<p>The quality of cameras is currently increasing very fast meanwhile the price of them is decreasing. The possibilities of using a camera as a measurement and navigation instrument are thus getting bigger all the time. This thesis studies the transformation relations between a camera image and the scene in space that is projected to it. A theoretical derivation of the transform will be presented, and methods and algorithms for applications based on the transform will be developed.</p><p>The above mentioned transform is called the camera matrix, which contains information about the camera attitude, the camera position, and the internal structure of the camera. Useful information for several different applications can be extracted from the camera image with the help of the camera matrix.</p><p>In one of the applications, treated in this Master´s thesis, the camera attitude is estimated when the camera is calibrated and its position is known. Another application is that of absolute target positioning, where a point in a digital map is searched from its position in a camera image. Better accuracy in the measurements can though be obtained with relative target positioning i.e., estimation of distance and angle between two points in the digital map by picking them out in the image. This is because that the errors of the</p><p>absolute target positioning for each of the two points are dependent and thus partly will cancel each other out when their relative position and angle is measured.</p>

The quality of cameras is currently increasing very fast meanwhile the price of them is decreasing. The possibilities of using a camera as a measurement and navigation instrument are thus getting bigger all the time. This thesis studies the transformation relations between a camera image and the scene in space that is projected to it. A theoretical derivation of the transform will be presented, and methods and algorithms for applications based on the transform will be developed. The above mentioned transform is called the camera matrix, which contains information about the camera attitude, the camera position, and the internal structure of the camera. Useful information for several different applications can be extracted from the camera image with the help of the camera matrix. In one of the applications, treated in this Master´s thesis, the camera attitude is estimated when the camera is calibrated and its position is known. Another application is that of absolute target positioning, where a point in a digital map is searched from its position in a camera image. Better accuracy in the measurements can though be obtained with relative target positioning i.e., estimation of distance and angle between two points in the digital map by picking them out in the image. This is because that the errors of the absolute target positioning for each of the two points are dependent and thus partly will cancel each other out when their relative position and angle is measured.

Cette thèse se concentre sur le sujet de la calibration de la camera interne et, en particulier, sur les aspects de haute précision. On suit et examine deux fils principaux: la correction d'une aberration chromatique de lentille et l'estimation des paramètres intrinsèques de la caméra. Pour la problème de l'aberration chromatique, on suit un chemin de post-traitement numérique de l'image, afin de se débarrasser des artefacts de couleur provoqués par le phénomène de dispersion du système d'objectif de la caméra, ce qui produit une désalignement perceptible des canaux couleur. Dans ce contexte, l'idée principale est de trouver un modèle de correction plus général pour réaligner les canaux de couleur que ce qui est couramment utilisé - différentes variantes du polynôme radial. Celui-ci ne peut pas être suffisamment général pour assurer la correction précise pour tous les types de caméras. En combinaison avec une détection précise des points clés, la correction la plus précise de l'aberration chromatique est obtenue en utilisant un modèle polynomial qui est capable de capter la nature physique du décalage des canaux couleur. Notre détection de points clés donne une précision allant jusqu'à 0,05 pixels, et nos expériences montrent sa grande résistance au bruit et au flou. Notre méthode de correction de l’aberration, par opposition aux logiciels existants, montre une géométrique résiduelle inférieure à 0,1 pixels, ce qui est la limite de la perception de la vision humaine. En ce qui concerne l'estimation des paramètres intrinsèques de la caméra, la question est de savoir comment éviter la compensation d'erreur résiduelle qui est inhérent aux méthodes globales d'étalonnage, dont le principe fondamental consiste à estimer tous les paramètres de la caméra ensemble - l'ajustement de faisceaux. Détacher les estimations de la distorsion de la caméra et des paramètres intrinsèques devient possible lorsque la distorsion est compensée séparément. Cela peut se faire au moyen de la harpe d'étalonnage, récemment développée, qui calcule le champ de distorsion en utilisant la mesure de la rectitude de cordes tendues dans différentes orientations. Une autre difficulté, étant donnée une image déjà corrigée de la distorsion, est de savoir comment éliminer un biais perspectif. Ce biais dû à la perspective est présent quand on utilise les centres de cibles circulaires comme points clés, et il s'amplifie avec l'augmentation de l'angle de vue. Afin d'éviter la modélisation de chaque cercle par une fonction conique, nous intégrons plutôt fonction de transformation affine conique dans la procédure de minimisation pour l'estimation de l'homographie. Nos expériences montrent que l'élimination séparée de la distorsion et la correction du biais perspectif sont efficaces et plus stables pour l'estimation des paramètres intrinsèques de la caméra que la méthode d'étalonnage globale / This dissertation focuses on internal camera calibration and, especially, on its high-precision aspects. Two main threads are followed and examined: lens chromatic aberration correction and estimation of camera intrinsic parameters. For the chromatic aberration problem, we follow a path of digital post-processing of the image in order to get rid from the color artefacts caused by dispersion phenomena of the camera lens system, leading to a noticeable color channels misalignment. In this context, the main idea is to search for a more general correction model to realign color channels than what is commonly used - different variations of radial polynomial. The latter may not be general enough to ensure stable correction for all types of cameras. Combined with an accurate detection of pattern keypoints, the most precise chromatic aberration correction is achieved by using a polynomial model, which is able to capture physical nature of color channels misalignment. Our keypoint detection yields an accuracy up to 0.05 pixels, and our experiments show its high resistance to noise and blur. Our aberration correction method, as opposed to existing software, demonstrates a final geometrical residual of less than 0.1 pixels, which is at the limit of perception by human vision. When referring to camera intrinsics calculation, the question is how to avoid residual error compensation which is inherent for global calibration methods, the main principle of which is to estimate all camera parameters simultaneously - the bundle adjustment. Detachment of the lens distortion from camera intrinsics becomes possible when the former is compensated separately, in advance. This can be done by means of the recently developed calibration harp, which captures distortion field by using the straightness measure of tightened strings in different orientations. Another difficulty, given a distortion-compensated calibration image, is how to eliminate a perspective bias. The perspective bias occurs when using centers of circular targets as keypoints, and it gets more amplified with increase of view angle. In order to avoid modelling each circle by a conic function, we rather incorporate conic affine transformation function into the minimization procedure for homography estimation. Our experiments show that separate elimination of distortion and perspective bias is effective and more stable for camera's intrinsics estimation than global calibration method