An Improved RANSAC Algorithm Based on Similar Structure Constraints

Zhu, Weiren; Sun, Wenjing; Wang, Yexiang; Liu, Shaolin; Ke-ke, XU

doi:10.1109/icris.2016.19

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…It sampled a minimal subset iteratively, estimated the fundamental matrix model, and obtained the consistent inliers by verifying the quality of the set's number. Many approaches have been developed to improve RANSAC [18][19][20][21]. The shortcomings of these resampling methods are the runtime will exponentially increase when the outlier percentage in the putative set is high, and the estimated parametric model is less-efficient undergoing non-rigid transformations which are more complex.…”

Section: Related Workmentioning

confidence: 99%

Similar image matching via global topology consensus

2023

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Recovering three-dimensional structure from images is one of the important researches in computer vision. The quality of feature matching is one of the keys to obtaining more accurate results. However, as different objects or different surfaces of objects have similar images with the same elements and different typography, the camera pose estimation will be wrong and the task will fail. This paper proposes a new mismatch elimination algorithm based on global topology consistency. We first formulate the matching task as a mathematical model based on the global constraints, then convert the feature matching into grid matching, calculate the confidence of the grids according to the changes in the angle and displacement between correspondence grid vectors, and remove the mismatches with low confidence. The experiments have demonstrated that our proposed method performs better than the state-of-the-art feature matching methods to accomplish outlier match rejection in the task of similar image matching and could be helped to obtain the correct camera pose to reconstruct more complete and more accurate object models.

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Section: Related Workmentioning

confidence: 99%

Similar image matching via global topology consensus

2023

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“…In our previous work [11], a robust method was proposed, which is based on comparing the triangular topologies of the feature points. Zhu et al put forward the method, based on the similar structure constraints of the feature points [12]. Luo et al analyze the relationship of the Euclidean distance between the feature points and then corrects the mismatch on the basis of the angular cosine [13].…”

Section: Geometry-based Mismatching Removalmentioning

confidence: 99%

Mismatching Removal for Feature-Point Matching Based on Triangular Topology Probability Sampling Consensus

Shen

Wang

et al. 2022

Remote Sensing

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Feature-point matching between two images is a fundamental process in remote-sensing applications, such as image registration. However, mismatching is inevitable, and it needs to be removed. It is difficult for existing methods to remove a high ratio of mismatches. To address this issue, a robust method, called triangular topology probability sampling consensus (TSAC), is proposed, which combines the topology network and resampling methods. The proposed method constructs the triangular topology of the feature points of two images, quantifies the mismatching probability for each point pair, and then weights the probability into the random process of RANSAC by calculating the optimal homography matrix between the two images so that the mismatches can be detected and removed. Compared with the state-of-the-art methods, TSAC has superior performances in accuracy and robustness.

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“…If the amount of calculation per iteration is 50, then l is 1000. In [41], the RANSAC algorithm is used for image matching. The upper limit of the number of iterations is 400.…”

Section: Comparative Analysismentioning

confidence: 99%

PECSA: Practical Edge Computing Service Architecture Applicable to Adaptive IoT-Based Applications

Liu

2021

Future Internet

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The cloud-based Internet of Things (IoT-Cloud) combines the advantages of the IoT and cloud computing, which not only expands the scope of cloud computing but also enhances the data processing capability of the IoT. Users always seek affordable and efficient services, which can be completed by the cooperation of all available network resources, such as edge computing nodes. However, current solutions exhibit significant security and efficiency problems that must be solved. Insider attacks could degrade the performance of the IoT-Cloud due to its natural environment and inherent open construction. Unfortunately, traditional security approaches cannot defend against these attacks effectively. In this paper, a novel practical edge computing service architecture (PECSA), which integrates a trust management methodology with dynamic cost evaluation schemes, is proposed to address these problems. In the architecture, the edge network devices and edge platform cooperate to achieve a shorter response time and/or less economic costs, as well as to enhance the effectiveness of the trust management methodology, respectively. To achieve faster responses for IoT-based requirements, all the edge computing devices and cloud resources cooperate in a reasonable way by evaluating computational cost and runtime resource capacity in the edge networks. Moreover, when cooperated with the edge platform, the edge networks compute trust values of linked nodes and find the best collaborative approach for each user to meet various service requirements. Experimental results demonstrate the efficiency and the security of the proposed architecture.

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An Improved RANSAC Algorithm Based on Similar Structure Constraints

Cited by 12 publications

References 4 publications

Similar image matching via global topology consensus

Similar image matching via global topology consensus

Mismatching Removal for Feature-Point Matching Based on Triangular Topology Probability Sampling Consensus

PECSA: Practical Edge Computing Service Architecture Applicable to Adaptive IoT-Based Applications

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