Correcting distortions in panoramic images using constructive neural networks.
This paper presents a novel approach to the correction of panoramic (wide-angle) image distortions. Unlike traditional methods that separate the distortion of the panoramic image into radial and tangential components and then concentrate on the correction of one type of distortion at a time, the method presented in this paper uses an integrated approach that simultaneously corrects all non-linear distortions of the panoramic image. The system uses data obtained from carefully constructed calibration patterns to automatically build and train a constructive neural network of suitable complexity to approximate the characteristic distortion of the panoramic image. The trained neural network is then used to correct the distortions represented by the sample data. It is demonstrated that by applying the distortion correction method presented in this paper to panoramic images representing real world scenes, perspective-corrected views of the real world scene that are usable in a wide variety of applications can be generated.
Ekpar
Frank
F
Department of Intellectual Information Systems, Faculty of Engineering, Toyama University, 3190 Gofuku, Toyama, 930-8555, Japan. frank@imatronics.com
Yoneda
Masaaki
M
Hase
Hiroyuki
H
D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional; D016571:Neural Networks, Computer; D055095:Optics and Photonics