Soft decision optimization method for robust fundamental matrix estimation

Xiao, Chun-Bao; Feng, Da‐Zheng; Yuan, Ming‐Dong

doi:10.1007/s00138-019-01019-7

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…We adopt the two testing datasets from the Affine Covariant Regions Datasets and USAC Datasets.The two Datasets were released by the Oxford University.We compared the proposed method with other state-of-art robust methods(M-estimator [10], LmeSeig [11], RANSAC [14], FNS [26], SDO [18], L2E-LSC [20]) using simulated data that contained different levels of Gaussian noise and false matches, as well as real images with different scenarios. In which process, the average epipolar distance of inliers were used as the evaluation performance criteria to test the accuracy [7].…”

Section: Resultsmentioning

confidence: 99%

“…As small a subset of the data as is feasible to estimate the parameters is used (e.g, seven correspondences for fundamental matrix estimation), and this process is repeated enough times, trying to find the largest consensus on an estimated F-matrix. Later, some researchers have presented different methods for improvement, such as LMedSeig, MLESAC and MAPSAC, SDO, etc [11], [15]- [18]. The key idea of these algorithms is to remove outliers independently based on the hypothesis testing strategy.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Fundamental Matrix Estimation Method Based on Epipolar Geometric Error Criterion

et al. 2019

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Fundamental Matrix Estimation Method Based on Epipolar Geometric Error Criterion

et al. 2019

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“…The online process quickly receives the input data stream, and saves the clustering results it produces as the intermediate results of mining for users to query offline. Through online and offline processes, dynamic and fast data 4 Wireless Communications and Mobile Computing flow processing is realized, and users' needs for data flow analysis can be well met [24].…”

Section: Data Streammentioning

confidence: 99%

Decision‐Making Optimization Design of Enterprise Standardization Management Planning Based on Mobile Network System

Wang

Wang²,

Ye³

2021

Wireless Communications and Mobile Computing

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With the continuous deepening of enterprise management and market competition, the pressure of production and operation of enterprises is increasing, and it is urgent to improve the management level of enterprises. This paper mainly studies the decision optimization design of enterprise standardization management planning based on mobile network system. In this paper, the idea of clustering is used to integrate the customers into a customer clustering mode, which will greatly reduce the number of customers studied in the model and simplify the optimization process of the model. Through online and offline processes, these can achieve dynamic and rapid processing of data flow and can well meet the needs of users for data flow analysis. In the construction of enterprise standardization strategic performance evaluation index system, it is necessary to decompose, combine, and converge the index system. According to the construction principles and methods discussed above, the influencing factors of standardized strategic performance evaluation indicators of construction enterprises are preliminarily stratified by using the expert survey method and cluster analysis method, so as to construct the initial evaluation system. For the mobile network system testing, we use the black box test and white box test. The test content mainly includes two modules: area calculation and label calculation. Experimental data show that when the time threshold Ts is set to 1 hour, the prediction accuracy of TBM increases from 46.9% to 47.7%. The results show that the mobile network system realizes the whole life cycle management of enterprise standards and improves the management level of enterprise standardization work.

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Estimating the fundamental matrix based on the end-to-end convolutional network

Yang

Zhang

2022

Appl Intell

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Soft decision optimization method for robust fundamental matrix estimation

Cited by 5 publications

References 39 publications

A Robust Fundamental Matrix Estimation Method Based on Epipolar Geometric Error Criterion

A Robust Fundamental Matrix Estimation Method Based on Epipolar Geometric Error Criterion

Decision‐Making Optimization Design of Enterprise Standardization Management Planning Based on Mobile Network System

Estimating the fundamental matrix based on the end-to-end convolutional network

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