, Saudi Arabia. A Scale Invariant Feature Transform (SIFT) algorithm is used for creating the feature vectors that contain shape, finger position, size, center points of palm, and hand margin by extracting the Important features for images of ArSL and transforming them to points of the vector. The accuracy of the proposed system is 97% using the SIFT with CNN, and equal to 94.8% nearly without SIFT. Finally, the proposed system was tried and tested on a group of persons and its effectiveness was proven after considering their observations.

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.

Micro-displacement measurement of optical interference with two-ring conjugated vortices

In this paper, we propose a two-ring conjugated vortex beam interferometer. The inner ring and outer ring of the vortex beams has different signs and topological charges. When combined, they produce an intricate pattern of interference fringes that resemble a flower petal, featuring both concentric inner and outer rings which rotates with different directions. We use the scale-invariant feature transformation algorithm to find the feature points. Then we use these points to measure the rotation angle of the outer ring relative to the inner ring and calculate the displacement based on it. We realize the measurement of sub-nanometer displacement. The displacement resolution is 0.38° nm−1. It is improved by about 12.55% compared with only the inner ring. The displacement corresponding to the minimum detectable angle of CCD is reduced from 0.080 nm to 0.060 nm. It will be a promising method in precision machining micro-vibration sensing, surface unevenness sensing, and microbial movement sensing and high-precision measurement of micro-displacement.

Detection of counterfeit images using SIFT and PNN

Manipulation of digital images presents a significant challenge, primarily due to the continuous advancement and sophistication of image enhancement tools. As a consequence, image feature extraction and matching have become pivotal areas of research in the field of image processing. Among the various techniques developed, the Scale-Invariant Feature Transform algorithm has gained widespread recognition for its robustness and superior performance in feature matching tasks when compared to other existing methods. In this context, our proposed method, which integrates the Scale-Invariant Feature Transform algorithm with a probabilistic neural network, offers a novel approach to digital image classification specifically aimed at counterfeit image detection. This integrated system not only enhances the accuracy of feature extraction but also leverages the probabilistic neural network to improve classification efficacy. Empirical results indicate that this hybrid approach achieves a remarkable relative error rate of just 0.666%, underscoring its potential as a reliable tool for combating digital image forgery. Such advancements are crucial for maintaining the integrity of digital media in various applications, from forensic analysis to digital content authentication.

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.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

With the development of applications related to image processing technology, the target requirements of data acquisition and analysis systems are constantly being optimized. Research on data acquisition and analysis based on the integrated SIFT (Scale Invariant Feature Transform) algorithm is becoming increasingly important. The construction of a data collection and analysis system focuses on ensuring efficient and accurate data collection and analysis to provide valuable insights and decision support. Key priorities include determining data requirements and objectives, designing appropriate data collection methods and tools, establishing reliable data storage and management systems, developing effective data analysis algorithms and models, and ensuring data security and privacy protection. This article analyzed the theoretical analysis of feature point extraction algorithm and the process steps and application of integrated SIFT algorithm. With the help of dimensionality reduction processing, the SIFT algorithm was improved and optimized. Combined with the scale space calculation formula, The conclusion is as follows: On the basis of image processing, the data collection and analysis system using the improved integrated SIFT algorithm on the five selected image file samples has improved the accuracy of feature point matching, with a comprehensive average improvement of 7.6%. The efficiency of feature point extraction has also been improved, with a comprehensive average improvement of about 8%. This indicated that the data collection and analysis system based on the improved integrated SIFT algorithm has good practical application results in feature point matching and extraction.

Product development strategy of non-heritage cultural and creative products under the fusion of traditional crafts and modern technology

Abstract In the contemporary digital era, the transformation of intangible cultural heritage (ICH) products represents a critical trend in their evolutionary trajectory. This study advocates for the synergistic integration of traditional craftsmanship with advanced technologies within the ICH sector. A standardized electronic image repository was established by scanning these items under uniform lighting conditions, forming the basis for generating non-heritage experimental datasets. Subsequently, a clustering algorithm was applied to extract color features from the images. Owing to its resilience against rotation, scale, and noise variations, the Scale-Invariant Feature Transform (SIFT) algorithm was utilized to delineate pattern features of non-heritage items. The analysis primarily focused on the predominant colors in contemporary ICH products, paving the way for innovative developments that incorporate augmented reality (AR) technology with non-heritage characteristics. Quantitative analyses indicated that the average hue of these products is 118.15°, predominantly within the warmer green spectrum of the color wheel. Moreover, the evaluation of pattern style similarity of non-heritage items, as determined by the SIFT algorithm, yielded an average of 88.36%. Further, regression analysis demonstrated a significant influence of hue, brightness, pattern, and texture on the effectiveness of product development, with regression coefficients of 0.276, 0.236, 0.237, and 0.159, respectively, at a significance level of P<0.001. This research highlights the potential of contemporary science and technology to invigorate traditional arts, thereby contributing significantly to the preservation and revitalization of valued cultural legacies.

Contribution of 3D reconstruction techniques to the animated restoration of historical scenes

Abstract Amid the escalating demand for heightened realism across diverse sectors such as film, television, gaming, tourism, virtual display, and the preservation of cultural heritage, there has been a notable advancement in reverse modeling technology that facilitates the extraction of three-dimensional models from image sequences. This study focuses on the classification of 3D reconstruction for the animation restoration of historical scenes. It delves into the technical pathways for the 3D reconstruction of such scenes, employing a clustering-based Scale Invariant Feature Transform (SIFT) feature-matching acceleration algorithm for the extraction and matching of image features. Subsequently, it integrates the Bundler method of camera self-calibration with the Patch-based Multi-view Stereo (PMVS) algorithm for dense reconstruction, culminating in the assembly and testing of the reconstructed historical scene. The acceleration time of the SIFT algorithm is higher than that of the standard algorithm when the number of images is 10. The standard algorithm’s acceleration time is the longest when there are more than 10 images. When the number of images is greater than 50, the acceleration ratio of sigma=60 is greater than that of sigma=120. This indicates that the clustering-based SIFT algorithm is suitable for accelerated matching of large-scale image sets. The acceleration effect can be improved by appropriately decreasing the value of sigma. The maximum difference between the five independent vectors in model 1 and model 2 is 3.18. After scaling model 2, the difference is narrowed to[ 0.01,0.03]. The small error of the model in scenes 3~9 and the clear graphic texture indicate that the 3D reconstruction model of the historical scene designed in this paper has high accuracy and provides a model reference for the animation restoration of the historical scene.

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

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

Design of automatic style classification system of visual art works based on image processing

Abstract The emergence of a vast array of visual artworks meets the growing public demand for cultural literacy while simultaneously presenting challenges in the understanding and selection of appropriate artistic styles. Leveraging advancements in image processing technology, this study successfully automates the classification of 158,652 visual art images by categorizing them into distinct artistic styles. A novel approach combining the Scale-Invariant Feature Transform (SIFT) feature extraction algorithm with the spatial pyramid matching technique is introduced. This paper details the development and evaluation of a classification system tailored for the automatic categorization of visual artworks. Comparative testing results demonstrate that this innovative combination surpasses the traditional SIFT algorithm in both execution speed and classification accuracy. Specifically, the Support Vector Machine (SVM) based classification system, utilizing the enhanced SIFT method, achieves an accuracy rate exceeding 90% in identifying five distinct styles, including PHPS, NOS, IS, TF, and AAS. The implementation of this automatic classification system proves to be a practical tool in aiding individuals to select the visual artwork style that best suits their preferences.

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.