Earticle

초록

영어

Techniques of generating a single panoramic image by using multiple images are being widely studied in a number of areas, such as computer vision and computer graphics. Generating a panoramic image is a good way of overcoming the limitations of the images obtained from one single camera (e.g., those of picture angles, resolutions, information amounts, etc.) and may be applied in a variety of fields such as virtual reality and robot vision that require wide-angle images. A panoramic image has a great significance in that it can provide a greater sensation of immersion compared to a single image. Currently, there are a variety of techniques of producing panoramic images, but most of them commonly use a method of detecting feature points and matching points in each of the panoramic images they generate. In addition, they use the method of converting images after obtaining homography matrix using the RANSAC (Random Sample Consensus) algorithm that uses matching points. The SURF (Speeded Up Robust Features) algorithm used in this study utilizes the black-and-white and local space information of images when detecting their feature points and is widely used because it provides an outstanding performance in detecting the viewpoints and the changes of the image sizes and is faster than SIFT (Scale Invariant Features Transform) algorithm. However, the SURF algorithm also has its weak point of detecting wrong matching points, which may slow down the performance speed of the RANSAC algorithm and thus increases CPU usage occupation rates. The errors in detecting matching points serve as essential elements of lowering the accuracy and resolutions of panoramic images. In order to minimize these errors, this paper went through an intermediate filtering process of removing wrong matching points using the RGB values of 3×3 region around their coordinates and then presented analysis and evaluation results related to improvements in panoramic image construction & processing and CPU usage occupation rates and the decreasing rates and accuracy of the extracted matching points.

목차

Abstract
 1. Introduction
 2. Panoramic Image Processing Techniques
  2.1. Panoramic Images
  2.2. SURF (Speeded Up Robust Features) Algorithm
  2.3. RANSAC (Random Sample Consensus) Algorithm
 3. Extracting and Filtering the Matching Points
  3.1. Solutions to the Limitation of Having to Obey the Sequence of the Image Input
  3.2. Matching Points Filtering
 4. Experiment
  4.1. Experiment on Solving the Limitation of Having to Obey the Sequence of the Image Input
  4.2. Matching Points Filtering
 5. Discussion and Analysis of the Results
  5.1. Results of the Experiment on Solving the Limitation of Having to Obey the Sequence of the Image Input
  5.2. Results of Matching Points Extraction and Filtering
 6. Conclusions
 References

키워드

저자

• Daewon Kim [ Department of Applied Computer Engineering, Dankook University, Republic of 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