基于采样优化的随机抽取一致性算法
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摘要
为了提高随机抽取一致性算法(RANSAC)的效率和精度,提出了一种基于采样优化的随机抽取一致性算法。首先通过匹配点的相似性度量计算匹配点先验概率,根据先验概率随机抽取最小子集估计模型,在全部数据上检验模型,依次迭代找到次优模型;然后以次优模型对应的内点集作为采样的初始集,随机抽取最小子集估计模型,并在全部数据上检验模型,若模型更好则更新采样初始集,依次迭代找到最优模型;最后,选择最优模型获得符合该模型的内点和最终的模型参数。选取多对不同变换的图像作为实验数据,从算法运行效率和模型精确度两方面对算法进行了测试实验。实验数据表明,本文算法的迭代次数约为标准RANSAC算法的20%,运行时间约为标准RANSAC算法的25%,标准误差降低了30%左右。本文算法充分利用了匹配点的先验知识和模型检验结果对采样模式进行优化,算法的运行效率和精度都有较大提高。
This paper presents a fast and accurate Random Sample Consensus( RANSAC) algorithm based on sampling optimization. Firstly,the prior probability of the matching points is calculated by the similarity measurement of matching points,and the minimum subset for model fitting is selected randomly according to the prior probability,which is tested on all the data,until the suboptimal model is found through iteration. Then,the interior point set corresponding to the suboptimal model is used as the initial set for sampling and the minimum subset of the model is randomly extracted and tested on all the data. If the model is better,then the initial set is updated,and the optimal model is found through iteration. Finally,the optimal model is selected,and the interior point and the final model parameters are obtained. The images with different changes are selected as the experimental data,and the algorithm is tested on both the algorithm operation efficiency and the model precision.The experimental data show that the number of iterations in this algorithm is about 20%,and its running time is about 25% of the standard RANSAC algorithm,and the standard square root error is reduced by about 30%. This paper makes full use of the prior knowledge of the matching point and the results of the model test to optimize the sampling mode,so that the operation efficiency and precision of the algorithm are greatly improved.
This paper presents a fast and accurate Random Sample Consensus( RANSAC) algorithm based on sampling optimization. Firstly,the prior probability of the matching points is calculated by the similarity measurement of matching points,and the minimum subset for model fitting is selected randomly according to the prior probability,which is tested on all the data,until the suboptimal model is found through iteration. Then,the interior point set corresponding to the suboptimal model is used as the initial set for sampling and the minimum subset of the model is randomly extracted and tested on all the data. If the model is better,then the initial set is updated,and the optimal model is found through iteration. Finally,the optimal model is selected,and the interior point and the final model parameters are obtained. The images with different changes are selected as the experimental data,and the algorithm is tested on both the algorithm operation efficiency and the model precision.The experimental data show that the number of iterations in this algorithm is about 20%,and its running time is about 25% of the standard RANSAC algorithm,and the standard square root error is reduced by about 30%. This paper makes full use of the prior knowledge of the matching point and the results of the model test to optimize the sampling mode,so that the operation efficiency and precision of the algorithm are greatly improved.
引文
[1]缪君,储珺,张桂梅.少量交互的多视角图像目标分割算法[J].计算机辅助设计与图形学学报,2017,29(1):115-123.
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[9]田文,王宏远,徐帆,等.RANSAC算法的自适应Tc,d预检验[J].中国图象图形学报,2009,14(5):973-977.
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[12]贾丰蔓,康志忠,于鹏.影像同名点匹配的SIFT算法与贝叶斯抽样一致性检验[J].测绘学报,2013,42(6):877-883.
[13]BAY H,TUYTELAARS T,VAN G L.SURF:speeded up robust features[C]∥Proceedings of 9th European Conference on Computer Vision,Graz:Springer Berlin Heidelberg,2006:404-417.
[14]卞玉霞,刘学军,刘丹.RANSAC估计基础矩阵的不确定性评价[J].地理与地理信息科学,2015,31(1):37-40.
[15]李珍,康志忠,贾丰蔓,等.利用贝叶斯抽样一致性的点云特征面拟合[J].测绘科学,2015,40(8):92-96.
[2]丁海燕,刘合辉,刘春菊.SIFT遥感图像快速配准方法[J].地理空间信息,2017,15(2):69-71.
[3]ZHANG X P,WANG J Q,ZHANG Y X,et al.Large-scale three dimensional stereo vision geometric measurement system[J].Acta Optica Sinica,2012,32(3):148-155.
[4]呼艳,耿国华,周明全,等.基于未标定彩色图像三维重建的立体匹配算法[J].计算机科学,2015,43(5):473-478.
[5]管秋,金俊杰,张剑华,等.基于最优RANSAC算法的非增加式多视图三维重建[J].浙江工业大学学报,2015,43(5):473-478.
[6]FSCHLER M,BOLLES R.Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J].Communication of the ACM,1981,24(6):381-395.
[7]MATAS J,CHUM O.Randomized RANSAC with sequential probability ratio test[C]∥Proceedings of the 10th IEEE International Conference on Computer Vision,Beijing,2005:1727-1732.
[8]陈付幸,王润生.基于预检验的快速随机抽样一致性算法[J].软件学报,2005,16(8):1431-1437.
[9]田文,王宏远,徐帆,等.RANSAC算法的自适应Tc,d预检验[J].中国图象图形学报,2009,14(5):973-977.
[10]TORDOFF B J,MRRAY D W.Guided-MLESAC:faster image transform estimation by using matching priors[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2005,27(10):1523-1535.
[11]CHUM O,MATAS J.Matching with PROSAC-progressive sample consensus[C]∥Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005:220-226.
[12]贾丰蔓,康志忠,于鹏.影像同名点匹配的SIFT算法与贝叶斯抽样一致性检验[J].测绘学报,2013,42(6):877-883.
[13]BAY H,TUYTELAARS T,VAN G L.SURF:speeded up robust features[C]∥Proceedings of 9th European Conference on Computer Vision,Graz:Springer Berlin Heidelberg,2006:404-417.
[14]卞玉霞,刘学军,刘丹.RANSAC估计基础矩阵的不确定性评价[J].地理与地理信息科学,2015,31(1):37-40.
[15]李珍,康志忠,贾丰蔓,等.利用贝叶斯抽样一致性的点云特征面拟合[J].测绘科学,2015,40(8):92-96.