Earticle

초록

영어

Pose estimation which is regard as the cross-technology of computer vision and pattern recognition, and an important prerequisite for human behavior understanding. Human pose estimation which use the probability theory, machine learning, pattern recognition, graph theory and other theories to get the position, the deflection angle of the various parts of the body. Then make the detection and estimation parameters for the human body pose. When the image has interference in the background, color and scale changed, human pose complex, self-occlusion and interpersonal interaction occlusion may make the precision and accuracy of pose estimation face great challenge. Thus, according to the above problems, this paper use the advanced model of the human body as contour model to descript the complex pose, in order to make the model more accurate and suitable for various human pose, we pre-clustering the human body pose of the training samples before we trained the model and in order to ensure the accuracy of the pose we use robust segmentation of multi-view with a novel shape prior. The experiment shows that the algorithm performs better than the classic algorithm on the public datasets.

목차

Abstract
 1. Introduction
 2. Body Model
  2.1. Pose Clustering
 3. Robust Image Segmentation
  3.1. Image Segmentation with MRF
  3.2. The Energy Function
  3.3. Shape Prior t
  3.4. Minimize E (f)
 4. The Pose Estimation
 5. Results and Analysis
 6. Conclusion
 References

키워드

저자

• Yunheng Liu [ School of information technology, Nanjing Forest Police College, Nanjing, Jiangsu, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605