Earticle

초록

영어

There are many methods to localize its position based on visual sensing schemes in indoor environment. This paper presents the problem of finding the correspondences of images feature’s descriptors when the images have large rotation. SIFT and SURF have always been considered as very effective algorithms to extract interest points and their orientation and descriptors. For descriptors, one of both uses a lot of time to calculate descriptors and the other has not good performance in large rotation of the image. In this paper, we propose an improved algorithm to calculate interest points’ descriptors for relative self-localization estimation. The proposed algorithm will satisfy descriptor invariant when the image rotates. Meanwhile, the proposed method reduces the calculated time as much as possible. Interest point’s descriptors are formed by resampling local regions.

목차

Abstract
 1. Introduction
 2. Related works
 3. Description of Interest Points
  3.1. Edge pixels value
  3.2. Neighborhood influence
  3.3. Descriptors
 4. Simulation Results
 5. Concluding Remarks
 References

키워드

저자

• Xing Xiong [ School of Electronic Engineering, Daegu University, Korea ]
• Byung-Jae Choi [ School of Electronic Engineering, Daegu University, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605