基于特征距离与内点的随机抽样一致性算法

张岩; 孙世宇; 胡永江; 李建增; 范聪

doi:10.11999/JEIT170703

基于特征距离与内点的随机抽样一致性算法

doi: 10.11999/JEIT170703

计量
- 文章访问数: 1381
- HTML全文浏览量: 108
- PDF下载量: 206
- 被引次数: 0
出版历程
- 收稿日期: 2017-07-17
- 修回日期: 2017-12-13
- 刊出日期: 2018-04-19

Random Sample Consensus Algorithm Based on Feature Distance and Inliers

摘要

摘要: 为了提高随机抽样一致性算法在特征匹配中的执行速度，该文提出一种基于特征距离与内点的随机抽样一致性算法。首先提出基于特征距离的先验概率引导方法，来提高算法每步循环找到正确模型的概率。其次提出基于样本集与内点的随机抽样及计算方法，来提高算法的收敛速度。最后提出基于最大值不变的循环跳出方法，来满足所提循环方法的跳出，同时提高运行效率。通过理论证明与实验验证，该文提出的算法在保证算法鲁棒性的同时，提高了执行速度。
- 模型估计 /
- 随机抽样一致性 /
- 特征距离 /
- 内点抽样 /
- 单应性矩阵
Abstract: In order to improve the operation efficiency of the RANdom SAmple Consensus (RANSAC) in feature registration, the Random Sample Consensus based on Feature Distance and Inliers (RSCFDI) is proposed. Firstly, the priori probability guidance method based on feature distance is proposed for increasing the probability of searching correct model in each loop. Then, to increase the convergence rate, the random sampling and calculation method based on sample set and inliers is adopted. Finally, the jumping out loop based on unchanged maximum is proposed to fit the proposed loop breaking, and the execution speed is elevated. The theoretical proof and experimental results show that the RSCFDI ensures the robustness of the algorithm, and the operation efficiency is improved.
- Model estimation /
- RANdom SAmple Consensus (RANSAC) /
- Feature distance /
- Inliers sample /
- Homograph matrix

HTML全文

参考文献(25)

丁海燕, 刘合辉, 刘春菊. SIFT遥感影像快速配准方法[J]. 地理空间信息, 2017, 15(2): 69-71. doi: 10.3969/j.issn.1672-4623. 2017.02.022.

FISCHLER M and 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. doi: 10.1145/358669.358692.

DING Haiyan, LIU Hehui, and LIU Chunju. Fast registration method of SIFT remote sensing image[J]. Geospatial Information, 2017, 15(2): 69-71. doi: 10.3969/j.issn.1672- 4623.2017.02.022.

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. doi: 10.3969/j.issn.1006-4303.2015.05.001.

GUAN Qiu, JIN Junjie, ZHANG Jianhua, et al. Non- sequential multi-view 3D reconstruction based on ac-RANSAC[J]. Journal of Zhejiang University of Technology, 2015, 43(5): 473-478. doi: 10.3969/j.issn.1006-4303.2015.05. 001.

李寰宇, 毕笃彦, 杨源, 等. 基于深度特征表达与学习的视觉跟踪算法研究[J]. 电子与信息学报, 2015, 37(9): 2033-2039. doi: 10.3969/j.issn.0372-2112.2016.03.008.

LI Huanyu, BI Duyan, YANG Yuan, et al. Research on visual tracking algorithm based on deep feature expression and learning[J]. Journal of Electronics Information Technology, 2015, 37(9): 2033-2039. doi: 10.3969/j.issn.0372-2112.2016.03. 008.

吴剑亮, 李艳, 高扬, 等. 基于改进RANSAC算法的道路直线提取方法[J]. 地理空间信息, 2017, 15(5): 42-44. doi: 10.3969/j.issn.1672-4623.2017.05.013.

WU Jianliang, LI Yan, GAO Yang, et al. Road straight line extraction method based on improved RANSAC algorithm [J]. Geospatial Information, 2017, 15(5): 42-44. doi: 10.3969/ j.issn.1672-4623.2017.05.013.

余淮, 杨文. 一种无人机航拍影像快速特征提取与匹配算法[J]. 电子与信息学报, 2016, 38(3): 509-516. doi: 10.11999/ JEIT150676.

YU Huai and YANG Wen. A fast feature extraction and matching algorithm for unmanned aerial vehicle images[J]. Journal of Electronics Information Technology, 2016, 38(3): 509-516. doi: 10.11999/ JEIT150676.

MATAS J and CHUM O. Randomized RANSAC with sequential probability ratio test[C]. Proceedings of the 10th IEEE International Conference on Computer Vision, Beijing, China, 2005: 1727-1732. doi: 10.1109/ICCV.2005.198.

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. doi: 10.1360/jos161431.

CHEN Fuxing and WANG Runsheng. Fast RANSAC with preview model parameters evaluation[J]. Journal of Software, 2005, 16(8): 1431-1437. doi: 10.1360/jos161431.

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.

TIAN Wen, WANG Hongyuan, XU Fan, et al. Enhanced RANSAC with adaptive pre-verification[J]. Journal of Image and Graphics, 2009, 14(5): 973-977.

TORDOFF B J and MURRAY D W. Guided-MLESAC: Faster image transform estimation by using matching priors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1523-1535. doi: 10.1109/TPAMI. 2005.199.

CHUM O and MATAS J. Matching with PROSAC- progressive sample consensus[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington DC, USA, 2005: 220-226. doi: 10.1109/CVPR.2005.221.

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.

JIA Fengman, KANG Zhizhong, and YU Peng. A SIFT and Bayes sampling consensus method for image matching[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(6): 877-883.

MIKOLAJCZYK K, TUYTELAARS T, SCHMID C, et al. A comparison of affine region detectors[J]. International Journal of Computer Vision, 2005, 65(1-2): 43-72. doi: 10.1007/s11263-005-3848-x.

PABLO F, JESUS N, and ADRIEN B. Fast explicit diffusion for accelerated features in nonlinear scale spaces[C]. Proceedings of the British Machine Vision Conference, London, Britain, 2013: 2157-2163. doi: 10.5244/c.27.13.

PABLO F, ADRIEN B, and ANDREW J. KAZE features[C]. Proceedings of the 12th European Conference on Computer Vision, Florence, Italy, 2012: 214-227. doi: 10.1007/978-3- 642-33783-3_16.

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. doi: 10.3969/j.issn.1003-9775.2017.01.014.

LIAO Jun, CHU Jun, and ZHANG Guimei. Object segmentation from multi-views images with a few interactions[J]. Journal of Computer-Aided Design Computer Graphics, 2017, 29(1): 115-123. doi: 10.3969/j.issn. 1003-9775.2017.01.014.

施引文献

资源附件(0)

访问统计