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In this paper, we propose a novel paper currency defect detection algorithm using quaternion uniform strength. We first build paper currency image preprocessing integration framework which includes intensity balancing, paper currency location, and geometric correction. We then propose a global–local paper currency image registration algorithm by moving key areas within certain range which can eliminate the false difference effectively. Finally, the quaternion uniform strength is calculated by using quaternion convolution edge detector. The defect degree of paper currency is determined by using the quaternion uniform color difference. The proposed algorithm is tested using different datasets from five countries: CNY, USD, EUR, VND, and RUB. The experimental results demonstrate that the proposed algorithm yields better results than the existing state-of-the-art paper currency defect detection techniques. The demo of the proposed paper currency defect detection algorithm will be publicly available. [ABSTRACT FROM AUTHOR]

Diffeomorphic registration is a widely used technique for finding a smooth and invertible transformation between two coordinate systems, which are measured using template and reference images. The point-wise volume-preserving constraint det ⁡ (∇ φ (x)) = 1 is effective in some cases, but may be too restrictive in others, especially when local deformations are relatively large. This can result in poor matching when enforcing large local deformations. In this paper, we propose a new bi-variant diffeomorphic image registration model that introduces a soft constraint on the Jacobian equation det ⁡ (∇ φ (x)) = f (x) > 0. This allows local deformations to shrink and grow within a flexible range 0 < κ m < det ⁡ (∇ φ (x)) < κ M. The Jacobian determinant of transformation is explicitly controlled by optimizing the relaxation function f (x). To prevent deformation folding and improve the smoothness of the transformation, a positive constraint is imposed on the optimization of the relaxation function f (x) , and a regularizer is used to ensure the smoothness of f (x). Furthermore, the positivity constraint ensures that f (x) is as close to one as possible, which helps to achieve a volume-preserving transformation on average. We also analyze the existence of the minimizer for the variational model and propose a penalty-splitting algorithm with a multilevel strategy to solve this model. Numerical experiments demonstrate the convergence of the proposed algorithm and show that the positivity constraint can effectively control the range of relative volume without compromising the accuracy of the registration. Moreover, the proposed model generates diffeomorphic maps for large local deformations and outperforms several existing registration models in terms of performance. • A novel bi-variant diffeomorphic image registration model with relaxed Jacobian determinant constraints is proposed. • The existence of the minimizer for the variational model is proved. • A penalty splitting algorithm with a multilevel strategy is designed to solve the new model. • Numerical experiments show that the proposed algorithm is convergent and the new model has good performance. [ABSTRACT FROM AUTHOR]

In response to the low matching accuracy of stereo matching algorithms in image regions with specular reflection, this paper proposes a multidimensional fusion stereo matching algorithm named MFANet. The algorithm embeds a multispectral attention module into the residual feature extraction network, utilizing two‐dimensional discrete cosine transforms to extract frequency features. In the pyramid pooling module, a coordinated attention mechanism is introduced to capture relevant positional information. In the cost aggregation part, the MFANet algorithm incorporates a three‐dimensional attention mechanism, focusing on the more important semantic information in high‐level features. By combining detailed information from low‐level features, semantic information from high‐level features, and contextual information, the algorithm generates features that are more conducive to disparity prediction. The MFANet algorithm is evaluated on three standard datasets (SceneFlow, KITTI2015, and KITTI2012). Experimental results demonstrate its robustness against specular reflection interference, accurate prediction of disparities in specular reflection pathological regions, and promising application prospects. [ABSTRACT FROM AUTHOR]

In this paper, the issues on current optimization methods for identifying modern digital camera photosensors are presented. Methods for improving the reliability of identification of digital cameras are discussed, i.e., determining the digital camera used to take a particular photograph. Homogeneous images were optically recorded to form a noise signature, and sets of amateur images were tested for identification of three types of cameras from the images. Digital image filtering and image identity metric were optimized. An optimal digital filter was selected to evaluate smoothed images to obtain the noise signatures of the identified cameras. An optimal identity criterion was obtained by comparison of camera noise signatures. Using an optimal filter and identity criterion allowed increasing, on average, the reliability of identifying the camera used to take a particular photograph by > 60 times. This work provides reference to solve image problems in image registration and processing, security, forensics, and big data analysis. [ABSTRACT FROM AUTHOR]

Image stitching aims to synthesize a wider and more informative whole image, which has been widely used in various fields. This study focuses on improving the accuracy of image mosaic and proposes an image mosaic method based on local edge contour matching constraints. Because the accuracy and quantity of feature matching have a direct influence on the stitching result, it often leads to wrong image warpage model estimation when feature points are difficult to detect and match errors are easy to occur. To address this issue, the geometric invariance is used to expand the number of feature matching points, thus enriching the matching information. Based on Canny edge detection, significant local edge contour features are constructed through operations such as structure separation and edge contour merging to improve the image registration effect. The method also introduces the spatial variation warping method to ensure the local alignment of the overlapping area, maintains the line structure in the image without bending by the constraints of short and long lines, and eliminates the distortion of the non-overlapping area by the global line-guided warping method. The method proposed in this paper is compared with other research through experimental comparisons on multiple datasets, and excellent stitching results are obtained. [ABSTRACT FROM AUTHOR]

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains. [ABSTRACT FROM AUTHOR]

Star image registration is the most important step in the application of astronomical image differencing, stacking, and mosaicking, which requires high robustness, accuracy, and real-time capability on the part of the algorithm. At present, there are no high-performance registration algorithms available in this field. In the present paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF) while providing excellent robustness, high accuracy, and good real-time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms as judged by the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation, and complex sky background, making it a more ideal star image registration algorithm than current alternatives. [ABSTRACT FROM AUTHOR]

The paper proposes a new initial disparity estimation algorithm based on sparse local feature matching and one-dimensional dynamic programming. The new algorithm solves the problems of long calculation time, high memory consumption and unknown search range of disparity in the classical pyramid SGM dense matching algorithm. Based on the initial disparity, the final accurate disparity can be got by using SGM algorithm within fixed little search range. The new algorithm is more accurate than the traditional SGM algorithm (by testing on a public data set, the accuracy of new algorithm is 1.57% higher than traditional SGM algorithm), it does not need to artificially estimate the search range of disparity, and it is suitable for implementation on FPGA. Based on the new algorithm, the CPU-FPGA collaborative high-speed dense matching for high-resolution aerial images is realized. The average speed of dense matching on CPU-FPGA is 31.81times faster than the original SGM algorithm and 6.46 times faster than pyramid SGM algorithm. The speed of accurate matching by FPGA is 4.75 times faster than running on GPU. [ABSTRACT FROM AUTHOR]

Multimodal image matching is a core basis for information fusion, change detection, and image-based navigation. However, multimodal images may simultaneously suffer from severe nonlinear radiation distortion (NRD) and complex geometric differences, which pose great challenges to existing methods. Although deep learning-based methods had shown potential in image matching, they mainly focus on same-source images or single types of multimodal images such as optical-synthetic aperture radar (SAR). One of the main obstacles is the lack of public data for different types of multimodal images. In this paper, we make two major contributions to the community of multimodal image matching: First, we collect six typical types of images, including optical-optical, optical-infrared, optical-SAR, optical-depth, optical-map, and nighttime, to construct a multimodal image dataset with a total of 1200 pairs. This dataset has good diversity in image categories, feature classes, resolutions, geometric variations, etc. Second, we propose a scale and rotation invariant feature transform (SRIF) method, which achieves good matching performance without relying on data characteristics. This is one of the advantages of our SRIF over deep learning methods. SRIF obtains the scales of FAST keypoints by projecting them into a simple pyramid scale space, which is based on the study that methods with/without scale space have similar performance under small scale change factors. This strategy largely reduces the complexity compared to traditional Gaussian scale space. SRIF also proposes a local intensity binary transform (LIBT) for SIFT-like feature description, which can largely enhance the structure information inside multimodal images. Extensive experiments on these 1200 image pairs show that our SRIF outperforms current state-of-the-arts by a large margin, including RIFT, CoFSM, LNIFT, and MS-HLMO. Both the created dataset and the code of SRIF will be publicly available in https://github.com/LJY-RS/SRIF. [ABSTRACT FROM AUTHOR]

With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot's ability for autonomous navigation during self-driving. In this paper, we address the issue of low matching accuracy of stereo matching algorithms in specular regions of images and propose a multi-attention-based stereo matching algorithm called MANet. The proposed algorithm embeds a multi-spectral attention module into the residual feature-extraction network of the PSMNet algorithm. It utilizes different 2D discrete cosine transforms to extract frequency-specific feature information, providing rich and effective features for cost computation in matching. The pyramid pooling module incorporates a coordinated attention mechanism, which not only maintains long-range dependencies with directional awareness but also captures more positional information during the pooling process, thereby enhancing the network's representational capacity. The MANet algorithm was evaluated on three major benchmark datasets, namely, SceneFlow, KITTI2015, and KITTI2012, and compared with relevant algorithms. Experimental results demonstrated that the MANet algorithm achieved higher accuracy in predicting disparities and exhibited stronger robustness against specular reflections, enabling more accurate disparity prediction in specular regions. [ABSTRACT FROM AUTHOR]

The method of detecting the whole tampered image increases many unnecessary calculations. In order to reduce the computational complexity and further improve the detection accuracy, this paper proposed a copy-move forgery detection method based on improved saliency map and local feature matching. Firstly, it combined the gradient of image to improve the saliency map, and separated the local salient regions containing high texture information of the image. Secondly, it only used SIFT（Scale Invariant Feature Transform） algorithm to extract feature points in this local area. Then, it adopted density clustering and two-stage matching strategy for images with low saliency, and adopted the strategy of superpixel segmentation and salient block feature matching for images with high saliency. Finally, it combined PSNR and morphological operations to locate the tampered area. Experiments on two public datasets show that the average detection time of this method is less than 10s, and the average detection accuracy is greater than 97%,which are better than the compared methods. The experimental results show that this method can greatly reduce the detection time, effectively improve the detection accuracy, and has good robustness to geometric transformation and post-processing operations. [ABSTRACT FROM AUTHOR]

With the gradual advancement of image sensor technology and the gradual complexity of the application environment, single-source imaging sensors have been difficult to meet practical application requirements. Therefore, the registration and fusion techniques of infrared images and visible images are hot topics in recent years. This article aims to study the fusion of two images. Based on the traditional image fusion algorithm, the color space IHS transform and the lifting wavelet transform are combined according to the characteristics of infrared image and visible light image captured by the UAV imaging device. The experimental results show that the algorithm can not only retain the brightness information of the infrared target. Among them, some details of the visible light image are retained. In this paper, the four objective criteria of information entropy, standard deviation, image mean and average gradient are used to evaluate the fusion effect of the proposed algorithm. The obtained values are 7.285, 29.487, 122.2739, and 4.5678, respectively, which are higher than other algorithms. It can be seen that the algorithm proposed in this paper has important practical significance. [ABSTRACT FROM AUTHOR]

As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new method for image mosaicing (image stitching) is introduced based on Scale Invariant Feature transform (SIFT). One of the main drawbacks of SIFT is the redundancy of the extracted keypoints, which leads to lower image mosaicing quality. Recently, a new method called Redundant Keypoint Elimination (RKEM) was presented to remove these redundant features, and enhance image registration performance. Despite the applicability of RKEM, its threshold value is considered the same in all parts of the image. This characteristic leads to inappropriate removal of keypoints due to the fact that distribution of keypoints in the high-detailed region is denser than the low-detailed ones. This paper proposes a new method to improve RKEM called Clustered RKEM (CRKEM) which is based on keypoints distribution. Moreover, in this paper a new blending algorithm is proposed based on a Gaussian-weighted function. In the proposed blending method, the Gaussian function is proposed based on the mean and variance of the pixels in the overlapped region of images to be mosiaced. In comparison with the classical methods, the experimental results confirm the superiority of the proposed method in image mosaicing as well as to image registration and matching. [ABSTRACT FROM AUTHOR]

The process of aligning one image, in the coordinate system of another is called registration. Image registration is vital in the medical domain and is used for diagnosis, therapy planning, and treatment of diseases. Due to its huge applicability and importance, medical image registration has emerged to be a separate domain of research. Beginning from the invasive landmark based registration, innumerable algorithms have been proposed to register two images of the human physiology. However, a breakthrough in the literature occurred when an information theory based measure, mutual information was used to register two images. Since then a large number of new algorithms have developed, which use mutual information for fully automatic registration of medical images. This paper is a survey of these algorithms. Beginning from its development, it discusses about some of the major works done on mutual information based image registration. Some comparative studies with other algorithms have also been discussed. The paper ends with a discussion of the developments in medical image registration algorithms post mutual information, which primarily include deep neural network (DNN) based algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the low accuracy and time-consuming problems of AKAZE algorithm in image matching of glass-encapsulated electrical connectors, an improved image matching algorithm based on LK optical flow and grid motion statistics is proposed in this paper. Firstly, the AKAZE algorithm is used to extract the feature points of the glass-encapsulated electrical connector, and the M-LDB descriptor is used for the feature description. Then, LK optical flow method is used to calculate the matching area for conditional constraints, so as to obtain matching points, and the FLANN algorithm is used for feature matching. Finally, the image of the glass-encapsulated electrical connector is divided into multiple grids, and the number of correct matching points in the neighborhood of the well-matched feature points by FLANN is calculated and the threshold value is used to eliminate the incorrect matching points. The experiments in this paper use public data sets and glass-encapsulated electrical connector data to verify and analyze the performance of the algorithm in terms of real-time and accuracy. The results show that the improved algorithm has a matching accuracy of more than 93% when dealing with image pairs of glass-encapsulated electrical connectors with changes in blur, brightness and rotation, and takes less than 0.4 s, which proves the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose an original algorithm allowing the registration of multimodal images. Indeed, the large and nonlinear intensity changes between different modalities often lead to mismatching of the interest points, which can alter the quality of the registration. Our method builds on the SURF for the identification and the description of the interest points. In order to boost the confidence on the subsequent matching operation of the registration, an applicable classification on both the distance and orientation SURF features is presented despite the low structural similarity of the multimodal acquisition. The proposed algorithm is tested and validated on a large dataset extracted from acquisitions on cultural heritage wall paintings that implement 4 imaging modalities covering UV, IR, visible and fluorescence. The comparison with conventional SIFT/SURF approaches, PIIFD, SURF-PIIFD-PRM,CFOG and HTCPR for registration highlights the effectiveness of the method and its robustness to outliers in SIFT/SURF descriptors. [ABSTRACT FROM AUTHOR]

Traditional manually designed feature descriptors are prone to cause a large number of mismatches in the face of multi temporal remote sensing images with large geometric and radiation differences and obvious changes in ground features. This paper developed a convolutional neural network matching algorithm with double screening and proposed double constraints to extract the deep semantic feature map of the image through CNN, it selected the key points that satisfied both the priority maximum principle and the precise extreme value principle from the feature map, and extracted 512-dimension descriptors from the corresponding positions. In the feature matching stage, the strategy of combining the rough to fine reverse matching constraints and RANSAC constraints ensured that the false matching points were eliminated effectively, and retained a certain number of correct matching points, which improved the matching accuracy. The experimental results show that the number of matches is moderate, the correct matching rate is improved compared with other algorithms selected in the experiment, and the image registration error is reduced by about 2 pixels. The running time is obviously improved, matching effect is good, with certain adaptability and robustness. [ABSTRACT FROM AUTHOR]

In this paper, we relax the equality constraints of the diffeomorphic image registration in Han and Wang (2020) [9] to equality and inequality constraints. Then, the mesh folding is eliminated. Moreover, a numerical algorithm for the proposed model is presented. Numerical tests are also performed to show that the proposed algorithm is competitive compared with some published image registration models. [ABSTRACT FROM AUTHOR]

The mainstream image matching method for recovering the motion of the camera is based on local feature matching, which faces the challenges of rotation, illumination, and the presence of dynamic objects. In addition, local feature matching relies on the distance between descriptors, which easily leads to lots of mismatches. In this paper, we propose a new robust image matching method for camera pose estimation, called IM_CPE. It is a novel descriptor matching method combined with 3-D point clouds for image matching. Specifically, we propose to extract feature points based on a pair of matched point cloud planes, which are generated and segmented based on depth images. Then, the feature points are matched based on the distance between their corresponding 3-D points on the point cloud planes and the distance between their descriptors. Moreover, the robustness of the matching can be guaranteed by the centroid distance of the matched point cloud planes. We evaluate the performance of IM_CPE using four well-known key point extraction algorithms, namely Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated Brief (ORB), with four sequences from the TUM RGBD dataset. According to the experimental results, compared to the original SIFT, SURF, FAST, and ORB algorithms, the NN_mAP performance of the four key point algorithms has been improved by 11.25%, 13.98%, 16.63%, and 10.53% on average, respectively, and the M.Score has also been improved by 25.15%, 23.05%, 22.28%, and 11.05% on average, respectively. The results show that the IM_CPE can be combined with the existing key points extraction algorithms and the IM_CPE can significantly improve the performance of these key points algorithms. [ABSTRACT FROM AUTHOR]

Registration between remote sensing images has been a research focus in the field of remote sensing image processing. Most of the existing image registration algorithms applied to feature point matching are derived from image feature extraction methods, such as scale-invariant feature transform (SIFT), speed-up robust features (SURF) and Siamese neural network. Such methods encounter difficulties in achieving accurate image registration where there is a large bias in the image features or no significant feature points. Aiming to solve this problem, this paper proposes an algorithm for multi-source image registration based on geographical location information (GLI). By calculating the geographic location information that corresponds to the pixel in the image, the ideal projected pixel position of the corresponding image is obtained using spatial coordinate transformation. Additionally, the corresponding relationship between the two images is calculated by combining multiple sets of registration points. The simulation experiment illustrates that, under selected common simulation parameters, the average value of the relative registration-point error between the two images is 12.64 pixels, and the registration accuracy of the corresponding ground registration point is higher than 6.5 m. In the registration experiment involving remote sensing images from different sources, the average registration pixel error of this algorithm is 20.92 pixels, and the registration error of the image center is 21.24 pixels. In comparison, the image center registration error given by the convolutional neural network (CNN) is 142.35 pixels after the registration error is manually eliminated. For the registration of homologous and featureless remote sensing images, the SIFT algorithm can only offer one set of registration points for the correct region, and the neural network cannot achieve accurate registration results. The registration accuracy of the presented algorithm is 7.2 pixels, corresponding to a ground registration accuracy of 4.32 m and achieving more accurate registration between featureless images. [ABSTRACT FROM AUTHOR]

Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing. [ABSTRACT FROM AUTHOR]

Imaging through the wavy water–air interface is challenging since the random fluctuations of water will cause complex geometric distortion and motion blur in the images, seriously affecting the effective identification of the monitored object. Considering the problems of image recovery accuracy and computational efficiency, an efficient reconstruction scheme that combines lucky-patch search and image registration technologies was proposed in this paper. Firstly, a high-quality reference frame is rebuilt using a lucky-patch search strategy. Then an iterative registration algorithm is employed to remove severe geometric distortions by registering warped frames to the reference frame. During the registration process, we integrate JADE and LBFGS algorithms as an optimization strategy to expedite the control parameter optimization process. Finally, the registered frames are refined using PCA and the lucky-patch search algorithm to remove residual distortions and random noise. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of sharpness and contrast. [ABSTRACT FROM AUTHOR]

Image registration of multimodal remote sensing images plays a vital role in remote sensing image analysis. However, there are significant nonlinear intensity differences between multimodal remote sensing image pairs, making it difficult for most traditional image registration algorithms to meet the registration requirements. In this paper, we propose a novel edge descriptor utilizing edge information, which has not only affine invariance but is also insensitive to nonlinear intensity differences. Moreover, we utilize the proposed descriptor to design a multimodal image registration algorithm. We use several different multimodal image pairs to evaluate the proposed algorithm. The experimental results show that the proposed algorithm holds a stable performance and can still achieve accurate spatial alignment even with the huge nonlinear intensity differences. [ABSTRACT FROM AUTHOR]

In the special sports camera, there are subframes. A lens is composed of multiple frames. It will be unclear if a frame is cut out. The definition of video screenshots lies in the quality of video. To get clear screenshots, we need to find clear video. The purpose of this paper is to analyze and evaluate the quality of sports video images. Through the semantic analysis and program design of video using computer language, the video images are matched with the data model constructed by research, and the real-time analysis of sports video images is formed, so as to achieve the real-time analysis effect of sports techniques and tactics. In view of the defects of rough image segmentation and high spatial distortion rate in current sports video image evaluation methods, this paper proposes a sports video image evaluation method based on BP neural network perception. The results show that the optimized algorithm can overcome the slow convergence of weights of traditional algorithm and the oscillation in error convergence of variable step size algorithm. The optimized algorithm will significantly reduce the learning error of neural network and the overall error of network quality classification and greatly improve the accuracy of evaluation. Sanda motion video image quality evaluation method based on BP (back propagation) neural network perception has high spatial accuracy, good noise iteration performance, and low spatial distortion rate, so it can accurately evaluate Sanda motion video image quality. [ABSTRACT FROM AUTHOR]

Despite all distortions and incompleteness present in Sketch maps, they can be used for different purposes such as collecting and searching spatial data and organizing the spatial knowledge of individuals. Since the prerequisite for all of these cases is matching sketches with existing data in spatial databases or metric maps, several studies have been conducted in this field. These studies generally focus on the relationships between routes, landmarks, and regions, while data sources do not necessarily contain all of these data. Also, the subject of the incompleteness of data and evaluation of the results has been less considered in previous studies. Therefore, in this paper, a new solution for matching routes from sketches to metric maps is presented. The proposed solution uses the Genetic Algorithm to measure the similarities between routes in two phases, and in each phase, a set of geometric/topological criteria is considered to improve the matching. The proposed algorithm is implemented to match routes of 25 sketches with the routes in a metric map. The average F Measure of matching is 45.59%. The results show that by considering the specific conditions (not availability of descriptive data of routes and geometric/descriptive information of landmarks), this paper has been able to take an important step toward finding an acceptable solution for the matching problem. The proposed methods can be used to deal with the incompleteness of the thematic information in metric maps. [ABSTRACT FROM AUTHOR]

Complex event processing is a technology for analyzing the event streams in a dynamic environment. Complex event processing technology is usually implemented based on finite state automaton. During the matching process, a large number of overlapping partial matches will be generated by the event stream. The finite state automaton needs to maintain a large number of repeated matching states, which leads to the problem of redundant calculation in the methods based on this technology. In order to improve the matching efficiency of complex event processing, this paper proposes a method of using complex event instance coverage technology to realize complex event processing. By designing a temporary matching chain partition storage structure and matching algorithms based on this structure, redundant calculations can be reduced using complex event instance coverage, thereby achieving an improvement in matching efficiency. Experiments are performed on simulated and real datasets, and compared with two commonly used complex event processing technologies. The experimental results show that the proposed method can effectively reduce the redundant computation in the matching process while ensuring the correctness of the matching, and improve the overall matching efficiency. [ABSTRACT FROM AUTHOR]

With the development of societies, the exploitation of mountains and forests is increasing to meet the needs of tourism, mineral resources, and environmental protection. The point cloud registration, 3D modeling, and deformation monitoring that are involved in surveying large scenes in the field have become a research focus for many scholars. At present, there are two major problems with outdoor terrestrial laser scanning (TLS) point cloud registration. First, compared with strong geometric conditions with obvious angle changes or symmetric structures, such as houses and roads, which are commonly found in cities and villages, outdoor TLS point cloud registration mostly collects data on weak geometric conditions with rough surfaces and irregular shapes, such as mountains, rocks, and forests. This makes the algorithm that set the geometric features as the main registration parameter invalid with uncontrollable alignment errors. Second, outdoor TLS point cloud registration is often characterized by its large scanning range of a single station and enormous point cloud data, which reduce the efficiency of point cloud registration. To address the above problems, we used the NARF + SIFT algorithm in this paper to extract key points with stronger expression, expanded the use of multi-view convolutional neural networks (MVCNN) in point cloud registration, and adopted GPU to accelerate the matrix calculation. The experimental results have demonstrated that this method has greatly improved registration efficiency while ensuring registration accuracy in the registration of point cloud data with weak geometric features. [ABSTRACT FROM AUTHOR]

A binocular camera composed of colour and depth cameras is an accurate and inexpensive tool for recognising fruit trees to plan spraying paths. For this purpose, however, the accurate visual registration of the depth and colour cameras is a problem that must be solved. This paper proposes a local feature point extraction (LFPE) algorithm for depth-colour image registration, which automatically extracts the feature points of local regions in the colour and depth images for registration. First, the binarised colour and depth images are divided into local regions after edge extraction, and the regions in which the edge features match at several pixels between images are selected. Then, these regions are again divided into smaller regions to detect local extrema of the pixels as feature points. Finally, the matching feature points are calculated based on the Euclidean distance and are used to optimise the affine transformation matrix. To verify the accuracy and effectiveness of the algorithm, it is compared with the FAST, SURF and BRISK algorithms. The experimental results show that the accuracy of the proposed LFPE algorithm is 1.6 times that of BRISK, 3.5 times that of FAST and 40.1 times that of SURF. Therefore, it is more suitable for registration between depth and colour images. • Proposed a depth-colour image registration method for orchard precision spraying. • A local feature point extraction (LFPE) algorithm is proposed to match images. • The registration accuracy of LFPE is much better than other similar algorithms. [ABSTRACT FROM AUTHOR]

The congruent matching cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) in 2012 for automatic and objective firearm evidence identifications and estimation of the weight of evidence in firearm evidence identifications. Since 2013, five CMC algorithms have been developed at NIST. In this paper, the virtual image standard (VIS) is proposed through trimming and stitching KNM images for quantitative performance evaluations of different CMC algorithms. The evaluation criteria include the correlation accuracy (both the CMC numbers and distribution pattern), correlation efficiency, false positive (FP) error rate, and the maximum separation of known matching (KM) and known non‐matching (KNM) image pairs. The VIS composes correlation cells from different KNM images, which can provide a ground truth for verifying the CMC numbers, distribution patterns, and FP errors. By identifying three groups of VIS, the Convergence CMC algorithm showed superior performances for the future casework in firearm evidence identifications. Lastly, the success of this study suggests that the VIS could also be used to optimize the correlation parameters, to develop and test new CMC algorithms, and evaluate the performance before it is put into use for firearm examiner's casework. [ABSTRACT FROM AUTHOR]

• A new image registration model is proposed. • An effective numerical method is proposed to solve the new model. • Numerical experiments show that our new model has good performance. In this paper, we propose a vectorial minimized surface regularizer based image registration model which is suitable for smooth and non-smooth registration. In order to avoid the mesh folding phenomenon, inequality constraint on transformed Jacobian matrix determinant is imposed. In addition, we use Lagrange multipliers combining Gauss-Newton method with Armijo line search with the multilevel method to solve the corresponding model. And guided filter is utilized on the displacement field before and after the registration of each level to avoid noise and preserve the edge information of the image. Furthermore, the convergence analysis of the algorithm is given. Finally, numerical experiments using both synthetic and realistic images are carried out to show the robustness of the proposed model and the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In emergency response and disaster rescue, unmanned aerial vehicles (UAVs) onboard thermal infrared (TIR) sensors are an essential means of acquiring ground information in the nighttime working environment. To enable field personnel to make better decisions based on TIR video streams returned from a UAV, the geographic information enhancement of TIR video streams is required. At present, it is difficult for low-cost UAVs to carry high-precision attitude sensors and thus obtain high-precision camera attitude information to meet the enhanced processing requirements of UAV TIR video streams. To this end, this paper proposes an improved Kalman filter algorithm to improve the geographic registration (geo-registration) accuracy by fusing the positioning and heading data from the dual-antenna real-time kinematic global positioning system (RTK-GPS) with onboard internal measurement unit (IMU) data. This method can yield high-precision position and attitude data in real time based on low-cost UAV hardware, based on which high-precision geo-registration results can be obtained. The computational complexity can be reduced compared with video stream feature tracking algorithms. Furthermore, the problem of unstable matching due to the low resolution and texture level of TIR video streams can be avoided. The experimental results prove that the proposed method can reduce the registration error by 66.15%, and significantly improve the geo-registration accuracy of UAV TIR video streams. Thus, it can strongly support the popularization and practicality of the application of augmented reality (AR) technology to low-cost UAV platforms. [ABSTRACT FROM AUTHOR]

Cultural landscapes are cultural property and they are an illustration of the evolution of human society and the living environment over time. As cultural landscape is being valued more and more, the use of 3D modeling is becoming more and more important. As for the 3D reconstruction technology, most of the current methods are complicated in terms of network construction, use, and storage, and then affect the reconstruction efficiency of subsequent cultural landscape heritage. To obtain the 3D reconstruction technology with high reconstruction efficiency, this paper combines the circumferential binary feature extraction algorithm and cloud computing technology, and proposes a circumferential binary feature extraction and matching search method. The interior-point rate of the CBD algorithm in this paper is greater than 72%, which is higher than the interior point rate of other different algorithms, which indicates that the CBD algorithm in this paper is suitable for matching HD rotated images. The experimental results show that the circular binary features extracted by the article have strong adaptability and fast contrast rate. To better the 3D reconstruction of cultural landscape heritage in the later period, this paper also improves the 4PSC point cloud rough registration algorithm. The experimental results show that compared with other coarse registration algorithms, the improved point cloud coarse registration algorithm improves the registration accuracy and the registration effect is good, which proves the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multi-source remote sensing images have the characteristics of large differences in texture and gray level. Mismatch and low recognition accuracy are easy to occur in the process of identifying targets. Thus, in this paper, the target recognition algorithm of multi-source remote sensing image based on IoT vision is investigated. The infrared sensor and SAR radars are set in the visual perception layer of the iVIOT. The visual perception layer transmits the collected remote sensing image information to the application layer through the wireless networks. The data processing module in the application layer uses the normalized central moment idea to extract the features of multi-source remote sensing image. Contourlet two-level decomposition is performed on the image after feature extraction to realize multi-scale and multi-directional feature fusion. A two-step method of primary fineness is used to match the fused features and the random sampling consensus algorithm is used to eliminate false matches for obtaining the correct match pairs. After the image feature matching is completed, the BVM target detection operator is used to complete the target recognition of multi-source remote sensing image. Experimental results show that the use of the IoT to visually recognizing the desired remote sensing image target has low communication overhead, and the recognition reaches 99% accuracy. [ABSTRACT FROM AUTHOR]

To solve the problems of low efficiency and poor stability of image matching with angle and scale changes, this paper proposed an improved mesh motion statistical feature point screening algorithm based on local clustering. Firstly, as seed points by local region suppression algorithm could screen feature points with high response intensity and paired appearance, and with seed points as the clustering center to obtain the minimum enclosing rectangle as the moving grid could segment images. Then, it divided the motion grid into 3 x 3 neighborhood supporting estimator grids, and it could calculate the maximum gradient value of the motion grid in eight directions as the main direction of the motion grid. Finally, it rotated the neighborhood support estimator grid of the image to be matched to the main direction of the target image motion grid, and the mesh motion statistics algorithm usually screened the matches. The experimental results show that the matching accuracy rate of the proposed algorithm is above 90% for matching images with JPEG compression transform, light change and fuzzy transform. For images with rotation and scale transformation, the matching accuracy of the proposed algorithm is about 10% higher than that of the moving grid statistic algorithm, up to more than 70%. The algorithm takes only 13 min and has high efficiency. It shows that the proposed algorithm can screen the correct matching points stably and efficiently. [ABSTRACT FROM AUTHOR]

Depth estimation is critical to 3D reconstruction and object location in intelligent agricultural machinery vision system, and a common method in it is stereo matching. Traditional stereo matching method used low-quality image extracted manually. Because the color and texture in the image of field plant is nonuniform, the artificial features in the image are poorly distinguishable and mismatching could occur as a result. This would compromise the accuracy of the depth of the map. While the supervised learning-based convolution neural network (CNN) is able to estimate the depth of each pixel in plant image directly, it is expensive to annotate the depth data. In this paper, we present a depth estimation model based on the self-supervised learning to phenotype tomato canopy. The tasks of the depth estimation method were to reconstruct the image. The dense disparity maps were estimated indirectly using the rectified stereo pair of images as the network input, from which a bilinear interpolation was used to sample the input images to reconstruct the warping images. We developed three channel wise group convolutional (CWGC) modules, including the dimension invariable convolution module, the down-sampling convolution module and the up-sampling convolution module, and used them to construct the convolutional auto-encoder - a key infrastructure in the depth estimation method. Considering the shortage of manual features for comparing image similarity, we used the loss in image convolutional feature similarity as one objective of the network training. A CWGC-based CNN classification network (CWGCNet) was developed to extract the low-level features automatically. In addition to the loss in image convolutional feature similarity, we also considered the whole training loss, which include the image appearance matching loss, disparity smoothness loss and left-right disparity consistency loss. A stereo pair of images of tomato was sampled using a binocular camera in a greenhouse. After epipolar rectification, the pair of images was constructed for training and testing of the depth estimation model. Using the Microsoft Cognitive Toolkit (CNTK), the CWGCNet and the depth estimation network of the tomato images were calculated using Python. Both training and testing experiments were conducted in a computer with a Tesla K40c GPU (graphics processing unit). The results showed that the shallow convolutional layer of the CWGCNet successfully extracted the low-level multiformity image features to calculate the loss in image convolutional feature similarity. The convolutional auto-encoder developed in this paper was able to significantly improve the disparity map estimated by the depth estimation model. The loss function in image convolutional feature similarity had a remarkable effect on accuracy of the image depth. The accuracy of the disparity map estimated by the model increased with the number of convolution modules for calculating the loss in convolutional feature similarity. When sampled within 9.0 m, the root means square error (RMSE) and the mean absolute error (MAE) of the corner distance estimated by the model were less than 2.5 cm and 1.8 cm, respectively, while when sampled within 3.0m, the associated errors were less than 0.7cm and 0.5cm, respectively. The coefficient of determination (R²) of the proposed model was 0.8081, and the test speed was 28 fps (frames per second). Compared with the existing models, the proposed model reduced the RMSE and MAE by 33.1% and 35.6% respectively, while increased calculation speed by 52.2%. [ABSTRACT FROM AUTHOR]

To improve the robustness of the existing multi-spectral image registration algorithms, this paper proposed a new algorithm based on multi-scale support region descriptors. Firstly, the algorithm extracted the Harris corner points as feature points. Secondly, it constructed the descriptor by combining the edge direction histograms calculated respectively in the support regions of different sizes around a feature point. Then, the similarity criterion was the Euclidean distance, and it obtained the initial matches by the ratio method. Finally, this paper proposed an outlier removal algorithm based on RANSAC algorithm. The experimental results show that the proposed algorithm can match multi-spectral images effectively, be more robust than the existing algorithms, and obtains more matches that are correct. [ABSTRACT FROM AUTHOR]

Wide-angle video frames typically contain more information, but they also exhibit distortion that degrades the visual quality, especially at the edges. To eliminate this distortion from videos, we propose a self-supervised iterative optimization method in this paper. Specifically, we construct a motion parameter estimation model utilizing two consecutive distorted frames, where motion parameters comprise affine transform and distortion parameters. We apply the Gauss–Newton algorithm to minimize the sum-of-squares error between frames and update parameters. Treating inter-frame motion as undistort-affine-distort transformations, frame alignment is achieved by continuously adjusting transform parameters. Ultimately, frames are corrected using the converged parameters. We generated a synthetic dataset with various distortion parameters for evaluation. Experiments demonstrate superior performance versus state-of-the-art methods on synthetic and real wide-angle videos. Our algorithm also achieves higher parameter estimation accuracy without sacrificing efficiency. • proposes a algorithm for video distortion correction based on iterative optimization. • the distort transform and undistort transform are derived separately and the range of parameters and the experimental error are given. • Based on this, we transform the distortion correction into a parameter estimation problem in image alignment. • optimize the parameter estimation problem using Gauss-Newton algorithm. • Experimental results show that the proposed algorithms outperform some state-of-art algorithms in accuracy, without reducing robustness and efficiency. [ABSTRACT FROM AUTHOR]

Domain Generalization (DG) model is an important tool to improve the robustness of person re-identification algorithm, but the domain gap makes it difficult to transfer knowledge cross-domain effectively. To solve the above problems, this paper proposes a generalization model based on a Meta-Bond Domain Alignment (M−BDA) model. To learn a generalizable model, a meta -learning strategy is introduced to simulate the training–testing process of the domain generalization. Then a bond-domain module is constructed in the training to align the source domain, which can reduce the domain gap between the source domain and the target domain, and facilitate the knowledge transfer. Finally, the bond-domain loss is counted on the feature space of the bond-domain to prevent the generated bond-domain from overfitting to the source domain. The experimental results show that the proposed algorithm achieves better performance in DukeMTMC-ReID, Market1501 and MSMT17 alternating as the source and the target domain tasks. In the generalization experiments of Market-1501 → DukeMTMC-ReID, mAP and Rank-1 increased by 7.7 % and 3.6 %, respectively, and in the generalization experiments of DukeMTMC-ReID → Market-1501, mAP and Rank-1 are increased by 9.3 % and 3.5 %, respectively, which are significantly better than the newer representative algorithms. [ABSTRACT FROM AUTHOR]

• A topology- and convexity-preserving registration-based segmentation model is proposed. • An efficient algorithm based on the alternating direction method of multipliers is devised to solve the proposed model. • Numerical tests illustrate that the model can lead to the topology- and convexity-preserving segmentations for 2/3D data. Image segmentation aims to extract the target objects or to identify the corresponding boundaries. For corrupted images due to occlusions, obscurities or noises, to get an accurate segmentation result is very difficult. To overcome this issue, the prior information is often introduced and particularly, the convex prior attracts more and more attentions recently. In this paper, we propose a topology- and convexity-preserving registration-based segmentation model, which can be suitable for both 2D and 3D cases. By incorporating the level set representation and imposing the constraints on the suitable regions, we can explicitly force the fully convex segmentation results or the partially convex segmentation results. To solve the proposed model, we employ the alternating direction method of multipliers and numerical experiments on 2/3D synthetic and real images demonstrate that the proposed model can indeed lead to the accurately topology- and convexity-preserving segmentation. [ABSTRACT FROM AUTHOR]

Image matching is critical for photorealistic 3D reconstruction based on oblique images. An efficient match pair selection methodology for matching large-scale oblique UAV images is proposed in this paper. The proposed methodology effectively uses existing geospatial data and prior knowledge about data acquisition to generate precise match pairs. First, the principal point of each image is directly georeferenced. The direct georeferencing is based on a novel new DEM-aided direct georeferencing algorithm which employs an iterative binary search in the elevation domain. Second, a terrain-adaptive search radius is calculated for each image based on the calculated ground points, POS data, and prior knowledge. Finally, initial match pairs are generated based on a camera-oriented approach. The camera-oriented approach generates match pairs using k nearest neighbours (knn) search and prior knowledge. False connections in the initial match pairs are filtered using geometrical constraints. A limited number of match pairs are kept to reduce the redundancy of the generated match graph. The proposed methodology is tested on a dataset containing 28,560 images. The experimental results show that the precision of the generated match pairs is 99.99%. An incremental 3D reconstruction of the scene is conducted based on the robustly matched images. More than 99.8% of all the images are successfully oriented. The profiling analysis shows that the proposed methodology generates all the match pairs in 7.719 seconds. The precision, efficiency, and robustness of the proposed methodology are comprehensively analysed. The experimental and simulation results show that the proposed methodology is efficient and precise for large-scale UAV photogrammetry. [ABSTRACT FROM AUTHOR]

Electric shovels are widely used in the mining industry to dig ore, and the teeth in shovels' bucket can be lost due to the tremendous pressure exerted by ore materials during operation. When the teeth fall off and enter the crusher with other ore materials, serious damages to crusher gears and other equipment happen, which causes millions of economic loss, because it is made of high-manganese steel. Thus, it is urgent to develop an efficient and automatic algorithm for detecting broken teeth. However, existing methods for detecting broken teeth have little effect and most research studies depended on sensor skills, which will be disturbed by closed cavity in shovel and not stable in practice. In this paper, we present an intelligent computer vision system for monitoring teeth condition and detecting missing teeth. Since the pixel-level algorithm is carried out, the amount of calculation should be reduced to improve the superiority of the algorithm. To release computational pressure of subsequent work, salient detection based on deep learning is proposed for extracting the key frame images from video flow taken by the camera installed on the shovel including the teeth we intend to analyze. Additionally, in order to more efficiently monitor teeth condition and detect missing teeth, semantic segmentation based on deep learning is processed to get the relative position of the teeth in the image. Once semantic segmentation is done, floating images containing the shape of teeth are obtained. Then, to detect missing teeth effectively, image registration is proposed. Finally, the result of image registration shows whether teeth are missing or not, and the system will immediately alert staff to check the shovel when teeth fall off. Through sufficient experiments, statistical result had demonstrated superiority of our presented model that serves more promising prospect in mining industry. [ABSTRACT FROM AUTHOR]

Imaging Satellites acquire multispectral images, MIs, in low resolution, LR, and panchromatic images, PANs, in high resolution, HR, due to some advantages provided in satellite design. The pansharpening, PS, is a super resolution image synthesis method that is used to generate the pansharpened image, PI, by fusion of PAN and MI. The use of PS process is unavoidable in applications such as efficient use of communication-bandwidth of imaging satellites and fusion of images derived from various image sensors. The PS process is a multi-step process consisting of various complex image processing stages, such as registration, resampling, synthesis, and fusion. The Weighted Differential Evolution Algorithm-based PS method, WDEPS, has been proposed in this paper. The WDEPS uses WDE to synthesize the intensity image, which is the blended-image of MI -bands. WDE has been used to compute the relevant image-blending weights, efficiently. In the experiments, several satellite images (QuickBird-2, Ikonos-2, and GeoEye-1) with different spatial resolutions were used. The WDEPS's experimental results have been compared with 17 well-known PS methods by using 9 full-reference and 3 blind image quality assessment metrics. The experimental results exposed that WDEPS generates high-quality PIs than traditional PS methods used in the experimental aspect of qualitative and quantitative assessment. [ABSTRACT FROM AUTHOR]

Feature matching in optical remote sensing images usually suffers from significant intensity differences and background variations. In this paper, an effective image matching approach is proposed to match salient structures in optical remote sensing images. The proposed method consists of three main steps. In the first step, EDLines algorithm is implemented to extract straight lines from two corresponding images, and these lines are further processed and divided into line segments for feature matching. In the second step, a robust descriptor namely line segment context (LSC) descriptor is computed for each line segment. An iterative procedure that combines line segment description and feature matching is presented for accurate matching. Finally, the consistency check is implemented to further refine the matching results. We conduct the experiments on pairs of optical remote sensing images, and experimental results show that the proposed method can automatically and robustly generate line segment features and achieve favourable performance compared to existing image matching approaches. [ABSTRACT FROM AUTHOR]

With the development of computer science, especially the application of 3D scanning technology in garment design, intelligent modeling is realized, which is impossible to achieve in traditional design methods. In this paper, we propose the 3D model construction of human garments based on the motion recovery structure method. The eigenmatrix is obtained from the camera parameters, and the transformation matrix is calculated by matching the image feature points with the help of scale-invariant feature conversion algorithm to realize the 3D reconstruction technology of human garments based on multiview image sequences. The effectiveness of this method is verified through experiments, and it has good robustness and accuracy. Through the form of style modeling, the design thinking and method can be extended to form a more reasonable garment structure and guide the innovation of garment production mode. [ABSTRACT FROM AUTHOR]

This paper presents a 3D reconstruction method for fast elevation determination on construction sites. The proposed method is intended to automatically and accurately determine construction site elevations using drone-based, low–high orthoimage pairs. This method requires fewer images than other methods for covering a large target area of a construction site. An up–forward–down path was designed to capture approximately 2∶1 -scale images at different altitudes over target stations. A pixel grid matching and elevation determination algorithm was developed to automatically match images in dense pixel grid-style via self-adaptive patch feature descriptors, and simultaneously determine elevations based on a virtual elevation model. The 3D reconstruction results were an elevation map and an orthoimage at each station. Then, the large-scale results of the entire site were easily stitched from adjacent results with narrow overlaps. Moreover, results alignment was automatically performed via the U-net detected ground control point. Experiments validated that in 10–20 and 20–40 orthoimage pairs, 92% of 2,500- and 4,761-pixels were matched in the strongest and strong levels, which was better than sparse reconstructions via structure from motion; moreover, the elevation measurements were as accurate as photogrammetry using multiscale overlapping images. [ABSTRACT FROM AUTHOR]

Aiming at the requirement of obtaining the panorama of the worktable in the automatic cutting system of workpiece groove based on machine vision, an image registration algorithm based on SIFT and improved PROSAC is proposed. First, SIFT algorithm is used for feature detection and feature description. Then, the bidirectional matching and cosine similarity method are used for rough matching of feature points. Finally, an improved PROSAC algorithm is proposed, which purifies the matching points and calculates the image transformation matrix. In image fusion, the weighted average method is used to fuse the overlapping parts of the image to obtain a whole image of the cutting platform. Experimental results show that the algorithm in this paper has been improved in terms of matching accuracy and time-consuming compared with several classical algorithms. [ABSTRACT FROM AUTHOR]

In order to fuse heterogeneous data with high precision, then express the buildings roof and facade information fully, this paper proposed a registration method of terrain laser scanning point cloud and image matching point cloud based on building footprint features. It extracted image matching point cloud building roof edge by boundary estimation, got terrain laser scanning point cloud building eaves boundary by a-shape algorithm, completed two footprint point cloud' s coarse registration by principal component analysis algorithm, centroid constraint and Rodrigues' s formula, finished fine registration according to the ICP algorithm. Experimental results show that the method can combine the advantages of cross-modal data, and improve the calculation efficiency and registration accuracy of image and point cloud registration effectively. [ABSTRACT FROM AUTHOR]