Accuracy Of Image Matching Research Articles

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

Effect of Gantry Speed on Image Quality of Four-dimensional Cone-beam Computed Tomography Using a Dynamic Phantom of Lung

The purpose of this paper was to determine the optimal imaging conditions for four-dimensional cone-beam computed tomography (4D-CBCT) using an X-ray tube and a flat-panel detector mounted on a radiotherapy device. The optimal imaging conditions were examined by changing the gantry speed (GS) parameter that affected the exposure time. Exposed dose during imaging and image quality of moving phantom were compared between examined conditions. The weighted computed tomography dose index (CTDIW) decreased linearly with increasing GS. However, when GS was 180°/min or faster, the image quality degraded, and errors of 1 mm or more were observed regarding the size of mock tumor in the moving phantom. The accuracy of automatic image matching was within 0.1 mm when GS of 120°/min or slower was chosen. From the results of this study, we concluded that GS of 120°/min is the optimum imaging condition. Under this imaging condition, the exposure time and CTDIW can be reduced by about 50% without compromising the accuracy of image registration, compared to the conventional GS of 70°/min. In addition, it has been clarified that there is an event that image reconstruction is not performed correctly due to the influence of phantom artifacts without depending on GS.

Image feature extraction algorithm in big data environment

The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the BRISK and SIFT algorithms, this paper proposes an image feature extraction and matching algorithm based on BRISK corner points. By combining the SIFT scale space and the BRISK algorithm, a new scale space construction method is proposed. The BRISK algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that BRISK and SIFT algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness.

Accumulative Errors Optimization for Visual Odometry of ORB-SLAM2 Based on RGB-D Cameras

Oriented feature from the accelerated segment test (oFAST) and rotated binary robust independent elementary features (rBRIEF) SLAM2 (ORB-SLAM2) represent a recognized complete visual simultaneous location and mapping (SLAM) framework with visual odometry as one of its core components. Given the accumulated error problem with RGB-Depth ORB-SLAM2 visual odometry, which causes a loss of camera tracking and trajectory drift, we created and implemented an improved visual odometry method to optimize the cumulative error. First, this paper proposes an adaptive threshold oFAST algorithm to extract feature points from images and rBRIEF is used to describe the feature points. Then, the fast library for approximate nearest neighbors strategy is used for image rough matching, the results of which are optimized by progressive sample consensus. The image matching precision is further improved by using an epipolar line constraint based on the essential matrix. Finally, the efficient Perspective-n-Point method is used to estimate the camera pose and a least-squares optimization problem is constructed to adjust the estimated value to obtain the final camera pose. The experimental results show that the proposed method has better robustness, higher image matching accuracy and more accurate determination of the camera motion trajectory.

PVIDM: Privacy-preserving verifiable shape context based image denoising and matching with efficient outsourcing in the malicious setting

With the emerging techniques of wireless communication and cloud computing, large volumes of multimedia data are outsourced from resource-constrained users to the cloud with abundant resource for both delegated storage and computation. Unfortunately, there exists a risk of users’ image privacy leakage in the process of outsourcing to the cloud in the malicious setting. Most of the existing work achieved privacy-preserving verifiable image feature extraction and matching by using public key (fully) homomorphic encryption (FHE), and the heavy computational overhead and communication overhead cannot adapt to resource-constrained mobile devices. Other works disabled to achieve image denoising in the encrypted domain or only focused on the scale-invariant feature transform (SIFT) descriptor that is inappropriate for position-sensitive feature extraction. To address these issues, in this paper, a privacy-preserving verifiable shape context based image denoising and matching protocol PVIDM with efficient outsourcing in the malicious setting is proposed. To achieve this end, by exploiting any one-way trapdoor permutation, an efficient privacy-preserving verifiable image denoising scheme PPVID is firstly proposed to improve the accuracy of image matching, without exploiting public key FHE. Then, based on PPVID, a lightweight shape context based privacy-preserving verifiable image feature extraction and matching protocol PPVIM is devised, where secure and efficient comparison and counting protocols in the encrypted domain are respectively proposed. Both image privacy and image matching result privacy are well protected, and the correctness of image matching result can be efficiently verified. Finally, formal security proof and extensive simulations on real-world data sets demonstrate the efficiency and practicability of our proposed PVIDM. Especially, both the computational complexity for the usage of any one-way trapdoor permutation at data owner’s/user’s end and the size of the authentication tag generated by the cloud are O(1), independent to both the number of database images and the number of pixels in each image.

Spotted hyena optimizer with lateral inhibition for image matching

A hybrid spotted hyena optimizer (SHO) based on lateral inhibition (LI) is proposed, it has been applied to solve complication image matching problems. Lateral inhibition mechanism is applied for image pre-process to make intensity gradient in the image contrast enhanced and has the ability to enhance the characters of image, which is able to improve the accuracy of image matching. SHO is inspired from the behavior of social relationship and collaborative of spotted hyenas. This algorithm search for the global optimum mainly through four steps: prey, encircling, attacking prey, and searching prey. In the algorithm, the computation of search location is drastically reduced by incorporating of fitness calculation strategy for solving the real-life optimization problems. The proposed LI-SHO method for image matching mixed together the advantages of SHO and lateral inhibition mechanism. The experiment shows that the proposed algorithm based on lateral inhibition is more effective and feasible in image matching than the other comparing algorithm.

Ground Control Point-Free Unmanned Aerial Vehicle-Based Photogrammetry for Volume Estimation of Stockpiles Carried on Barges.

In this study, an approach using ground control point-free unmanned aerial vehicle (UAV)-based photogrammetry is proposed to estimate the volume of stockpiles carried on barges in a dynamic environment. Compared with similar studies regarding UAVs, an indirect absolute orientation based on the geometry of the vessel is used to establish a custom-built framework that can provide a unified reference instead of prerequisite ground control points (GCPs). To ensure sufficient overlap and reduce manual intervention, the stereo images are extracted from a UAV video for aerial triangulation. The region of interest is defined to exclude the area of water in all UAV images using a simple linear iterative clustering algorithm, which segments the UAV images into superpixels and helps to improve the accuracy of image matching. Structure-from-motion is used to recover three-dimensional geometry from the overlapping images without assistance of exterior parameters obtained from the airborne global positioning system and inertial measurement unit. Then, the semi-global matching algorithm is used to generate stockpile-covered and stockpile-free surface models. These models are oriented into a custom-built framework established by the known distance, such as the length and width of the vessel, and they do not require GCPs for coordinate transformation. Lastly, the volume of a stockpile is estimated by multiplying the height difference between the stockpile-covered and stockpile-free surface models by the size of the grid that is defined using the resolution of these models. Results show that a relatively small deviation of approximately ±2% between the volume estimated by UAV photogrammetry and the volume calculated by traditional manual measurement was obtained. Therefore, the proposed approach can be considered the better solution for the volume measurement of stockpiles carried on barges in a dynamic environment because UAV-based photogrammetry not only attains superior density and spatial object accuracy but also remarkably reduces data collection time.

New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation

Autonomous vehicle self‐localisation by scene matching under extreme environmental changes has been among the most challenging problems in robotics and computer vision in the last few years. Large dynamic illumination changes during the day and appearance changes between different seasons are the major difficulties about this problem, especially when the comparison is made between day‐time and night‐time images for the same scene. This study presents a new extended local difference binary (ELDB) image descriptor that represents a robust appearance invariant extension for the state‐of‐the‐art local difference binary (LDB) image descriptor. This study also introduces a new algorithm for vehicle visual localisation under extreme environmental changes. The new algorithm uses ELDB for image matching, and uses a modified multi‐hypothesis version of the Markov localisation (MHML) filter for self‐localisation. Experimental results show that the proposed modified MHML has reduced computational cost and has resulted in a faster cycle rate. Furthermore, these results show that ELDB has an improved image matching accuracy and requires less processing time compared to the original LDB. The proposed vision‐based vehicle localisation algorithm is shown to be faster and more accurate than other state‐of‐the‐art algorithms.

Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

In feature-based image matching, implementing a fast and ultra-robust feature matching technique is a challenging task. To solve the problems that the traditional feature matching algorithm suffers from, such as long running time and low registration accuracy, an algorithm called feedback unilateral grid-based clustering (FUGC) is presented which is able to improve computation efficiency, accuracy and robustness of feature-based image matching while applying it to remote sensing image registration. First, the image is divided by using unilateral grids and then fast coarse screening of the initial matching feature points through local grid clustering is performed to eliminate a great deal of mismatches in milliseconds. To ensure that true matches are not erroneously screened, a local linear transformation is designed to take feedback verification further, thereby performing fine screening between true matching points deleted erroneously and undeleted false positives in and around this area. This strategy can not only extract high-accuracy matching from coarse baseline matching with low accuracy, but also preserves the true matching points to the greatest extent. The experimental results demonstrate the strong robustness of the FUGC algorithm on various real-world remote sensing images. The FUGC algorithm outperforms current state-of-the-art methods and meets the real-time requirement.

Spark-based real-time proactive image tracking protection model

With rapid development of the Internet, images are spreading more and more quickly and widely. The phenomenon of image illegal usage emerges frequently, and this has marked impacts on people’s normal life. Therefore, it is of great importance to protect image security and image owner’s rights. At present, most image protection is passive. Most of the time, only when the images had been used illegally and serious adverse consequences had appeared did the image owners discover it. In this paper, a Spark-based real-time proactive image tracking protection model (SRPITP) is proposed to monitor the status of images under protection in real time. Whenever illegal use is found, an alert will be issued to image owners. The model mainly includes image fingerprint extraction module, image crawling module, and image matching module. The experimental results show that in SRPITP, the image matching accuracy rate is above 98.9%, and compared with its stand-alone counterpart, the corresponding time reduction for image extraction and matching are about 58.78% and 61.67%.

Remote Sensing Image Matching Based Improved ORB in NSCT Domain

Aiming at the problem that the ORB algorithm has no scale invariance and low matching accuracy in image matching, an improved ORB algorithm is proposed on the basis of SURF algorithm. Based on the flexibility of NSCT in image decomposition and the effectiveness of the improved ORB algorithm in remote sensing image matching, an improved ORB algorithm based on NSCT domain is proposed for remote sensing image matching. The image to be matched and the reference image are decomposed by NSCT. Two corresponding low-frequency images are obtained. Then, to reduce the influence of high-frequency noise on matching results, two low-frequency images are inputted to the improved ORB algorithm to obtain initial match results. The RANSAC algorithm is adopted to eliminate the mismatching points and complete the image matching. The experimental results show that the algorithm can make up the problem that the ORB algorithm has no scale invariance, and effectively improve the matching speed and accuracy of scale and rotation changes between two images. Meanwhile, the algorithm is more robust than classical methods in many complex situations such as image blur, field of view change, and noise interference.

Joint Image Deblurring and Matching with Blurred Invariant‐Based Sparse Representation Prior

Image matching is important for vision‐based navigation. However, most image matching approaches do not consider the degradation of the real world, such as image blur; thus, the performance of image matching often decreases greatly. Recent methods try to deal with this problem by utilizing a two‐stage framework—first resorting to image deblurring and then performing image matching, which is effective but depends heavily on the quality of image deblurring. An emerging way to resolve this dilemma is to perform image deblurring and matching jointly, which utilize sparse representation prior to explore the correlation between deblurring and matching. However, these approaches obtain the sparse representation prior in the original pixel space, which do not adequately consider the influence of image blurring and thus may lead to an inaccurate estimation of sparse representation prior. Fortunately, we can extract the pseudo‐Zernike moment with blurred invariant from images and obtain a reliable sparse representation prior in the blurred invariant space. Motivated by the observation, we propose a joint image deblurring and matching method with blurred invariant‐based sparse representation prior (JDM‐BISR), which obtains the sparse representation prior in the robust blurred invariant space rather than the original pixel space and thus can effectively improve the quality of image deblurring and the accuracy of image matching. Moreover, since the dimension of the pseudo‐Zernike moment is much lower than the original image feature, our model can also increase the computational efficiency. Extensive experimental results demonstrate that the proposed method performs favorably against the state‐of‐the‐art blurred image matching approach.

Multi modal registration of structural features and mutual information of medical image

Based on automatic matching of medical images synthesized by complementary characteristic, this paper makes full use of SURF algorithm’s good scale rotation invariance and HOG algorithm to describe local shape information better, showing good robustness when affected by geometric and optical deformation of graphics. Each performance of algorithm is comprehensively evaluated through the experiment, with accuracy of reaching 98%, meeting the actual application requirements. The algorithm shows good performance and application potential in the field of medical image matching. At the same time, since medical images are affected by lighting, equipment, scene and other factors in the acquisition process, there are large differences, which affect the robustness of the algorithm to a certain extent. Experiments show that this method has high accuracy in digital medical image matching and can be used to construct automatic matching system of medical images.

Research on Improving the Reliability of Vision Positioning System by Deep Learning

Vision positioning system is widely used in robotics, industrial testing, servo control and other autonomous systems. Precise object positioning needs to accurately match the feature key points of the object, so as to solve the object position and attitude precisely. Simple image or feature matching method cannot meet the requirements of both reliability and accuracy for positioning system. Recently, deep learning has aroused widespread concern, Caffe as a framework for deep learning, performs particularly well in image recognition. In this paper, we propose a method of using convolutional neural networks to recognize target in complex scenes, and then to obtain the information of the target position by precise image matching. The experimental results show that the proposed method effectively eliminates the false target recognition and improves the image matching accuracy.

GW28-e0419 Image detection scale-invariant feature transform algorithm based on feature matching improves image matching accuracy

GW28-e0419 Image detection scale-invariant feature transform algorithm based on feature matching improves image matching accuracy

SURF binarization and fast codebook construction for image retrieval

A new framework for image retrieval/object search is proposed based on the VLAD model and SURF descriptors to improve the codebook construction speed, the image matching accuracy, and the online retrieval speed and to reduce the data storage. First, SURF binarization and dimensionality reduction methods are proposed to convert a 64-dimensional SURF descriptor into an 8-dimensional descriptor. Second, a two-step clustering algorithm is proposed for codebook construction to significantly reduce the computational cost of clustering while maintaining the accuracy of the clustering results. Moreover, for object search, a scalable overlapping partition method is proposed to segment an image into 65 patches with different sizes so that the object can be matched quickly and efficiently. Finally, a feature fusion strategy is employed to compensate the performance degradation caused by the information loss of our proposed dimensionality reduction method. Experiments on the Holidays and Oxford datasets demonstrate the effectiveness and efficiency of the proposed algorithms.

A GPU-based symmetric non-rigid image registration method in human lung.

Quantitative computed tomography (QCT) of the lungs plays an increasing role in identifying sub-phenotypes of pathologies previously lumped into broad categories such as chronic obstructive pulmonary disease and asthma. Methods for image matching and linking multiple lung volumes have proven useful in linking structure to function and in the identification of regional longitudinal changes. Here, we seek to improve the accuracy of image matching via the use of a symmetric multi-level non-rigid registration employing an inverse consistent (IC) transformation whereby images are registered both in the forward and reverse directions. To develop the symmetric method, two similarity measures, the sum of squared intensity difference (SSD) and the sum of squared tissue volume difference (SSTVD), were used. The method is based on a novel generic mathematical framework to include forward and backward transformations, simultaneously, eliminating the need to compute the inverse transformation. Two implementations were used to assess the proposed method: a two-dimensional (2-D) implementation using synthetic examples with SSD, and a multi-core CPU and graphics processing unit (GPU) implementation with SSTVD for three-dimensional (3-D) human lung datasets (six normal adults studied at total lung capacity (TLC) and functional residual capacity (FRC)). Success was evaluated in terms of the IC transformation consistency serving to link TLC to FRC. 2-D registration on synthetic images, using both symmetric and non-symmetric SSD methods, and comparison of displacement fields showed that the symmetric method gave a symmetrical grid shape and reduced IC errors, with the mean values of IC errors decreased by 37%. Results for both symmetric and non-symmetric transformations of human datasets showed that the symmetric method gave better results for IC errors in all cases, with mean values of IC errors for the symmetric method lower than the non-symmetric methods using both SSD and SSTVD. The GPU version demonstrated an average of 43 times speedup and ~5.2 times speedup over the single-threaded and 12-threaded CPU versions, respectively. Run times with the GPU were as fast as 2min. The symmetric method improved the inverse consistency, aiding the use of image registration in the QCT-based evaluation of the lung.

Indoor localization with a signal tree

Indoor localization based on image matching faces the challenges of clustering large amounts of images to build a reference database, costly query when the database is large and indistinctive image features in buildings with unified decoration style. We propose a novel indoor localization algorithm using smartphones where WiFi, orientation and visual signals are fused together to improve the localization performance. The reference database is built as a signal tree with less computational cost as WiFi and orientation signals pre-cluster the reference images. During localization, WiFi and orientation signals not only offer more context information, but also prune impossible reference images, improving the accuracy and efficiency of image matching. In addition, images are described by multiple-level descriptors recording both global and local image information. The proposed method is compared with other methods in terms of localization accuracy, localization efficiency and time cost to build the reference database. Experimental results on four large university buildings show that our algorithm is efficient and accurate for indoor localization.

Gray-value Image Matching Method Based on Feature Points for Trademark Defect Detection of Spirit Level

In order to achieve trademark defect detection on metal background of a spirit level, a gray-value image matching method based on feature points is introduced to improve the robustness of image matching and avoid image linear changing by camera rotation or placement deviation of spirit level on a conveyor belt which leads to an inaccurate result of the subsequent algorithm in image matching. Before the image matching, FAST and SUFT feature point detection is introduced to preliminarily correct linear image to overcome the impact of the linear transformation and to improve the accuracy and robustness of image matching.

Use of Roadway Scene Semantic Information and Geometry-Preserving Landmark Pairs to Improve Visual Place Recognition in Changing Environments

Visual place recognition (VPR) in changing environments is an urgent challenge for long-term autonomous navigation. One recent ConvNet landmark-based approach exploits region landmarks coupled with ConvNet features to match images, and the approach has shown promising results under significant environmental and viewpoint changes. In this paper, we propose a robust ConvNet landmark-based system for VPR in changing outdoor roadway environments by extension of this approach from the following two aspects. First, our method utilizes more discriminative landmarks obtained by a novel refinement method called SemLandmarks , which leverages roadway scene semantic information to screen landmarks directly detected by an existing object proposal method. Second, our method improves the accuracy of image matching by introducing consistent spatial constraints based on the use of geometry-preserving landmark pairs. Experimental results demonstrate that our method significantly improves the state of the art in VPR in terms of recognition accuracy on three challenging benchmark data sets with various environmental and viewpoint changes.