Wrapped Phase Based SVM Method for 3D Object Recognition
Kernel methods are effective machine learning techniques for many image based pattern recognition problems. Incorporating 3D information is useful in such applications. The optical profilometries and interforometric techniques provide 3D information in an implicit form. Typically phase unwrapping process, which is often hindered by the presence of noises, spots of low intensity modulation, and instability of the solutions, is applied to retrieve the proper depth information. In certain applications such as pattern recognition problems, the goal is to classify the 3D objects in the image, rather than to simply display or reconstruct them. In this paper we present a technique for constructing kernels on the measured data directly without explicit phase unwrapping. Such a kernel will naturally incorporate the 3D depth information and can be used to improve the systems involving 3D object analysis and classification. It avoids possible phase unwrapping errors introduced during object reconstruction.
SVM Phase Uunwrapping 3D Object Recongnition Kernal Construction
Hong Zhang Hongjun Su
Department of Computer Science Armstrong Atlantic State University 11935 Abercorn Street, Savannah, GA 31419 USA
国际会议
北京
英文
2154-2157
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）