Browse > Article

A Compensation for Distortion of Stereo-scopic Camera Image Using Neuro-Fuzzy Inference System  

Seo, Han-Seog (광운대학교 로봇학부)
Yim, Wha-Young (광운대학교 로봇학부)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.5, no.3, 2010 , pp. 262-268 More about this Journal
Abstract
In this paper, this study restores the distorted image to its original image by compensating for the distortion of image from a fixed-focus camera lens. The various developments and applications of the imaging devices and the image sensors used in a wide range of industries and expanded use, but due to the needs of the small size and light weight of the camera, the distortion from acquiring images of the distorted curvature of the lens tends to affect many. In particular, the three-dimensional imaging camera, each different distortion of left and right lens cause the degradation of three-dimensional sensitivity and left-right image distortion ratio. we approached the way of generalizing the approximate equations to restore each part of left-right camera images to the coordinators of the original images. The adaptive Neuro-Fuzzy Inference System is configured for it. This system is divided from each membership function and is inferred by 1st order Sugeno Fuzzy model. The result is that the compensated images close to the left, right original images. Using low-cost and compact imaging lens by which also determine the exact three-dimensional image-sensing capabilities and will be able to expect from this study.
Keywords
Neuro-Fuzzy; Inference; Compensationg of distorted image; Fixed-Focus lens; Stereo-Scopic; Three-Dimensional;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Neural Fuzzy Systems -Chin-Teng Lin, C.S. George Lee
2 Neuro-Fuzzy and Soft Computing - J.S.R. Jang, C.T. Sun, E. Mizutani
3 J.-S. Roger Jang. ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics , May 1993   DOI   ScienceOn
4 S. Sha, F. Palmieri, and M. Datum. Optimal filtering algorithms for fast learning in feedforward neual networks. Neural Networks, 1992   DOI   ScienceOn
5 R.J. William and D. Zipser. A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1989   DOI