Monocular vision-based iterative pose estimation algorithm from corresponding feature points

In this paper, the applications of the iterative Gauss-Newton (GN) approach in nonlinear image restoration are considered. The convergence properties of a general class of nonlinear iterative algorithm are studied through the Global Convergence Theorem (GCT). The iterative GN algorithm for the solution of the least-squares optimization problem is presented. The computational complexity of this algorithm is enormous, making its implementation very difficult in practical applications. Structural modifications are introduced, which drastically reduce the computational complexity while preserving the convergence rate of the GN algorithm. With the structural modifications, the GN algorithm becomes particularly useful in nonlinear optimization problems. The convergence properties of the algorithms introduced are readily derived, on the basis of the generalized analysis and the GCT. The applications of these algorithms on practical problems, is demonstrated through an example.

Monocular Vision-based Two-stage Iterative Algorithm for Relative Position and Attitude Estimation of Docking Spacecraft

This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem.

In this paper, two iterative algorithms for NDA (nondata_aided) channel estimation are introduced to the DSJJWB system. The two iterative algorithms are named MMSE (minimum-mean-square-error) iterative algorithm and NMMSE(nonminimum-mean-square-error) iterative algorithm. The BER performances of the MRC Rake receiver with the two iterative algorithms are verified and compared via simulations. The simulation results show that the MMSE iterative algorithm performs better than NMMSE iterative algorithm for NDA channel estimation.

Deep feature descriptor based hierarchical dense matching for X-ray angiographic images

We present UWSPSM, an algorithm of uncertainty weighted stereopsis pose solution method based on the projection vector which to solve the problem of pose estimation for stereo vision measurement system based on feature points. Firstly, we use a covariance matrix to represent the direction uncertainty of feature points, and utilize projection matrix to integrate the direction uncertainty of feature points into stereo-vision pose estimation. Then, the optimal translation vector is solved based on the projection vector of feature points, as well the depth is updated by the projection vector of feature points. In the absolute azimuth solution stage, the singular value decomposition algorithm is used to calculate the relative attitude matrix, and the above two stages are iteratively performed until the result converges. Finally, the convergence of the proposed algorithm is proved, from the theoretical point of view, by the global convergence theorem. Expanded into stereo-vision, the fixed relationship constraint between cameras is introduced into the stereoscopic pose estimation, so that only one pose parameter of the two images captured is optimized in the iterative process, and the two cameras are better bound as a camera, it can improve accuracy and efficiency while enhancing measurement reliability. The experimental results show that the proposed pose estimation algorithm can converge quickly, has high-precision and good robustness, and can tolerate different degrees of error uncertainty. So, it has useful practical application prospects.

It is well-known that the stability of a first-order autonomous system can be determined by testing the symmetric positive definite solutions of associated Lyapunov matrix equations. However, the research on the constrained solutions of the Lyapunov matrix equations is quite few. In this paper, we present three iterative algorithms for symmetric positive semidefinite solutions of the Lyapunov matrix equations. The first and second iterative algorithms are based on the relaxed proximal point algorithm (RPPA) and the Peaceman–Rachford splitting method (PRSM), respectively, and their global convergence can be ensured by corresponding results in the literature. The third iterative algorithm is based on the famous alternating direction method of multipliers (ADMM), and its convergence is subsequently discussed in detail. Finally, numerical simulation results illustrate the effectiveness of the proposed iterative algorithms.

We propose a feature-based multi-target tracking algorithm which can track multiple targets in real time with a simple but efficient velocity segmentation method. The optical flow velocity distribution profile of the feature points detected on a moving target or on the background when the camera has ego-motion is assumed to have a Gaussian-like function. We can separate the background feature points and those of moving targets by examining the velocity distribution function profile at each frame without a prior knowledge on the number and texture of the moving objects. The optical flow velocity of a feature point in each image frame is computed by the iterative Lucas-Kanade algorithm. These feature points are divided into groups with the similar velocity. Feature points are further divided into sub-groups according to their proximity the image frame. The feature point group with the largest span is identified as the background feature and its velocity is the camera velocity when the background is fixed. Multiple targets are identified based on their velocities and proximity in one frame. With a pyramidal sampling scheme to reduce the frame size to one-sixteenth of its original size, and the iterative Lucas-Kanade algorithm to find the optical flow in a multi-resolution manner, we are able to track multiple objects in real time with a moving hand-held camera.

In this article, we try to proposed a new conjugate gradient method for solving unconstrained optimization problems, we focus on conjugate gradient methods applied to the non-linear unconstrained optimization problems, the positive step size is obtained by a line search and the new scalar to the new direction for the conjugate gradient method is derived from the quadratic function and Taylor series and by using quasi newton condition and Newton direction while deriving the new formulae. We also prove that the search direction of the new conjugate gradient method satisfies the sufficient descent and all assumptions of the global convergence property are considered and proved .in order to complete the benefit of our research we should take into account studied the numerical results which are written in FORTRAN language when the objective function is compared our new algorithm with HS and PRP methods on the similar set of unconstrained optimization test problems which is very efficient and encouragement numerical results.

Image metamorphosis transformation of facial images based on elastic body splines

Algorithm for simultaneous adaptation and time step iterations for the electromagnetic waves propagation and heating of the human head induced by cell phone

The problem of pose estimation arises in many areas of computer vision, including object recognition, object tracking, site inspection and updating, and autonomous navigation when scene models are available. We present a new algorithm, called SoftPOSIT, for determining the pose of a 3D object from a single 2D image when correspondences between object points and image points are not known. The algorithm combines the iterative softassign algorithm (Gold and Rangarajan, 1996; Gold et al., 1998) for computing correspondences and the iterative POSIT algorithm (DeMenthon and Davis, 1995) for computing object pose under a full-perspective camera model. Our algorithm, unlike most previous algorithms for pose determination, does not have to hypothesize small sets of matches and then verify the remaining image points. Instead, all possible matches are treated identically throughout the search for an optimal pose. The performance of the algorithm is extensively evaluated in Monte Carlo simulations on synthetic data under a variety of levels of clutter, occlusion, and image noise. These tests show that the algorithm performs well in a variety of difficult scenarios, and empirical evidence suggests that the algorithm has an asymptotic run-time complexity that is better than previous methods by a factor of the number of image points. The algorithm is being applied to a number of practical autonomous vehicle navigation problems including the registration of 3D architectural models of a city to images, and the docking of small robots onto larger robots.

Hierarchical iterative identification of output nonlinear Box-Jenkins Wiener model with ARMA noise

Hierarchical gradient based iterative parameter estimation algorithm for multivariable output error moving average systems

In this letter, we propose an iterative algorithm for the joint estimation of the channel covariance matrices and the channel vector for the spatially-correlated massive MIMO system with the following main advantages: low pilot overhead, improved mean square error (MSE), and low complexity. The results show that this scheme outperforms the conventional estimation schemes in terms of the required pilot overhead and the resulting MSE. The results also show that the achievable sum rate of the proposed scheme tends to that of the ideal case that the channel covariance matrices are assumed to be perfectly known at the base station.