Rectification from Radially-Distorted Scales

This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle images that contain nearly any type of coplanar repeated content. We demonstrate a principled approach to generating stable minimal solvers by the Grobner basis method, which is accomplished by sampling feasible monomial bases to maximize numerical stability. Synthetic and real-image experiments confirm that the solvers give accurate rectifications from noisy measurements when used in a RANSAC-based estimator. The proposed solvers demonstrate superior robustness to noise compared to the state-of-the-art. The solvers work on scenes without straight lines and, in general, relax the strong assumptions on scene content made by the state-of-the-art. Accurate rectifications on imagery that was taken with narrow focal length to near fish-eye lenses demonstrate the wide applicability of the proposed method. The method is fully automated, and the code is publicly available at https://github.com/prittjam/repeats.Comment: pre-prin

Radially-Distorted Conjugate Translations

This paper introduces the first minimal solvers that jointly solve for affine-rectification and radial lens distortion from coplanar repeated patterns. Even with imagery from moderately distorted lenses, plane rectification using the pinhole camera model is inaccurate or invalid. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle imagery, which is now common from consumer cameras. The solvers are derived from constraints induced by the conjugate translations of an imaged scene plane, which are integrated with the division model for radial lens distortion. The hidden-variable trick with ideal saturation is used to reformulate the constraints so that the solvers generated by the Grobner-basis method are stable, small and fast. Rectification and lens distortion are recovered from either one conjugately translated affine-covariant feature or two independently translated similarity-covariant features. The proposed solvers are used in a \RANSAC-based estimator, which gives accurate rectifications after few iterations. The proposed solvers are evaluated against the state-of-the-art and demonstrate significantly better rectifications on noisy measurements. Qualitative results on diverse imagery demonstrate high-accuracy undistortions and rectifications. The source code is publicly available at https://github.com/prittjam/repeats

Алгоритми компенсації оптичних спотворень на цифрових зображеннях

До бакалаврської дипломної роботи Перцова Вадим Миколайовича. На тему: «Алгоритми компенсації оптичних спотворень на цифрових зображеннях» Дана дипломна робота присвячена методам компенсації оптичних спотворень на цифрових зображеннях. В роботі зроблено аналіз алгоритмів компенсації оптичних спотворень та визначення найбільш оптимальних методів компенсації для цифрових зображеннях. Аналіз проводиться в пакеті прикладних програм MATLAB.This thesis is devoted to algorithms for compensating optical distortion of digital images. The paper analyzes the methods of optical distortion compensation and determines the most optimal compensation methods for digital images. The analysis is performed in the MATLAB application package

IMAGE DISTORTION CORRECTION FOR BIPRISM-BASED SINGLE-LENS STEREOVISION SYSTEM

Ph.DDOCTOR OF PHILOSOPH