Removal of mismatches in scale-invariant feature transform algorithm using image depth information

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (12): 3554-3559.

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Removal of mismatches in scale-invariant feature transform algorithm using image depth information

LIU Zheng¹,LIU Yongben²

1. College of Computer Science and Technology, Guizhou University, Guiyang Guizhou 550025, China;
2. College of Big Data and Information Engineering, Guizhou University, Guiyang Guizhou 550025, China

Received:2014-06-30 Revised:2014-08-26 Online:2014-12-31 Published:2014-12-01
Contact: LIU Zheng

基于图像深度信息的尺度不变特征变换算法误匹配点对剔除

刘政¹,刘本永²

1. 贵州大学计算机科学与技术学院,贵阳 550025
2. 贵州大学大数据与信息工程学院,贵阳 550025

通讯作者: 刘政
作者简介:刘政(1990-),男,湖北黄冈人,硕士研究生,主要研究方向:图像处理、模式识别；刘本永(1966-),男,贵州兴仁人,教授,博士生导师,博士,主要研究方向:图像处理、模式识别。
基金资助:
国家自然科学基金资助项目;科技部国际合作项目;贵州省工业科技攻关项目;教育部高等院校博士点基金资助项目;贵州大学研究生创新基金资助项目

Abstract

Abstract:

Feature point matching is of central importance in feature-based image registration algorithms such as Scale-Invariant Feature Transform (SIFT) algorithm. Since most of the existed feature matching algorithms are not so powerful and efficient in mismatch removing, in this paper, a mismatch removal algorithm was proposed which adopted the depth information in an image to improve the performance. In the proposed approach, the depth map of an acquired image was produced using the clues of defocusing blurring effect, and machine learning algorithm, followed by SIFT feature point extraction. Then, the correct feature correspondences and the transformation between two feature sets were iteratively estimated using the RANdom SAmple Consensus (RANSAC) algorithm and exploiting the rule of local depth continuity. The experimental results demonstrate that the proposed algorithm outperforms conventional ones in mismatch removing.

摘要：

特征点匹配是基于特征点的图像配准技术中的一个重要环节。针对现有基于尺度不变特征变换(SIFT）图像配准技术特征点匹配不理想,也无法较客观、快速地筛选正确匹配点对的问题,提出结合图像深度信息进行特征点误匹配筛选剔除的方法。该算法首先根据模糊聚焦线索和机器学习算法估计出待配准图像的深度信息图,再提取SIFT特征点,并在特征点匹配环节利用随机抽样一致性(RANSAC)算法迭代循环,结合深度局部连续性的原理来进一步提高匹配精度。实验结果表明,该算法具有很好的误匹配点对剔除功能。

CLC Number:

TP391.4

LIU Zheng LIU Yongben. Removal of mismatches in scale-invariant feature transform algorithm using image depth information[J]. Journal of Computer Applications, 2014, 34(12): 3554-3559.

刘政刘本永. 基于图像深度信息的尺度不变特征变换算法误匹配点对剔除[J]. 计算机应用, 2014, 34(12): 3554-3559.

References

［1］BROWN L G. A survey of image registration techniques［J］. ACM Computing Surveys, 1992, 24(4): 325-376.
［2］HARRIS C, STEPHENS M. A combined corner and edge detector［C］// Proceedings of the 4th Alvey Vision Conference. Manchester: Alvey Vision Club, 1988: 147-151.
［3］FONSECA L M G, COSTA M H M. Automatic registration of satellite images［C］// Proceedings of the Brazilian Symposium on Computer Graphic and Image Processing. Piscataway: IEEE, 1997: 219-226.
［4］TON J, JAIN A K. Registering landsat images by point matching［J］. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(5): 642-651.
［5］FISCHLER M, BOLLES R. Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography［J］. Communication of ACM, 1981, 24(6): 381-395.
［6］TORR P H S, ZISSERMAN A. MLESAC: a new robust estimator with application to estimating image geometry［J］. Computer Vision and Image Understanding, 2000, 78(1): 138-156.
［7］ZHUO S J, SIM T. On the recovery of depth from a single defocused image［J］. Computer Analysis of Images and Patterns, 2009, 44(9): 1852-1858.
［8］PENTLAND A P. A new sense for depth of field［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(4): 523-531.
［9］KONRAD J, WANG M, ISHWAR P. 2D-to-3D image conversion by learning depth from examples［C］// Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Washington, DC:IEEE Computer Society, 2012: 16-22.
［10］KONRAD J, BROWN G, WANG M, et al. Automatic 2D-to-3D image conversion using 3D examples from the Internet［C］// Proceedings of SPIE: Stereoscopic Displays and Applications. Bellingham: SPIE, 2012: 82880F1-82880F12.
［11］DALAL N, TRIGGS B. Histograms of oriented gradients for human detection［C］// Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2005: 886-893.
［12］LIU C, YUEN J, TORRALBA A. Sift flow: dense correspondence across scenes and its applications［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(5): 978-994.
［13］YUAN Z, YAN P M, LI S. Super resolution based on scale invariant feature transform［C］// Proceedings of the 2008 International Conference on Audio, Language and Image Processing. Piscataway: IEEE, 2008: 1550-1554.
［14］SONKA M, HLAVAC V,BOYLE R. Image processing, analysis, and machine vision［M］. AI H, WU B, translated. 2nd ed. Beijing: Posts and Telecom Press, 2003: 310-312. (SONKA M, HLAVAC V, BOYLE R. 图像处理、分析与机器视觉［M］.艾海舟,孟小峰,译.2版.北京:人民邮电出版社,2004.310-312.)
［15］SILBERMAN N, KOHLI P, HOIEM D, FERGUS R. Indoor segmentation and support inference from RGBD images［DB/OL］. ［2012-11-13］.http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html.

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Removal of mismatches in scale-invariant feature transform algorithm using image depth information

基于图像深度信息的尺度不变特征变换算法误匹配点对剔除

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