Khalid Satori

Fast and efficient geometric constraints optimization for an automatic 3D modelling

We present our 3D models reconstructed based on geometric constraints. Used constraints in 3D reconstruction are frequently inaccurate. In fact, we detect an important set of theme, which one can we choose as input of reconstruction method? So, redundancies, cycles and inaccurate 3D model present a challenge to overcome. It is therefore necessary to “beautify” constraint and to elaborate an accurate 3D model before use in reconstruction systems. We present in our work a new method of 3D model elaboration. So, we remove redundancies; cycles and we bring a safe and final 3D model with an optimal freedom degree of estimated parameters. Our reconstruction algorithm first prioritizes the constraint detection. User can add them manually or through an automatic process. But the important thing is to avoid 3D modeling step which is very challenging. To detect and eliminate redundant and inconsistent constraints use freedom degree analysis. Results from our implementation show that the method can beautify and optimize recovered 3D models correctly at acceptable speed

Important approach to 3D reconstruction of tridimensional objects based on multiple plan images

In the present paper, we will focus on a new approach for efficient and reliable tridimensional reconstruction of objects from flat images. Our approach allows the realisation of tridimensional reconstruction without passing through the calibration and self-calibration phase of the camera, but based on the estimation of the fundamental matrix and the homography at infinity to have the projective, affine and Euclidean projection. Our method is based firstly on a very important step in the 3D reconstruction that is the detection of interest points using the Harris detector to have a sufficient number of matches distributed on the images, these matches are used to estimate the 3D points, and secondly to estimate the projection matrices that are made from different existing relationships between the three types of tridimensional reconstruction (projective reconstruction, affine reconstruction, Euclidean reconstruction). Experimental results prove that this method is practical and gives satisfying results without going through the calibration step.

Amazigh audiovisual speech recognition system design

It is well known that speech recognition is a multimodal process which uses information not only from audio but also from vision. This paper describes our experience to design an audio visual speech recognition system, which relates the acoustic and the visual information in order to improve noise robustness of automatic speech recognition. The accuracy rate for face and mouth detection using Viola-Jones approach was satisfactory (reaches to 99% and 96.6% for face and mouth detection respectively).

Camera Self-Calibration with Varying Intrinsic Parameters by an Unknown Three-Dimensional Scene

In the present paper, we will propose a new and robust method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used to determine the projection matrices. The present method is based on the formulation of a non linear cost function from the determination of a relationship between two points of the scene with their opposite relative to the axis of abscise and their projections in the image planes. The resolution of this function with genetic algorithm enables us to estimate the intrinsic parameters of different cameras. The important of our approach reside in the use of a single pair of images which provides fewer equations, simplifies the mathematical complexities and minimizes the execution time of the application, the use of the data of the first image only without the use of the data of the second image, the use of any camera which makes the intrinsic parameters variable not constant and the use of a 3D scene reduces the planarity constraints. The experimental results on synthetic and real data prove the performance and robustness of our approach.

Camera self-calibration with varying intrinsic parameters and arc of the circle

This paper presents a robust and important method for the self-calibration of a numerical camera using a tridimensional unknown scene. The particularity of our technique resides in the use of an arc of the circle and the localization of some of the matching points detected in the use of an ensemble of images in order to define in an automated way the intrinsic parameters, which are unknown and variable. Our approach is based on minimizing the constraints of the self-calibration of the camera with the projection of the two extremities of chord of each pair of images. It is obvious that in order to produce good results from the simulations we have made the choice of the right programming language would be of prime importance. Our main criteria were simplicity, stability and performing algorithms. The results have proven that the selection of the aforementioned criteria was successful.

Affine Reconstruction based on the Projective Reconstruction and Homography at Infinity

In the present article, we will focus on the 3D Reconstruction of objects from flat images. The method we propose allows the realization of the 3D Reconstruction of objects from images based on estimating the Affine Projection Matrix made from different existing relationships between the three methods of Three-dimensional Reconstruction and without the passage through the calibration phase and self calibration of the cameras.