Mostafa Merras

Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene

This work proposes a 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 with fundamental matrices to determine the projection matrices. The present approach is based on the formulation of a nonlinear cost function from the determination of a relationship between two points of the scene and their projections in the image planes. The resolution of this function enables us to estimate the intrinsic parameters of different cameras. The strong point of the present approach is clearly seen in the minimization of the three constraints of a self-calibration system (a pair of images, 3D scene, any camera): The use of a single pair of images provides fewer equations, which minimizes the execution time of the program, the use of a 3D scene reduces the planarity constraints, and the use of any camera eliminates the constraints of cameras having constant parameters. The experiment results on synthetic and real data are presented to demonstrate the performance of the present approach in terms of accuracy, simplicity, stability, and convergence.

A new method of camera self-calibration with varying intrinsic parameters using an improved genetic algorithm

In this paper, we present a new method of camera self-calibration with varying intrinsic parameters by an improved genetic algorithm. Firstly, the simplified Kruppa equation (the case of varying intrinsic parameters) defined by Hartley is translated into the optimized cost function. Secondly, the minimization of the cost function is calculated by an optimized modified genetic algorithm. Finally, the intrinsic parameters of the camera are obtained. Comparing to traditional optimization methods, the camera self-calibration with varying intrinsic parameters by this approach can avoid being trapped in a local minimum and converge quickly to the optimal solution without initial estimates of the camera parameters. Our study is performed on synthetic and real data to demonstrate the validity and performance of the presented approach. The results show that the proposed technique is both accurate and robust.

Multi-view 3D reconstruction and modeling of the unknown 3D scenes using genetic algorithms

This paper presents a complete pipeline of the reconstruction and the modeling of the unknown complex 3D scenes from a sequence of unconstrained images. The proposed system is based on the formulation of a nonlinear cost function by determining the relationship between 2D points of the images and the cameras parameters; the optimization of this function by a genetic algorithm makes finding the optimal cameras parameters. The determination of these parameters allows thereafter to estimate the 3D points of the observed scene. Then, the mesh of the 3D points is achieved by 3D Crust algorithm and the texture mapping is performed by multiple dependent viewpoints. Extensive experiments on synthetic and real data are performed to validate the proposed approach, and the results indicate that our system is robust and can achieve a very satisfactory reconstruction quality.

3D reconstruction system based on incremental structure from motion using a camera with varying parameters

In this paper, we present a flexible and fast system for multi-scale objects/scenes 3D reconstruction from uncalibrated images/video taken by a moving camera characterized by variable parameters. The proposed system is based on incremental structure from motion and good exploitation of bundle adjustment. At first, from two selected images, our system allows to recover, in a well-chosen reference, coordinates of a set of 3D points. In this context, we have proposed a new method of self-calibration based on the use of two unknown scene points with their image projections. After that, new images are inserted progressively using 3D information already obtained. Local bundle adjustment is used to adjust the new estimated entities. At some time, we introduce a global bundle adjustment to adjust as best as possible all estimated entities and to have an initial 3D model of quality covering an interesting part of the object/scene. This model will be used as reference for the insertion of the rest of images. The proposed system allows to obtain satisfactory results within a reasonable time.

Camera self-calibration having the varying parameters and based on homography of the plane at infinity

In this approach, we will process a self-calibration problem of camera characterized by varying parameters. Our approach is based on estimating of homography of the plane at infinity and depths of interest points. This estimation is made from resolution of nonlinear equation system that is formulated from the projection of some points of the scene in the planes of different images. The relationships established between the homography of the plane at infinity and matches, between images, and those established between points of the 3D scene and their projections, in image planes, allow formulating the second nonlinear equations. This system is minimized by using the Levenberg-Marquardt algorithm to estimate the intrinsic parameters of used camera. This approach has several strong points: i) The use of any cameras (having varying parameters), ii) The use of any scenes (3D) and iii) the use of a minimum number of images (two images only). Experiments and simulations show the performance of this approach in terms of stability, accuracy and convergence.

A Novel Text Encryption Algorithm Based on the Two-Square Cipher and Caesar Cipher

Security of information has become a popular subject during the last decades, it is the balanced protection of the Confidentiality, Integrity and Availability of data, also known as the CIA Triad. In this work, we introduce a new hybrid system based on two different encryption techniques: two square cipher and Caesar cipher with multiples keys. This homogeneity between the two systems allows us to provide the good properties of the two square cipher method and the simplicity of the Caesar cipher method. The security analysis shows that the system is secure enough to resist brute-force attack, and statistical attack. Therefore, this robustness is proven and justified.

Method of 3D mesh reconstruction from sequences of calibrated images

In this paper we propose a technical mesh of an unknown 3D scene from two or more images taken by cameras having the constant parameters based on genetic algorithms. The present approach is based on the formulation of a nonlinear cost function from the determination of a relationship between points of the image planes and all parameters of the cameras. The minimization of this function by a genetic approach enables us to simultaneously estimate the intrinsic and extrinsic parameters of the cameras. These parameters are used with matching to determine the 3D point clouds. The mesh generation is performed by Delaunay triangulation. The strong point of this approach can be clearly seen in the minimization of the constraints of a self-calibration system: The use a genetic algorithm to accelerate the speed of convergence and avoid local minima to obtain a good estimation of the camera parameters and the use of a 3D scene reduces the planarity constraints. Our study is performed on real data to demonstrate the validity and performance of the presented approach in terms of accuracy, simplicity, stability, and convergence.