Ellipse-based camera calibration

An important task in most 3D vision systems is camera modeling . We present a calibration method designed for a good accuracy in the parameters estimation . Accurate image measurments are required to perform the camera model estimation. We suggest a technique, based upon surface photometry analysis, to compute robustly and accurately the moments of a shape in an image, and use them as inputs for the estimation algorithm. As a consequences, an ellipse-based camera calibration is introduced, which uses the relationships between the specification of a 3D-ellipse, the moments of its image and the calibration parameters . These equations are partially extended to models including distortions .En vision par ordinateur, il est souvent utile de connaître un modèle de la caméra pour pouvoir obtenir des informations 3D à partir des images. Nous présentons une méthode de calibration (ou calibrage) de caméra conçue pour fournir une estimation précise des paramètres du modèle de prise de vue. Pour avoir une bonne estimation, il est nécessaire avant tout d'avoir de bonnes mesures dans l'image. Nous avons choisi d'utiliser les moments géométriques d'une région comme les données 'image' à fournir à l'algorithme, car ils peuvent être calculés avec précision et robustesse en se fondant sur la photométrie des surfaces. Cela nous a conduits à développer une calibration à base d'ellipses. Pour cela, 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 a dû être explicité en fonction des paramètres de la calibration. Ces relations sont en partie étendues au cas où les distorsions sont modélisées

Properties of a single photon generated by a solid-state emitter: effects of pure dephasing

We investigate the properties of a single photon generated by a solid-state emitter subject to strong pure dephasing. We employ a model in which all the elements of the system, including the propagating fields, are treated quantum mechanically. We analytically derive the density matrix of the emitted photon, which contains full information about the photon, such as its pulse profile, power spectrum, and purity. We visualize these analytical results using realistic parameters and reveal the conditions for maximizing the purity of generated photons.Comment: 25pages(one column), 10 figure

Non-resonant dot-cavity coupling and its applications in resonant quantum dot spectroscopy

We present experimental investigations on the non-resonant dot-cavity coupling of a single quantum dot inside a micro-pillar where the dot has been resonantly excited in the s-shell, thereby avoiding the generation of additional charges in the QD and its surrounding. As a direct proof of the pure single dot-cavity system, strong photon anti-bunching is consistently observed in the autocorrelation functions of the QD and the mode emission, as well as in the cross-correlation function between the dot and mode signals. Strong Stokes and anti-Stokes-like emission is observed for energetic QD-mode detunings of up to ~100 times the QD linewidth. Furthermore, we demonstrate that non-resonant dot-cavity coupling can be utilized to directly monitor and study relevant QD s-shell properties like fine-structure splittings, emission saturation and power broadening, as well as photon statistics with negligible background contributions. Our results open a new perspective on the understanding and implementation of dot-cavity systems for single-photon sources, single and multiple quantum dot lasers, semiconductor cavity quantum electrodynamics, and their implementation, e.g. in quantum information technology.Comment: 17 pages, 4 figure

I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images

Image dehazing has become an important computational imaging topic in the recent years. However, due to the lack of ground truth images, the comparison of dehazing methods is not straightforward, nor objective. To overcome this issue we introduce a new dataset -named I-HAZE- that contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images. Different from most of the existing dehazing databases, hazy images have been generated using real haze produced by a professional haze machine. For easy color calibration and improved assessment of dehazing algorithms, each scene include a MacBeth color checker. Moreover, since the images are captured in a controlled environment, both haze-free and hazy images are captured under the same illumination conditions. This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM

Mt. Kelud haze removal using color attenuation prior

Kelud crater observation using closed-circuit television (CCTV) has not been used as the main guide in the world of volcanology. This is caused by observations manually by volcanologist who is not certain and depends on their ability and experience. In practice, there is still obstacles haze in the image taken from CCTV record. This paper present color attenuation prior method to eliminate haze on the digital image. The results obtained showed that the selected method is capable of eliminating sparse haze and moderate haze but not dense haze

Daytime fog detection and density estimation with entropy minimization

Fog disturbs the proper image processing in many outdoor observation tools. For instance, fog reduces the visibility of obstacles in vehicle driving applications. Usually, the estimation of the amount of fog in the scene image allows to greatly improve the image processing, and thus to better perform the observation task. One possibility is to restore the visibility of the contrasts in the image from the foggy scene image before applying the usual image processing. Several algorithms were proposed in the recent years for defogging. Before to apply the defogging, it is necessary to detect the presence of fog, not to emphasis the contrasts due to noise. Surprisingly, few a reduced number of image processing algorithms were proposed for fog detection and characterization. Most are dedicated to static cameras and can not be used when the camera is moving. Daytime fog is characterized by its extinction coefficient, which is equivalent to the visibility distance. A visibility-meter can be used for fog detection and characterization, but this kind of sensor performs an estimation in a relatively small volume of air, and is thus sensitive to heterogeneous fog, and air turbulence with moving cameras. In this paper, we propose an original algorithm, based on entropy minimization, to detect fog and estimate its extinction coefficient by the processing of stereo pairs. This algorithm is fast, provides accurate results using low cost stereo camera sensor and, the more important, can work when the cameras are moving. The proposed algorithm is evaluated on synthetic and camera images with ground truth. Results show that the proposed method is accurate, and, combined with a fast stereo reconstruction algorithm, should provide a solution, close to real time, for fog detection and visibility estimation for moving sensors

Road Singularities Detection and Classification

Abstract. We propose a detection and classification system for various road situations, which is robust to light changes and different road markings. The road curves in an image are described with a Hough Transform, where histograms accumulate the contrast lines for each pixel. The resulting 2D histograms are used to train a Kohonen Neural Network. The final output classification can be used to improve road security, indicating dangerous situations to the driver or feeding a driving control system.