Gridbased Statistical Model for 3D Object Recognition

Abstract

Abstract:

Abstract: A 3D object recognition algorithm based on statistical model of grid structure is presented in this paper. First, a grid structure is introduced into the multiview points image. Then according to the positions of the grids, the relationship between the multiview points of the 3D object and the local invariant features of the object, the statistical model is established. Second, the degrees of freedom of the 3D object, which is from the image databaseCOIL, are expanded. At last, based on the previous work, the recognition performance of this algorithm is tested. The empirical results illustrate that this 3D object recognition algorithm, with relatively strong robustness, can not only recognize the classification of 3D objects effectively, but also make reliable judgments on the attitudes of the objects.

Key words:

, Key words: , 3D object recognition； multiple views； grid structure； object pose recognition

CLC Number:

TP391.4

FANG Xue, YU Ruixing. Gridbased Statistical Model for 3D Object Recognition[J]. Computer and Modernization, 2014, 0(4): 24-28.

References

［1］

彭辉，黄士科，陶琳,等. 基于Zernike矩的三维目标多视点特性视图建模［J］. 红外与激光工程, 2005,34(3):292296.

［2］田谨,蒋强荣,许蔓苓. 基于多视点不变量的三维物体识别［J］. 北京工业大学学报, 2000,26(1):98101.

［3］ Yu X， Ma H， You S， et al． A solution to efficient viewpoint space partition in 3D object recognition［C］//International Conference on Image and Graphics． Xi’an， Shaanxi， China: IEEE Computer Society， 2009:565570.

［4］席学强，王润生. 遥感图像三维目标多视点建模识别方法［J］. 计算机辅助设计与图形学学报, 2002,14(10):945949.

［5］王松伟,李言俊,张科. 基于局部小波矩的易混淆三维目标识别［J］. 红外与激光工程, 2008,37(6):11061110.

［6］王睿,李言俊,张科. 基于不变矩和SVM分类的三维目标识别方法［J］. 计算机仿真, 2011,28(1):242245.

［7］马超杰,李晓霞,杨华,等. 应用支持向量机分类的多角度目标识别技术［J］. 激光与红外, 2009,39(1):8891.

［8］ Grzegorzek M, Reinhold M, Niemann H. Feature extraction with wavelet transformation for statistical object recognition［C］//The 4th International Conference on Computer Recognition Systems. 2005:161168.

［9］ Mallat Stephne. 信号处理的小波导引［M］. 北京：机械工业出版社， 2002:54237.

［10］陈武凡. 小波分析及其在图像处理中的应用［M］. 北京:科学出版社, 2002:166168.

［11］王冕，王晶晶，王波，等. 小波变换在光学相关目标识别中的应用［J］. 兵工学报， 2006，27（5）：836840.

［12］Murenzi R, Johnson D. Detection of targets in low resolution FLIR imagery using twodimension directional wavelets［C］//SPIE’98. 1998：5105l5.

［13］万缨,韩毅,卢汉清. 运动目标检测算法的探讨［J］. 计算机仿真, 2006,23(10):221226.

［14］Ueda N， Nakano R， Ghahramani Z， et al． Split and merge EM algorithm for improving Gaussian mixture density estimates［J］． Journal of VLSI Signal Processing Systems for Signal， Image， and Video Technology， 2000，26(12):133140．

［15］Yang M H, Roth D, Ahuja N. Learning to recognize 3d objects with snow［C］// Computer VisionECCV. 2000:439454.

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