Earticle

초록

영어

In this paper, a novel affine invariant descriptor for object matching is proposed. The advantage of Maximally Stable Extremal Regions (MSER) method is applied to get the most stable regions in the image. Inside each region, we pick the seeds as keypoints since MSER regions are invariant to affine transformation. Besides that, Voronoi diagram is employed to split the image into small Voronoi cells or local regions based on the key points picked in the previous step. Finally, local features inside each local region including color, texture and geometric properties are extracted to generate the descriptor. Our experiments demonstrate that the proposed affine invariant local descriptor based on Voronoi tessellation is more stable and robust to object matching than SIFT descriptor while using the same keypoints.

목차

Abstract
 1. Introduction
 2. Keypoint Detector
 3. Local Feature Extraction
  3.1. Geometric Feature Extraction
  3.2. Color Feature Extraction
  3.3. Texture and Region Feature Extraction
  3.4. Feature Descriptors
 4. Experimental Results and Conclusions
  4.1. Dataset and Ground Truth
  4.2. Experimental Results
 Acknowledgements
 References

키워드

저자

• Yen Do [ School of Electronics & Computer Engineering, Chonnam National University ]
• Soo Hyung Kim [ School of Electronics & Computer Engineering, Chonnam National University ] the corresponding author
• Sang Cheol Park [ Samsung Medison ]
• In Seop Na [ School of Electronics & Computer Engineering, Chonnam National University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605