A New Approach for Stereo Matching Algorithm with Dynamic Programming

Stereo matching algorithms are one of heavily researched topic in binocular stereo vision. Massive 3D information can be obtained by finding correct correspondence of different points between images captured from different views. Development of stereo matching algorithm is done for obtaining disparity maps i.e. depth information. When disparities computed for scan lines then dense reconstruction becomes time consuming for vision navigation systems. So for pair of stereo images proposed method extracts features points those are at contours in images and then a dynamic program is used to find the corresponding points from each image and calculates disparities. Also to reduce the noise which may lead to incorrect results in stereo correspondence, a new stereo matching algorithm based on the dynamic programming is proposed. Generally dynamic programming finds the global minimum for independent scan lines in polynomial time. While efficient, its performance is far from desired one because vertical consistency between scan lines is not enforced. This method review the use of dynamic programming for stereo correspondence by applying it to a contour instead to individual scan lines. Proposed methodology will obtain the global minimum for contour array in linear time using Longest Common Subsequent (LCS) dynamic programming method with no disparity space image (DSI). DOI: 10.17762/ijritcc2321-8169.15025

THE IDENTIFICATION OF IMPULSES IN 3-D RECONSTRUCTED DATA USING RECURSIVE FILTERS ON THE ORIGINALLY DIGITIZED 2-D IMAGE DATA

INTRODUCTION: Reconstructed three-dimensional (3-D) data is usually processed using either a low-pass Butterworth filter or quintic spline, though neither gives an implicit indication that the processed data is more accurate than the original data. This study investigated the effect of processing the original twodimensional (2-D) data before reconstruction, a technique commonly applied in the communications industry (e.g., the removal of noise prior to amplification). Reconstruction of the 3-D data can be considered successful when the condition of coplanarity is met (i.e., the lines from each centre of projection pass through their respective image points and intersect at the object point). The 3-D data is considered to be more accurate if the lines converge after processing (i.e. the residual error of the reconstructed points was reduced) and less accurate if the lines diverge. The independent application of a recursive filter in both forward and backward directions to the 2-D data enables the detection of a discontinuity. Each original 2- D data set can then be considered as two different data sets whose paths intersect at the discontinuity caused by an impulse. The timing of the impulse can be calculated from the extrapolated curves even if it was not explicitly captured on film. METHOD: Two cameras were located so that their optical axes were approximately orthogonal to each other and a 3-D volume was calibrated to generate the linear transformation parameters. A bouncing ball was then filmed within the calibrated volume, digitized and then passed through a Kalman filter in both the forward and backward directions to determine the timing of the impulse, (i.e., when the ball hit the ground). RESULTS: Two cameras provide four image co-ordinates and thus four equations with three unknowns so the 3-D object space co-ordinates were calculated using least squares estimation. To ensure only random noise was removed, the processed 2-D signal must have the same total energy as the original 2-D signal, even if the energy density spectrum has altered and the filter was deemed to be effective if the residual error of the reconstruction was reduced. Using this method, it was possible to identify when the impulse forces occurred, for both image sets, independently. CONCLUSIONS: This technique breaks a signal down into a series of discontinuous signals, each discontinuity indicating the application of an impulse to the original measured data. With proper camera placement any impulse can be observed independently in both the 2-D images so the impulses can also be used to synchronize previously unsynchronized data

Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.Comment: 19 pages, 14 figures, 7 tables, J. Math. Imaging Vi

Optimization code with weighting function for the reconstruction of coronal magnetic fields

We developed a code for the reconstruction of nonlinear force-free and non-force-free coronal magnetic fields. The 3D magnetic field is computed numerically with the help of an optimization principle. The force-free and non-force-free codes are compiled in one program. The force-free approach needs photospheric vector magnetograms as input. The non-force-free code additionally requires the line-of-sight integrated coronal density distribution in combination with a tomographic inversion code. Previously the optimization approach has been used to compute magnetic fields using all six boundaries of a computational box. Here we extend this method and show how the coronal magnetic field can be reconstructed only from the bottom boundary, where the boundary conditions are measured with vector magnetographs. The program is planed for use within the Stereo mission.Comment: 23 pages, 5 figure

Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System

Az http://intechweb.org/ alatti "Books" fül alatt kell rákeresni a "Stereo Vision" címre és az 1. fejezetre