Two images comparison with invariance to illumination properties

ICIP 19 - IEEE International Conference on Image Processing, Taipei, TAIWAN, 22-/09/2019 - 25/09/2019We propose a new way of performing pixel by pixel comparison between two images, taking advantage of interesting invariance properties with respect to illumination conditions and camera settings. Moreover, we show that the proposed operator is relatively robust to strong noise on one of the compared images. The new operator can be used for background subtraction which inherits its invariance properties. The useful properties of the proposed operators are illustrated in the experiments

Improving the stability of algebraic curves for applications

Journal ArticleAn algebraic curve is defined as the zero set of a polynomial in two variables. Algebraic curves are practical for modeling shapes much more complicated than conics or superquadrics. The main drawback in representing shapes by algebraic curves has been the lack of repeatability in fitting algebraic curves to data. Usually, arguments against using algebraic curves involve references to mathematicians Wilkinson (see [1, ch. 7] and Runge (see [3, ch. 4]). The first goal of this article is to understand the stability issue of algebraic curve fitting. Then a fitting method based on ridge regression and restricting the representation to well behaved subsets of polynomials is proposed, and its properties are investigated. The fitting algorithm is of sufficient stability for very fast position-invariant shape recognition, position estimation, and shape tracking, based on invariants and new representations. Among appropriate applications are shape-based indexing into image databases

Calibration Radiométrique de Caméra

Projet SYNTIMIn Computer Vision methods such as shape-from-shading, 3D reconstruction by correlation and edge detection, all the pixels of the CCD matrix are assumed equivalent. A radiometric camera calibration step is necessary to verify this hypothesis, which is the subject of this paper. First, the modeling of the sensor, based on its physical properties, is described. A method to estimate the parameters of this model is given and the results of experimentations are described. These experiments valid the complete model only partially, and simplifications of this model are subsequently proposed which remain experimentally correct with focal length variations. Finally, improvements of the image analysis process after performing radiometric calibration are demonstrated.La correction des défauts de la radiométrie de la caméra est importante pour vérifier l'hypothèse de similarité des cellules CCD faite dans de nombreuses méthodes d'analyse d'images comme le shape-from-shading, la reconstruction par corrélation et l'extraction de contours. Nous décrivons la modélisation de ces défauts radiométriques à partir de la physique du senseur. Les paramètres de ce modèle peuvent être estimés par différentes expérimentations que nous avons réalisées sur notre objectif, ce qui a permis de partiellement valider le modèle et d'en proposer un autre simplifié, qui reste valide expérimentalement pour un objectif à focale variable. Enfin, les avantages de la correction radiométrique sont illustrés sur différents processus d'analyse d'image

Reconstruction globale et robuste de facettes 3D

Projet SYNTIMWe present a 3D reconstruction approach which is few studied in consideration of its numerous advantages. It is a global approach, where the algorithm attempt to reconstruct geometric high level features by using only global parameters of the image projections of the feature. Our elementary feature is 3D planar patch, and geometric moments are our global parameters. The proposed method of reconstruction has the following adavatages: - using of the geometric moments of image region do not need a pixel-to-pixel matching and yields robustness to small errors of segmentation on region edges, - valid matched regions are selected and thus, probably occluded planar patches are eliminated, - occluded paches are processed by a global reconstruction algorithm based on region photometry. Finally, a validation tool of reconstruction method is presented and used on our results.Nous présentons une approche de la reconstruction 3D peu explorée jusqu'à présent qui présente pourtant des avantages importants. Dite globale, elle s'attache à reconstruire des primitives géométriques de hauts niveaux en s'appuyant uniquement sur les caractéristiques géométriques globales des projections 'image' de la primitive. Notre primitive de base est la facette 3D, et les moments géométriques des régions images sont nos caractéristiques. Notre méthode présente les avantages suivants : - l'utilisation des moments géométriques des régions images ne nécessite aucune mise en correspondance au niveau du pixel et rend la méthode robuste aux petites erreurs de segmentation sur leurs contours, - les paires de régions peuvent être sélectionnées pour ne reconstruire que les primitives 3D qui semblent valides, ce qui permet de les séparer des faces probablement occultées, - les faces occultées sont traitées par une méthode globale fondée sur la photométrie des régions. Enfin, un outil de validation de méthodes de reconstruction est présenté et utilisé sur notre approche

Recalage géométrique avec plusieurs prototypes

Projet SYNTIMWe describe a general-purpose method for the accurate and robust interpretation of a data set of p-dimensional points by several deformable prototypes. This method is based on the fusion of two algorithms: a Generalization of the Iterative Closest Point (GICP) to different types of deformations for registration purposes, and a fuzzy clustering algorithm (FCM). Our method always converges monotonically to the nearest local minimum of a mean-square distance metric, and experiments show that the convergence is fast during the first few iterations. Therefore, we propose a scheme for choosing the initial solution to converge to an "interesting" local minimum. The method presented is very generic and can be applied: to shapes or objects in a p-dimensional space, to many shape patterns such as polyhedra, quadrics, polynomial functions, snakes, to many possible shape deformations such as rigid displacements, similitudes, affine and homographic transforms. Consequently, our method has important applications in registration with an ideal model prior to shape inspection, i.e. to interpret 2D or 3D sensed data obtained from calibrated or uncalibrated sensors. Experimental results illustrate some capabilities of our method.Nous décrivons un cadre général pour l'interprétation précise et robuste d'un ensemble de points par plusieurs prototypes déformables. Cette méthode est basée sur l'unification de deux algorithmes : une généralisation de l'algorithme "Iterative Closest Point" (GICP) à différents types de transformations pour des tâches de recalage, et un algorithme de classification floue (FCM) pour traiter plusieurs prototypes. Notre algorithme converge de façon monotone vers le plus proche minimum local d'un fonction de coût au moindre carré, et les expériences montrent que la convergence est rapide dans les premières étapes. En conséquence, nous avons proposé un schéma pour choisir la position initiale des prototypes pour qu'ils convergent vers une solution "intéressante". La méthode présentée est très générique et peut être appliquée : à des prototypes dans un espace de dimension p quelconque, à différentes formes de prototypes comme les polyèdres, les quadriques, les fonctions polynômiales, les snakes, à de nombreux types de déformations comme les déplacements rigides, les similitudes, les affinités et les homographies. Ainsi, notre méthode a un grand nombre d'applications en recalage avec un modèle idéal connu a priori, c'est-à-dire pour interpréter des données 2D et 3D obtenues par des capteurs calibrés ou non. Des résultats expérimentaux illustrent quelqu'unes des possibilités de notre approche

Calibration de camera fondée sur les ellipses

Projet SYNTIMWe present an ellipse-based camera calibration. Correct measures in the image are required to perform the camera model estimation. We suggest a technique, based upon surface photometry, allowing to compute robustly and accurately the moments of a shape in an image, and use them as inputs for the estimation algorithm. Connections between the specification of a 3D-ellipse, the moments of its image and calibration parameters are described. These equations are partially extended to models including distortions. Then, the parameters estimation is a non linear problem solved by an iterative algorithm. Tests with synthetic and real images are described and discussed for different camera models.Une méthode de calibration de caméra à base d'ellipses est présentée. Pour avoir une bonne estimation des paramètres du modèle, il est nécessaire d'avoir de bonnes mesures dans l'image. Les moments géométriques d'une région ont été choisis comme les données à fournir à l'algorithme d'estimation, car ils peuvent être calculés, dans une image, avec précision et robustesse en se fondant sur la photométrie des surfaces. Le lien exact, entre la description d'une ellipse de l'espace 3D et les moments d'ordre inférieur ou égal à deux de sa projection, est explicité en fonction des paramètres de la calibration. Ces relations sont en partie étendues au cas où les distorsions sont modélisées. Alors, l'estimation des paramètres est un problème non linéaire qui est résolu avec un algorithme itératif. Des résultats sur des images réelles et synthétiques sont présentés et discutés pour différents types de modèles de la caméra

Markov Random Field model for single image defogging

Fog reduces contrast and thus the visibility of vehicles and obstacles for drivers. Each year, this causes traffic accidents. Fog is caused by a high concentration of very fine water droplets in the air. When light hits these droplets, it is scattered and this results in a dense white background, called the atmospheric veil. As pointed in [1], Advanced Driver Assistance Systems (ADAS) based on the display of defogged images from a camera may help the driver by improving objects visibility in the image and thus may leads to a decrease of fatality and injury rates. In the last few years, the problem of single image defogging has attracted attention in the image processing community. Being an ill-posed problem, several methods have been proposed. However, a few among of these methods are dedicated to the processing of road images. One of the first exception is the method in [2], [1] where a planar constraint is introduced to improve the restoration of the road area, assuming an approximately flat road. The single image defogging problem being ill-posed, the choice of the Bayesian approach seems adequate to set this problem as an inference problem. A first Markov Random Field (MRF) approach of the problem has been proposed recently in [3]. However, this method is not dedicated to road images. In this paper, we propose a novel MRF model of the single image defogging problem which applies to all kinds of images but can also easily be refined to obtain better results on road images using the planar constraint. A comparative study and quantitative evaluation with several state-of-the-art algorithms is presented. This evaluation demonstrates that the proposed MRF model allows to derive a new algorithm which produces better quality results, in particular in case of a noisy input image

Robust 2D location of interest points by accumulation

Various interest point and corner definitions were proposed in the past with associated detection algorithms. We propose an intuitive and novel detection algorithm for finding the location of such features in an image. The detection is based on the edges in the original image. Interest points are detected as accumulation points where several edge tangent lines in a neighborhood are crossing. Edge connectivity is not used and thus detected interest points are robust to partial edges, outliers and edge extraction failures at junctions. One advantage of the approach is that detected interest points are not shifted in location when the original image is smoothed compared with other approaches. Experiments performed on Oxford and Cambridge reference databases allow us to show that the proposed detection algorithm performs better than 9 existing interest point detectors in terms of repeatability from multiple camera views

Camcal v1.0 Manual A Complete Software Solution for Camera Calibration

Projet SYNTIMThis technical report is the user manual of CamCal v1.0. CamCal is a software for geometric camera calibration from a 3D calibration set-up image