Earticle

초록

영어

Keypoint selection is the important step in object recognition, including general object classification, human tracing and human pose discrimination etc .This paper proposes a more accurate modified key point selection algorithm by modifying SIFT in the stage of extreme point selection. In machine vision or computer vision, including human pose recognition, to select key points, the traditional SIFT completes this according to the extremes derived from LoG (Laplacian of Gaussian) convolution with image, which provides scale invariance features for key points. The extreme points’ position is the foundation of feature descriptor for the gradient calculation in the next step. But in the process of images convoluting with the difference of Gaussian function to attain the extreme point, bias is produced because the extreme points’ positions aren’t accurate. We modify the extreme points’ selection to make key points more accurate with less bias to the theoretical points. Simulation with about 3500 images of different resolutions gives the AIPR (adjusted interest point ratio) and illustrates the universality of extreme points’ selection and verifies the values of this algorithm.

목차

Abstract
 1. Introduction
 2. Scale invariance in SIFT key point extraction
 3. Modified feature extreme point extraction
 4. Experiments and result
  4.1. Selecting dataset
  4.2. Interest point position bias
  4.3. Adjusting the interest point
 5. Conclusion and outlook
 Acknowledgements
 References

키워드

저자

• Ding Zuchun [ School of Electronic and Communication, South China University of Technology ]

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