Earticle

초록

영어

Variational Bayesian (VB) inference is the latest iterative method for prediction of data in machine learning. It provides the solution for intractable integration in Bayesian methodology. In this paper, a simple VB linear regression is applied for prediction of the damaged pixels in an image. Bayesian linear regression model is used for prediction of the pixels. For this neighbor pixels are used as training data to generate the parameters of the prediction function. Now using this prediction function, damaged pixels are predicted and incorporated into the image. Proposed method is linear while image is a non-linear object, generally. Hence, for linearity, a small image window size is used to avoid the nonlinearities in image.

목차

Abstract
 1. Introduction
 2. Review of Fast Marching Method
 3. Elements of Variational Bayes
 4. Bayesian Linear Regression Model
 5. Variational Approximation and Results
 6. Conclusion
 References

키워드

저자

• Rohit Sain [ Electronics and Communication Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India ]
• Vikas Mittal [ Electronics and Communication Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India ]
• Vrinda Gupta [ Electronics and Communication Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India ]

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