Speeded Up Robust Features Algorithm Research Articles

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

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

DIGITAL IMAGE RESTORATION USING SURF ALGORITHM

In contemporary times, the preservation of scientific and creative endeavours often relies on the utilization of film and image archives, hence emphasizing the significance of image processing as a critical undertaking. Image inpainting refers to the process of digitally altering an image in a manner that renders the adjustments imperceptible to a viewer lacking knowledge of the original image. Image inpainting is a technique mostly employed to restore damaged regions within an image by utilizing information obtained from matching characteristics in relevant images. This process involves filling in the damaged areas and removing undesired objects. The SURF (Speeded Up Robust Feature) algorithm under consideration is partitioned into three primary phases. Firstly, the essential characteristics of the impaired image and the pertinent image are identified. In the second stage, the relationship between the damaged image and the relevant image is determined in terms of translation, scaling, and rotation. Ultimately, the destroyed area is reconstructed through the application of the inverse transformation. The quality assessment of inpainted images can be evaluated using metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The experimental findings provide evidence that the suggested inpainting technique is effective in terms of both speed and quality.

Synergy of digital economy and green economy in sustainable development policy

<p>Sustainability challenges and ICT perspectives are at the heart of current thinking on global economic and social development. The current development and growth process is based on unsustainable foundations due to irresponsible resource consumption and negative environmental impacts as well as greenhouse gas emissions. People need to find ways to integrate the digital economy and the sustainability of the green economy. Therefore, this paper firstly describes the intersection of digital economy and green economy, secondly introduces the security system of digital economy and green economy, then based on this, the SURF (Speeded-up robust features) algorithm is used to locate and improve the data aggregation system of digital economy and green economy, and finally, the algorithm simulation experiment is conducted. The experimental results found that the aggregation algorithm based on digital economy and green economy has 19% higher accuracy than the traditional algorithm. At the same time, the calculation speed is increased by three to five times. The above results show that the SURF algorithm is applied to the sustainable development research of digital economy and green economy with significant effect.</p>

InSAR Simulation and Speeded-Up Robust Features Algorithm for Terrain Relative Navigation in PSRs on the Moon

InSAR Simulation and Speeded-Up Robust Features Algorithm for Terrain Relative Navigation in PSRs on the Moon

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.

Enhancement Panoramic Image Based on Principal Component Analysis (PCA) and SURF Algorithm

Image upgrade is essential for giving improved data to other image handling methods. Also, the requirement for differentiation improvement to all-encompassing image is a usually performed examination by dental specialists and oral specialists in regular practice and is a critical indicative apparatus. It covers a more extensive zone than a traditional intraoral x-beam and, subsequently, gives important data about the maxillary sinuses, tooth situating and other bone variations from the norm. Thusly, it is anything but difficult to distinguish and perceive valuable data. In this pursuit was utilized the calcu lation of Principle Component Analysis (PCA) to upgrade the subtle elements of images and enhance the quality the image, by its joining with strategy for difference improvement, and highlights extraction that take from the upgrade technique to limit mistakes in images, along these lines enhance the execution of the framework, image upgrade strategies used to deliver more reasonable images for identification and extraction. Three principle computerized image process strategies: Contrast Stretching, Histogram Equalization and, Contrast Limited Adaptive Histogram Equalization , were considered to build the improvement image, and evacuat e clamor, separately however low quality and the image is light angle in hues and less clear . In this exploration we will, pre-prepared images were assessed utilizing current image recognition and highlight extraction techniques Speeded Up Robust Features (SURF) calculations after go in improve ment strategies. After that PCA embed the yield vector from SURF. Gotten results demonstrated that upgrade image expa nded after number of separated highlights and diminished mistakes amid info image in PCA. Therefore, the impacts of var ious pre-process procedures were thought about in the paper and the assessment parameters are figured which demonstrate that proposed work gives better execution from the image broke down under PSNR to enhance the dependability of proposed strategy from the outcomes.

Research on fabric image registration and color difference detection based on cyclic elements

In order to realize online color difference detection of printed fabrics, a color difference detection method of printed fabrics based on image segmentation and image registration was proposed to detect the color difference of simple printed fabrics. The acquired standard fabric images are segmented using the combination of a Canny edge detector and uniform grid partitioning. The color information of each sub image is extracted based on the segmentation results and saved. We use the speeded-up robust features algorithm that incorporates the position information of the cyclic element where the feature points are located. The segmentation path of the standard image is registered onto the fabric image to be detected. The color information of each sub image of the image to be detected is extracted according to the registered segmentation path. The color difference value between the sub image of the image to be detected and the sub image of the standard sample is calculated using the CIEDE2000 color difference formula. The experimental results show that when fabric images with duplicate patterns are registered, the image registration methods mentioned in this paper significantly improve the accuracy of registration. The accuracy of the detection method in this paper on the printed fabric dataset can reach 95% compared to the manual calibration results.

Video Global Motion Compensation Based on Affine Inverse Transform Model.

Global motion greatly increases the number of false alarms for object detection in video sequences against dynamic backgrounds. Therefore, before detecting the target in the dynamic background, it is necessary to estimate and compensate the global motion to eliminate the influence of the global motion. In this paper, we use the SURF (speeded up robust features) algorithm combined with the MSAC (M-Estimate Sample Consensus) algorithm to process the video. The global motion of a video sequence is estimated according to the feature point matching pairs of adjacent frames of the video sequence and the global motion parameters of the video sequence under the dynamic background. On this basis, we propose an inverse transformation model of affine transformation, which acts on each adjacent frame of the video sequence in turn. The model compensates the global motion, and outputs a video sequence after global motion compensation from a specific view for object detection. Experimental results show that the algorithm proposed in this paper can accurately perform motion compensation on video sequences containing complex global motion, and the compensated video sequences achieve higher peak signal-to-noise ratio and better visual effects.

Automatic Point Cloud Colorization of Ground-Based LiDAR Data Using Video Imagery without Position and Orientation System

With the continuous development of three-dimensional city modeling, traditional close-range photogrammetry is limited by complex processing procedures and incomplete 3D depth information, making it unable to meet high-precision modeling requirements. In contrast, the integration of light detection and ranging and cameras in mobile measurement systems provides a new and highly effective solution. Currently, integrated mobile measurement systems commonly require cameras, lasers, position and orientation system and inertial measurement units; thus, the hardware cost is relatively expensive, and the system integration is complex. Therefore, in this paper, we propose a ground mobile measurement system only composed of a LiDAR and a GoPro camera, providing a more convenient and reliable way to automatically obtain 3D point cloud data with spectral information. The automatic point cloud coloring based on video images mainly includes four aspects: (1) Establishing models for radial distortion and tangential distortion to correct video images. (2) Establishing a registration method based on normalized Zernike moments to obtain the exterior orientation elements. The error of the result is only 0.5–1 pixel, which is far higher than registration based on a collinearity equation. (3) Establishing relative orientation based on essential matrix decomposition and nonlinear optimization. This involves uniformly using the speeded-up robust features algorithm with distance restriction and random sample consensus to select corresponding points. The vertical parallax of the stereo image pair model is less than one pixel, indicating that the accuracy is high. (4) A point cloud coloring method based on Gaussian distribution with central region restriction is adopted. Only pixels within the central region are considered valid for coloring. Then, the point cloud is colored based on the mean of the Gaussian distribution of the color set. In the colored point cloud, the textures of the buildings are clear, and targets such as windows, grass, trees, and vehicles can be clearly distinguished. Overall, the result meets the accuracy requirements of applications such as tunnel detection, street-view modeling and 3D urban modeling.

An FPGA-Based General-Purpose Feature Detection Algorithm for Space Applications

Spacecraft takes images for applications/missions like attitude determination, astronomy, and space situational awareness. Feature detection is a primary operation in vision-based processing systems. Although different feature detection algorithms are used for specific purposes, they need to achieve invariance by scaling, rotation, and other interference, and output robust results. This article develops a general-purpose feature detection hardware architecture based on the speeded-up robust features (SURF) algorithm. On the other hand, the efficiency of the algorithm can affect the overall system performance to a large extent, especially in real-time operations. This article presents an field-programmable gate array (FPGA)-based implementation of a modified SURF algorithm. The advantages of FPGAs including parallel and pipelining, fixed-point arithmetic, and bitwise operations are fully applied to improve the performance and efficiency of the system in terms of power consumption, and resource utilization.

A reliable matching algorithm for heterogeneous remote sensing images considering the spatial distribution of matched features

ABSTRACT Owing to the differences in sensor types,resolutions, and imaging conditions of heterologous remote sensing images, thematching results of remote sensing images, such as low accuracy, few matched pairs,and low distribution quality, are not ideal, which makes precise registrationbetween heterogeneous images difficult. To mitigate this, we propose a reliablematching algorithm for heterogeneous remote sensing images that considers thespatial distribution of the matched features. First, feature-based matchingalgorithms such as the scale-invariant feature transform (SIFT) algorithm orthe speeded-up robust features algorithm are used to match images to obtain aninitial set of matched pairs and a set of candidate features. Then, accordingto the stability of the spatial distribution of locally correctly matchedfeatures, the distance and angular proximity between matched features and theirneighbours are calculated to obtain the accuracy of the matched pairs and removeincorrectly matched pairs. Finally, the random sample consensus (RANSAC)algorithm was used to fit the transformation model between images, and thefinal matched feature selection algorithm and automatic transformation erroralgorithm were used to detect candidate features to increase the number ofmatched pairs. Experimental analysis of heterogeneous multiscale andmultitemporal optical remote sensing images demonstrates the superiorcapability of the proposed algorithm over commonly used algorithms, includingSIFT, RANSAC, locality preserving matching, learning a two-class classifier formismatch removal, and linear adaptive filtering algorithms. In particular, whenthe precision of the initially matched pair is low, the proposed algorithm canachieve excellent results.

BOSF-SVM: A thermal image-based fault diagnosis method of circuit boards

In recent years, the lack of thermal images and the difficulty of thermal feature extraction have led to low accuracy and efficiency in the fault diagnosis of circuit boards using thermal images. To address the problem, this paper presents a simple and efficient intelligent fault diagnosis method combined with computer vision, namely the bag-of-SURF-features support vector machine (BOSF-SVM). Firstly, an improved BOF feature extraction based on SURF is proposed. The preliminary fault features of the abnormally hot components are extracted by the speeded-up robust features algorithm (SURF). In order to extract the ultimate fault features, the preliminary fault features are clustered into K clusters by K-means and substituted into the bag-of-features model (BOF) to generate a bag-of-SURF-feature vector (BOSF) for each image. Then, all of the BOSF vectors are fed into SVM to train the fault classification model. Finally, extensive experiments are conducted on two homemade thermal image datasets of circuit board faults. Experimental results show that the proposed method is effective in extracting the thermal fault features of components and reducing misdiagnosis and underdiagnosis. Also, it is economical and fast, facilitating savings in labour costs and computing resources in industrial production.

Extraction of 3D distribution of potato plant CWSI based on thermal infrared image and binocular stereovision system.

As the largest component of crops, water has an important impact on the growth and development of crops. Timely, rapid, continuous, and non-destructive detection of crop water stress status is crucial for crop water-saving irrigation, production, and breeding. Indices based on leaf or canopy temperature acquired by thermal imaging are widely used for crop water stress diagnosis. However, most studies fail to achieve high-throughput, continuous water stress detection and mostly focus on two-dimension measurements. This study developed a low-cost three-dimension (3D) motion robotic system, which is equipped with a designed 3D imaging system to automatically collect potato plant data, including thermal and binocular RGB data. A method is developed to obtain 3D plant fusion point cloud with depth, temperature, and RGB color information using the acquired thermal and binocular RGB data. Firstly, the developed system is used to automatically collect the data of the potato plants in the scene. Secondly, the collected data was processed, and the green canopy was extracted from the color image, which is convenient for the speeded-up robust features algorithm to detect more effective matching features. Photogrammetry combined with structural similarity index was applied to calculate the optimal homography transform matrix between thermal and color images and used for image registration. Thirdly, based on the registration of the two images, 3D reconstruction was carried out using binocular stereo vision technology to generate the original 3D point cloud with temperature information. The original 3D point cloud data were further processed through canopy extraction, denoising, and k-means based temperature clustering steps to optimize the data. Finally, the crop water stress index (CWSI) of each point and average CWSI in the canopy were calculated, and its daily variation and influencing factors were analyzed in combination with environmental parameters. The developed system and the proposed method can effectively detect the water stress status of potato plants in 3D, which can provide support for analyzing the differences in the three-dimensional distribution and spatial and temporal variation patterns of CWSI in potato.

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.

Fish species classification using a collaborative technique of firefly algorithm and neural network

Monitoring various Fish Species and its distribution of the species obtains a primary significance in receiving the insights to marine ecological-system. After this, visual classification of those species would aid in tracing out the movement and yield the patterns and trends in fish activities, which provides in depth knowledge of the species. Unconstrained under-water images pose highly variations because of the fish orientation changes, Light-intensities, similarity in fish patterns and fish shapes. This would create the greater challenge for Image-processing techniques in accurate classification of Fish species or the Fish classes. Hence, for this reason, Underwater Image Enhancement is implemented in combination of Morphological-operations in pre-processing method. The pre-processed image is then subjected to feature extraction process by using Speed-up Robust Feature algorithm. This is followed by Firefly Algorithm, applied for optimization of Region of interest selection in the selected-features. For the categorization of Fish-species, PatternNet is a technique which is employed, in classifying 10,000 marine fish-images to five categories (Dascyllus reticulatus, Plectroglyphidodon dickii, Chromis chrysura, Amphiprion clarkii, and Chaetodon lunulatus). The Efficiency of the proposed-framework is performed in terms of Classification accuracy, execution time, precision value, F-measure and recall factors with respect to various categories of fish species. The comparison of the proposed-framework is also assessed with the other existing methods. 98% of accuracy rate in classification was produced by the evaluation results of the proposed framework with a lesser average computation time of 3.64 s upon different tested images. Thus, the higher efficiency of the proposed framework is proved by the outcomes of the study.

A study of multi-target image-based displacement measurement approach for field testing of bridges

ABSTRACT As the demand for field testing of bridges grows, so does the need to optimize field testing procedures to allow for a simplified testing strategy that can be employed more effectively. With the advent of computer vision, there has been limited research exploring Feature-Based Image Registration (FBIR) methods for structural testing of bridges. In this paper, the potential of a simple FBIR approach to accurately capture submillimeter displacements using consumer-grade cameras will be demonstrated through a field test on a reinforced concrete slab bridge and on a full-scale bridge deck specimen in the laboratory. The internal and external parameters that influence the results of this measurement strategy were investigated by using various camera positions during the laboratory tests and applying different threshold parameters to the Speeded-Up Robust Features algorithm used for the feature detection and matching. The FBIR method demonstrates great potential, producing an average measurement accuracy within 1.6% of conventional displacement sensors during the field test and 3.3% during the laboratory tests. Altogether, the advantages to this image-based measurement approach enhance the load testing strategy to be implemented by bridge owners at much lower costs and with minimal complication and field setup.

Improved speeded-up robust features registration method based on particle swarm and gray wolf algorithm for coal mine images

Image registration is an electronic imaging technology that matches two or more images acquired at different times or under different conditions. Here, we highlight the application of image registration in monitoring robots (e.g., autonomous vehicles) of coal mines. In image registration processing, the scale-invariant feature transform algorithm and its improved versions achieve poor extraction effects for feature points on smooth-edge curves. In comparison, the speeded-up robust features (SURF) algorithm achieves more stable registration results in various images. To improve the accuracy of the SURF algorithm, it is optimized using the particle swarm optimization (PSO) algorithm and several improved PSOs, but it achieves unstable accuracy. Therefore, we optimize the improved gray wolf algorithm (IGWO) using advanced mathematical models in several computing steps. We add the memory function of both experienced individual and group optimal locations in PSO to update the particle positions. The evaluations performed on different datasets show that the accuracy and range of registration are substantially improved by IGWO and IGWO-PSO; however, the running time is extended. Thus, our improved method is competitive with other state-of-the-art image registration methods.

Real-Time Image Defect Detection System of Cloth Digital Printing Machine.

In order to solve the surface defects such as white silk, spots, and wrinkles on the fabrics in the process of digital printing production, a surface defect detection system for printed fabrics based on the accelerated robust feature algorithm is proposed. The image registration is mainly carried out by the speeded up robust features (SURF) algorithm; the bidirectional unique matching method is used to reduce the mismatch points, realize the accurate registration of the image, and extract the defect information through the difference algorithm. The experiment uses multiple images to verify the performance of the improved SURF algorithm. The experimental results show that the detection accuracy of the new system for surface defects of printed fabrics reaches 98%. The algorithm has higher detection rate and faster detection speed, which can meet the needs of practical industrial applications.

High resolution and real-time measurement of 2D fatigue crack propagation using an advanced digital image correlation

This paper developed a real-time and non-destructive 2D fatigue crack propagation tracing method based on an optimized digital image correlation (DIC) approach by introducing an image in-situ processing algorithm and a fatigue crack propagation monitoring algorithm. The in-situ processing algorithm coupled with optimizing the speeded up robust features (SURF) was applied to correct the shifted images during fatigue crack propagation tests, while the fatigue crack propagation monitoring algorithm was developed to detect the true crack tip and measure crack length. By optimizing SURF algorithm, the construction time of feature descriptor for interest point matching was reduced to 0.0066 s, 55 times shorter than the case using traditional SURF. In conjunction with the fatigue crack identification, this enables the real-time measurement of fatigue crack length with the resolution as high as 5.2 μm for a measurement region of 15.85 × 10.57 mm2. Compared to current DIC, the proposed algorithm either enables characterizing the crack tip and crack propagation path, or can be programmed as user-defined subroutine and integrated into commercial DIC software for example MatchID. As a result, the experimental measurements obtained by the proposed approach on the fatigue crack propagation tests of an Aluminium alloy exhibited a good agreement with simulation results based on the extended finite element method (XFEM), indicated its promising industrial prospect.

Image processing and machine learning based cavings characterization and classification

Wellbore failure is the most headache and common problem in drilling engineering. Wireline loggings like caliper log and image log are usually used to identify wellbore failure. However, it is impossible to discern the failure types and take steps in real-time using the post-drilling methods. During the drilling process, various cavings will circulate to the ground with drilling fluid when wellbore instability occurs. These cavings are the foremost indicator of wellbore deterioration and have a potential link to the mechanism of borehole failure. Unfortunately, the use of cavings to understand borehole instability and its mechanism requires correct descriptions of caving which are inclined to be qualitative and fragmentary in previous studies, as well as proper interpretation which entails a wealth of field experience and comprehensive understanding of drilling, geo-mechanics and geology. This paper proposes a new method for caving characterization and a caving interpretation workflow based on image processing and machine learning. Firstly, we thoroughly analyze all modes of borehole failures and the corresponding cavings they generate, and make clear the significant characteristics to distinguish different types of cavings. Then the morphologies of caving contour and surface are quantitatively characterized using Speeded-Up Robust Features (SURF) algorithm and 2D- Discrete Wavelet Transform (DWT), respectively. These two categories of characteristics are used as the input of Light Gradient Boosting Machine (LightGBM)-based classification model, and the mode of borehole failure that generates the caving can be inferred from the classification results. We demonstrate the application of this interpretation workflow utilizing the cavings from the T oilfield, Xinjiang, China. The result shows that the average accuracy of classification is 90.9 %, which reflects that the workflow has a satisfactory performance in caving interpretation as well as that the features have high representativeness and robustness for caving characterization. This method opens up a new path to the early warning of borehole instability and real-time diagnosis of instability mechanism, thus corresponding remedies can be taken in time to avoid serious downhole accidents. • The differences of morphology between various types of cavings are summarized into two aspects: contour and surface. • Contour morphology is quantitatively characterized based on the SURF algorithm. • 2D-DWT is applied to extract the characteristics of surface morphology at multi scales. • A caving interpretation model that links the morphology of caving to the mode of borehole instability is built.