Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images

<span>The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate ‘F’. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution ‘F’. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of ‘F’ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification.</span>

Deteksi Tungkai Bayi Pada Image Sequence Berbasis Vector Depth Estimation

Image processing in the image sequence for pattern recognition can be a solution for detecting limb movements in infants after surgery, but the camera is not calibrated. So we need the right method solution to be able to detect these conditions. This happens to cameras that are generally not calibrated and do not have the feature to calculate the vector depth for 3D reconstruction. Because to detect and find limb movement depth is needed to be able to do 3D reconstruction, because it is not only based on the x and y parameters but also z so that with the additional parameters it makes it easier to analyze the motion of the motion axis and the motion vector. This paper discusses a method for detecting 2D motion into a 3D-based motion vector by sequencing the image sequence image then finding the point of transfer of the motion frame destination from the frame reference frame by obtaining the depth (depth vector) using the fundamental matrix from the generated motion vector. This method is recommended because it can perform 3D reconstruction from input in the form of 2D image sequences by calculating the intrinsic parameters so that 3D reconstruction can be carried out. So that the limb vector movement in infants that was originally 2D can be reconstructed into 3D based and makes it easier to carry out the analysis because of the additional parameters.

Correction: Kiskinov et al. Existence of Absolutely Continuous Fundamental Matrix of Linear Fractional System with Distributed Delays. Mathematics 2021, 9, 150

We have found that, in the right side of Equation (35) in our paper [...]

Fundamental Matrix Computing Based on 3D Metrical Distance

To reconstruct point geometry from multiple images, computation of the fundamental matrix is always necessary. With a new optimization criterion, i.e., the re-projective 3D metric geometric distance rather than projective space under RANSAC (Random Sample And Consensus) framework, our method can reveal the quality of the fundamental matrix visually through 3D reconstruction. The geometric distance is the projection error of 3D points to the corresponding image pixel coordinates in metric space. The reasonable visual figures of the reconstructed scenes are shown but only some numerical result were compared, as is standard practice. This criterion can lead to a better 3D reconstruction result especially in 3D metric space. Our experiments validate our new error criterion and the quality of fundamental matrix under the new criterion.

Evaluation of Solving Methods for the Fundamental Matrix Computation

Solving the fundamental matrix is a key step in many image calibration and 3D reconstruction systems. The goal of this paper is to study the performance of non-linear solvers for estimating the fundamental matrix in projector-camera calibration. To prevent measurements errors from distorting our understanding, synthetic data are created from ground-truth camera and projector parameters and then used for the assessment of four nonlinear solving strategies.