Détermination de l'orientation d'un plan texturé à partir du calcul des échelles locales

Dans cet article, nous proposons une méthode de « Shape from Texture » basée sur une nouvelle technique d'extraction des fréquences locales. La méthode que nous utilisons ici est une amélioration de celle introduite par Lu et co. [3]. Elle consiste à faire une interpolation des fréquences locales, contenues dans une image d'un plan incliné texturé, pour retrouver l'orientation de ce plan en utilisant le modèle de projection en perspective. La méthode d'extraction des fréquences locales dans l'image est obtenue par une décomposition en ondelettes. La détermination de la fréquence locale de chaque point de l'image est réalisée à l'aide d'une interpolation de la réponse des ondelettes calculées à différentes échelles

Applications de l'estimation de la variation des échelles locales d'un plan texturé

Les méthodes de "Shape From Texture (SFT)" permettent de retrouver l'orientation d'un plan texturé incliné à partir d'une image unique de ce plan. L'objectif de cet article est de montrer l'apport d'une de ces méthodes pour quatre applications : segmentation des texels, construction d'un maillage, extraction de défauts, calcul de l'orientation à partir de la ligne de fuite. La méthode de "SFT" développée est basée sur une analyse de la variation des échelles locales. Elle est articulée en deux parties : extraction des échelles locales puis interpolation de la carte des échelles obtenues avec la surface théorique afin de calculer directement l'orientation du plan ou d'utiliser ces résultats pour les applications visées

Extraction de formes dans des images complexes basée sur des propriétés du système visuel. Intégration dans un système de reconnaissance.

La méthode d'extraction de formes dans des images complexes développée, s'appuie sur des propriétés de fonctionnement du système visuel humain. Elle comporte plusieurs modules séquentiels: 1) analyse spatiale du contraste de luminance calculée en appliquant une fonction non linéaire de type logarithmique sur l'image, puis en réalisant une décomposition multi-échelle de l'image résultante, 2) seuillage des cartes de contrastes suivant la Fonction de Sensibilité aux Contrastes, 3) segmentation en régions 4) description géométriques et structurelles des régions obtenues 5) exploration des grandes échelles vers les petites échelles pour représenter les régions sous forme d'arbre et d'obtenir les détails fins permettant d'identifier ces formes. L'intégration de cette extraction comme un prétraitement dans un système de reconnaissance de formes est envisagée

Which Ocular Dominance Should Be Considered for Monocular Augmented Reality Devices?

A monocular augmented reality device allows the user to see information that is superimposed on the environment. As it does not stimulate both eyes in the same way, it creates a phenomenon known as binocular rivalry. The question therefore arises as to whether monocular information should be displayed to a particular eye and if an ocular dominance test can determine it. This paper contributes to give a better understanding of ocular dominance by comparing nine tests. Our results suggest that ocular dominance can be divided into sighting and sensorial dominance. However, different sensorial dominance tests give different results, suggesting that it is composed of distinct components that are assessed by different tests. There is a need for a comprehensive test that can consider all of these components, in order to identify on which eye monocular information should be directed to when using monocular augmented reality devices

(Disparity-Driven) Accommodation Response Contributes to Perceived Depth

When looking at objects at various distances in the physical space, the accommodation and vergence systems adjust their parameters to provide a single and clear vision of the world. Subtended muscle activity provides oculomotor cues that can contribute to the perception of depth and distance. While several studies have outlined the role of vergence in distance perception, little is known about the contribution of its concommitant accommodation component. It is possible to unravel the role of each of these physiological systems by placing observers in a situation where there is a conflict between accommodation and vergence distances. We thus sought to determine the contribution of each response system to perceived depth by simultaneously measuring vergence and accommodation while participants judged the depth of 3D stimuli. The distance conflict decreased depth constancy for stimulus displayed with negative disparity steps (divergence). Although vergence was unaffected by the stimulus distance, accommodation responses were significantly reduced when the stimulus was displayed with negative disparities. Our results show that biases in perceived depth follow undershoots in the disparity-driven accommodation response. These findings suggest that accommodation responses (i.e., from oculomotor information) can contribute to perceived depth

How to compensate the effect of using an incomplete wavelet base to reconstruct an image? Application in psychovisual experiment

International audienceOne way in psychovisual experiment to understand human visual system is to analyze separately contents of different spatial frequency bands. To prepare images for this purpose, we proceed to a decomposition of the original image by a wavelet transform centered on selected scales. The wavelets used are Difference Of Gaussians (DOG) according to works modeling the human visual system. Before rebuilding the visual stimulus, various transformations can be performed on different scales to measure the efficiency of the observer, for a given task, according to the spatial frequencies used. The problem is that if we use an incomplete wavelet basis during decomposition, there is a significant loss of information between the original image and the reconstructed image. The work presented here offers a way to solve this problem by using coefficients appropriate for each scale during the decomposition step

Defect Detection on Inclined Textured Planes Using the Shape from Texture Method and the Delaunay Triangulation

We present one method for detecting defects on an inclined textured plane. This method uses a combination of a shape from texture (SFT) method with the Delaunay triangulation technique. The SFT method provides the theoretical equation of the plane orientation in two steps. First, a wavelet decomposition allows us to build an image of the inverse of the local frequency, that is the scale, that we call the local scales map. Then we perform an interpolation of this map using the equation of the theoretical variation of the scales. With the interpolation parameters it is possible to extract the texels by the use of an adaptive thresholding for each pixel of this map. Then we compute the centers of each texel in order to match a mesh on it after processing a Delaunay triangulation. When there is a defect, the regularity of the triangulation is disturbed, so one hole appears in the mesh.</p

Results of a shape from texture method and improvements obtained for macrotexture

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Conception of a psychovisual experiment for taking into account the information from different sensors images

International audienceTo perform tasks, such as detection or recognition of objects in natural environment by day or by night, it is possible to use images acquired from different sensors: natural images, thermal images from infrared sensor or images acquired during the night with light intensifier. Our goal is to improve the efficiency of operators performing these tasks by providing a synthetic image made from different sensors that will enhance the information content of each sensor. First we have to know the image characteristics we use: edges detection and spatial frequencies are statistically analyzed. They show the differences between the image sensors. Then we have to understand which information is important for the observer, from each sensor, for a given task. To obtain this knowledge, we have developed a psychovisual experiment to discriminate vehicles, by using the method of [Gosselin, Schyns, 2001, Vision Research 41, 2261-2271]. Stimuli presented to the observers are constructed by filtering the original image at different scales and multiplied by Gaussian “bubbles” that partially obscure the signal [Lelandais, Plantier, BIOSIGNALS2013, Spain]. The results of the psychovisual experiment give the number of bubbles necessary to perform the task and to determine the useful parts of vehicles for their discrimination