基于相对形状上下文和谱匹配方法的点模式匹配算法

赵键; 孙即祥; 李智勇; 陈明生

doi:10.3724/SP.J.1146.2010.00655

基于相对形状上下文和谱匹配方法的点模式匹配算法

doi: 10.3724/SP.J.1146.2010.00655

基金项目:

国家自然科学基金(40901216)资助课题

计量
- 文章访问数: 4485
- HTML全文浏览量: 104
- PDF下载量: 1319
- 被引次数: 0
出版历程
- 收稿日期: 2010-06-21
- 修回日期: 2010-08-20
- 刊出日期: 2010-10-19

Point Pattern Matching Algorithm Based on Relative Shape Context and Spectral Matching Method

摘要

摘要: 该文提出了一种将不变特征与谱匹配方法相结合的点模式匹配算法。该算法首先提出一种新的基于点集的不变特征相对形状上下文，然后利用点集间相对形状上下文的统计检验匹配测度来定义新的相容性度量，并以此为基础构造分配图及其亲近矩阵。最后利用分配图亲近矩阵的主特征向量以及匹配约束条件来实现点模式匹配问题的求解。模拟仿真与真实数据实验验证了该文算法的有效性和鲁棒性。
- 点模式匹配 /
- 相对形状上下文 /
- 分配图 /
- 谱匹配方法
Abstract: This paper presents a novel and robust point pattern matching algorithm in which the invariant feature and the method of spectral matching are combined. A new point-set based invariant feature, Relative Shape Context (RSC), is proposed firstly. Using the test statistic of relative shape context descriptors matching scores as the foundation of new compatibility measurement, the assignment graph and the affinity matrix of assignment graph are constructed based on the gained compatibility measurement. Finally, the correct matching results are recovered by using the principal eigenvector of affinity matrix of assignment graph and imposing the mapping constraints required by the overall correspondence mapping. Experiments on both synthetic point-sets and on real world data show that the proposed algorithm is effective and robust.
- Point pattern matching /
- Relative shape context /
- Assignment graph /
- Spectral matching method

HTML全文

参考文献(1)

Jackson B P and Goshtasby A A. Registering aerial video images using the projective constraint[J].IEEE Transactions on Image Processing.2010, 19(3):795-804[2]Xiong Z and Zhang Y. A novel interest-point-matching algorithm for high-resolution satellite images[J].IEEE Transactions on Geoscience and Remote Sensing.2009, 47(12):4189-4200[3]Jain A K, Jung-Eun L, and Rong J, et al.. Content-based image retrieval: an application to tattoo images[C]. The 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, Nov. 7-10, 2009: 2745-2748.Jiang T T, Jurie F, and Schmid C. Learning shape prior models for object matching[C]. IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, June 20-25, 2009: 848-855.[4]Li H S, Kim E, and Huang X L, et al.. Object matching with a locally affine-invariant constraint[C]. The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, California, USA, June 13-18, 2010.[5]McKeon R T and Flynn P J. Three-dimensional facial imaging using a Static Light Screen (SLS) and a dynamic subject[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 59(4): 774-783.[6]McAuley J J, Caetano T S, and Barbosa M S. Graph rigidity, cyclic belief propagation, and point pattern matching[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2008, 30(11):2047-2054[7]Besl P J and Mckay N D. A method for registration of 3-D shapes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.1992, 14(2):239-256[8]Chui H and Rangarajan A. A new point matching algorithm for non-rigid registration[J].Computer Vision and Image Understanding.2003, 89(2):114-141[9]Belongie S, Malik J, and Puzicha J. Shape matching and object recognition using shape contexts [J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2002, 24(4):509-522[10]Gope C and Kehtarnavaz N. Affine invariant comparison of point-sets using convex hulls and hausdorff distances[J].Pattern Recognition.2007, 40(1):309-320[11]Caetano T S, McAuley J J, and Cheng L, et al.. Learning graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 36(6): 1048-1058.[12]Scott G L and Longuet-higgins H. An algorithmfor associating the features of two patterns [C][J].Proceedings: Biological Sciences, The Royal Society, London.1991, 244:21-26[13]Shapiro L S and Brady J M. Feature-based correspondence: an eigenvector approach[J].Image and Vision Computing.1992, 10(5):283-288[14]Carcassoni M and Hancock E R. Spectral correspondence for point pattern matching[J].Pattern Recognition.2003, 36(1):193-204[15]Carcassoni M and Hancock E R. Correspondence matching with modal clusters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2003, 25(12):1609-1615[16]Leordeanu M and Hebert M. A spectral technique for correspondence problems using pairwise constraints[C]. The Tenth IEEE International Conference on Computer Vision, Beijing, China, October 17-20, 2005, Vol. 2: 1482-1489.[17]Zhang X D. Matrix Analysis and Applications[M]. Beijing: Tsinghua University Press, 2004: 528-530.

施引文献

资源附件(0)

访问统计