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As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of m...
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As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions about which method would be a suitable choice for specific applications with respect to different scenarios and task requirements and how to design better image matching methods with superior performance in accuracy, robustness and efficiency. This encourages us to conduct a comprehensive and systematic review and analysis for those classical and latest techniques. Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the development of these methods in theory and practice. Secondly, we briefly introduce several typical image matching-based applications for a comprehensive understanding of the significance of image matching. In addition, we also provide a comprehensive and objective comparison of these classical and latest techniques through extensive experiments on representative datasets. Finally, we conclude with the current status of image matching technologies and deliver insightful discussions and prospects for future works. This survey can serve as a reference for (but not limited to) researchers and engineers in image matching and related fields.
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Several feature descriptors have been proposed in the literature with a variety of definitions and a common goal, describe and get the best possible match between potentially interesting points in two images. In this paper, we pro...
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Several feature descriptors have been proposed in the literature with a variety of definitions and a common goal, describe and get the best possible match between potentially interesting points in two images. In this paper, we proposed a new orientation invariant feature descriptor without an additional step dedicated to this task. We exploited the information provided by two representations of the image (intensity and gradient) for a better understanding and representation of the feature point and its surroundings. The information provided is summarised in two cumulative histograms and used in the description and matching process of the feature points. The experimental results show its robustness in the face of multiple image changes.
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Computer vision applications that involve the matching of local image features frequently use Ratio-Match as introduced by Lowe and others, but is this really the optimal approach? We formalize the theoretical foundation of Ratio-...
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Computer vision applications that involve the matching of local image features frequently use Ratio-Match as introduced by Lowe and others, but is this really the optimal approach? We formalize the theoretical foundation of Ratio-Match and propose a general framework encompassing Ratio-Match and three other matching methods. Using this framework, we establish a theoretical performance ranking in terms of precision and recall, proving that all three methods consistently outperform or equal Ratio-Match. We confirm the theoretical results experimentally on over 3000 image pairs and show that matching precision can be increased by up to 20 percentage-points without further assumptions about the images we are using. These gains are achieved by making only a few key changes of the Ratio-Match algorithm that do not affect computation times. (C) 2016 Elsevier B.V. All rights reserved.
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Satellite remote sensing has entered the era of big data due to the increase in the number of remote sensing satellites and imaging modes. This presents significant challenges for the processing of remote sensing systems and will ...
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Satellite remote sensing has entered the era of big data due to the increase in the number of remote sensing satellites and imaging modes. This presents significant challenges for the processing of remote sensing systems and will result in extremely high real-time data processing requirements. The effective and reliable geometric positioning of remote sensing images is the foundation of remote sensing applications. In this paper, we propose an optical remote sensing image matching method based on a simple stable feature database. This method entails building the stable feature database, extracting local invariant features that are comparatively stable from remote sensing images using an iterative matching strategy, and storing useful information about the features. Without reference images, the feature database-based matching approach potentially saves storage space for reference data while increasing image processing speed. To evaluate the performance of the feature database matching method, we train the feature database with various local invariant feature algorithms on different time phases of Gaofen-2 (GF-2) images. Furthermore, we carried out matching comparison experiments with various satellite images to confirm the viability and stability of the feature database-based matching method. In comparison with direct matching using the classical feature algorithm, the feature database-based matching method in this paper can essentially improve the correct rate of feature point matching by more than 30% and reduce the matching time by more than 40%. This method improves the accuracy and timeliness of image matching, potentially solves the problem of large storage space occupied by the reference data, and has great potential for fast matching of optical remote sensing images.
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Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate e...
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Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight re-estimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed.
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Image feature detection and matching is a fundamental operation in image processing. As the detected and matched features are used as input data for high-level computer vision algorithms, the matching accuracy directly influences ...
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Image feature detection and matching is a fundamental operation in image processing. As the detected and matched features are used as input data for high-level computer vision algorithms, the matching accuracy directly influences the quality of the results of the whole computer vision system. Moreover, as the algorithms are frequently used as a part of a real-time processing pipeline, the speed at which the input image data are handled is also a concern. The paper proposes an embedded system architecture for feature detection and matching. The architecture implements the FAST feature detector and the BRIEF feature descriptor and is capable of establishing key point correspondences in the input image data stream coming from either an external sensor or memory at a speed of hundreds of frames per second, so that it can cope with most demanding applications. Moreover, the proposed design is highly flexible and configurable, and facilitates the trade-off between the processing speed and programmable logic resource utilization. All the designed hardware blocks are designed to use standard, widely adopted hardware interfaces based on the AMBA AXI4 interface protocol and are connected using an underlying direct memory access (DMA) architecture, enabling bottleneck-free inter-component data transfers.
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Visual object tracking using deep features has achieved great success, particularly when object appearances change in the presence of illumination variation, occlusion, in-plane rotation, scaling, and fast motion. In the state-of-...
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Visual object tracking using deep features has achieved great success, particularly when object appearances change in the presence of illumination variation, occlusion, in-plane rotation, scaling, and fast motion. In the state-of-the-art approaches, the common model for object tracking is developed to address appearance variations with coexisting challenges. When the object features differ from appearance variations, the common model approach is ineffective, with simultaneous challenges. To alleviate these limitations, in this paper, a visual object tracking framework is proposed that relies on an adaptive object appearance feature update and template overlap maximization. The tracked object location is identified by performing feature matching between the previous frame's tracked template and the scaled templates' feature vectors in the current frame. Feature vectors are formed using template color and pretrained deep features. To adapt to appearance variations, the proposed tracking model updates the tracked template feature vector and the spatial information for target tracking in the successive frames. The experimental results on challenging video sequences in object tracking benchmarks demonstrate that the proposed tracking model can track objects with precision of 88.7% at a 12 frames/second tracking speed. The qualitative analysis shows that the proposed tracking model outperforms the related conventional trackers.
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A primary shortcoming of existing techniques for three-dimensional (3D) model matching is the reliance on global information of the model's structure. Models are matched in their entirety, depending on overall topology and geometr...
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A primary shortcoming of existing techniques for three-dimensional (3D) model matching is the reliance on global information of the model's structure. Models are matched in their entirety, depending on overall topology and geometry information. A currently open challenge is how to perform partial matching. Partial matching is important for finding similarities across part models with different global shape properties and for the segmentation and matching of data acquired from 3D scanners. This paper presents a Scale-Space feature extraction technique based on recursive decomposition of polyhedral surfaces into surface patches. The experimental results presented in this paper suggest that this technique can potentially be used to perform matching based on local model structure. In our previous work, Scale-Space decomposition has been used successfully to extract features from mechanical artifacts. Scale-Space techniques can be parameterized to generate decompositions that correspond to manufacturing, assembly or surface features relevant to mechanical design. One application of these technique is to support matching and content-based retrieval of solid models. This paper shows how a Scale-Space technique can extract features that are invariant with respect to the global structure of the model as well as small perturbations that 3D laser scanning process introduce. In order to accomplish this, we introduce a new distance function defined on triangles instead of points. We believe this technique offers a new way to control the feature decomposition process, which results in the extraction of features that are more meaningful from an engineering viewpoint. The new technique is computationally practical for use in indexing large models. Examples are provided that demonstrate effective feature extraction on 3D laser scanned models. In addition, a simple sub-graph isomorphism algorithm was used to show that the feature adjacency graphs, obtained through feature extraction, are meaningful descriptors of 3D CAD objects. All of the data used in the experiments for this work is freely available at: http://www.designrepository.org/datasets/.
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This paper addresses the problem of establishing correspondences between two sets of visual features using higher order constraints instead of the unary or pairwise ones used in classical methods. Concretely, the corresponding hyp...
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This paper addresses the problem of establishing correspondences between two sets of visual features using higher order constraints instead of the unary or pairwise ones used in classical methods. Concretely, the corresponding hypergraph matching problem is formulated as the maximization of a multilinear objective function over all permutations of the features. This function is defined by a tensor representing the affinity between feature tuples. It is maximized using a generalization of spectral techniques where a relaxed problem is first solved by a multidimensional power method and the solution is then projected onto the closest assignment matrix. The proposed approach has been implemented, and it is compared to state-of-the-art algorithms on both synthetic and real data.
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Most existing approaches prunewrong matches via estimating an image transformation or solving a graph-based global matching optimization problem, which usually suffers fromvarying local transformations and outliers. Inspired by th...
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Most existing approaches prunewrong matches via estimating an image transformation or solving a graph-based global matching optimization problem, which usually suffers fromvarying local transformations and outliers. Inspired by the insight that neighboring truematches usually hold consistent local topological structures across images, in this paper we propose a new approach to evaluate the confidence of each putative match based on how well its two keypoints can predict each other by exploring the geometric constraintwith its neighboringmatches. With the evaluation, a two- stage approach combining recursively false match pruning and correct match incrementing is presented to obtain the reliable matches. Experiments on various image pairs demonstrate that our approach can conduct robust feature matching in challenging conditions and outperform state-of-theart approaches. (c) 2020 Elsevier B.V. All rights reserved.
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