AN EXTENSION OF LOCALLY LINEAR EMBEDDING FOR POSE ESTIMATION OF 3D OBJECT

摘要：

Diverse pose estimation of 3D object in the whole view-space is a problem perplexed many researchers.In this paper we propose an algorithm extended from LLE which can estimate the arbitrary pose of 3D object in the whole view space.First, we compute the eigen-images of training set by introducing the idea of PCA using the low-dimensional embedding coordinate deduced from LLE.For a new sample we can compute its projection to the eigen-images, and the nearest training images from the new sample are the estimation poses.Next, we set different weight for different projection direction depends on its eigen-value when computing the distance between the new sample and the training images.Experimental results obtained demonstrated that the performance of the proposed method could estimate the diverse pose of 3D object efficiently and precisely, also our algorithm can be extended to real-time pose estimate, is of a potential future.

关键词： Locally linear embedding Dimensionality reduction Pose estimation of 3D object Eigen-image

作者: XU ZHANG HUI-MIN MA YU-SHU LIU CHUN-XIAO GAO

作者单位: School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081,China Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 1672-1677

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）