Earticle

초록

영어

This paper presents an accurate large displacement optical flow estimation approach by adaptively integrate the local feature match. Despite coarse-to-fine warping approach can handle large displacement optical flow; however, there is inherent limit for small object with large motion. And recently integration of feature match to the variational framework has relaxed the limit, but raised another problem of ambiguous feature matching due to poor feature descriptor. Address the aforementioned problem, in this paper we propose an adaptive integration approach of local feature match. The essence is that we only keep the robust feature and remove those unstable features (e.g, textureless region) to improve the flow accuracy. The adaptive approach substantially decreases the computational cost by remove uncertain features and leads to more robust performance by excluding unreliable matches. We qualitatively and quantitatively compared to the conventional flow methods on Middlebury and Sintel benchmark and show that we achieve more accurate and promising results.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Variational Model
  3.1. Data Tern  Ed(W)
  3.2. Smoothness Tern Es(W)
  3.3. Matching Tern Em(W)
 4. Adaptive Feature Matching
  4.1. Confidence Function Pm(x) Computation
 5. Minimization of the Energy Functional
 6. Experimental Results
 7. Conclusion
 References

키워드

저자

• Xiaoping Zhang [ Information Engineering School, Binzhou Vocational College, Binzhou, China ]
• Guoxin Li [ Information Engineering School, Binzhou Vocational College, Binzhou, 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