Vector Field Consensus Research Articles

Feature Scalar Field Grid-Guided Optical-Flow Image Matching for Multi-View Images of Asteroid

Images captured by deep space probes exhibit large-scale variations, irregular overlap, and remarkable differences in field of view. These issues present considerable challenges for the registration of multi-view asteroid sensor images. To obtain accurate, dense, and reliable matching results of homonymous points in asteroid images, this paper proposes a new scale-invariant feature matching and displacement scalar field-guided optical-flow-tracking method. The method initially uses scale-invariant feature matching to obtain the geometric correspondence between two images. Subsequently, scalar fields of coordinate differences in the x and y directions are constructed based on this correspondence. Next, interim images are generated using the scalar field grid. Finally, optical-flow tracking is performed based on these interim images. Additionally, to ensure the reliability of the matching results, this paper introduces three methods for eliminating mismatched points: bidirectional optical-flow tracking, vector field consensus, and epipolar geometry constraints. Experimental results demonstrate that the proposed method achieves a 98% matching correctness rate and a root mean square error of 0.25 pixels. By combining the advantages of feature matching and optical-flow field methods, this approach achieves image homonymous point matching results with precision and density. The matching method exhibits robustness and strong applicability for asteroid images with cross-scale, large displacement, and large rotation angles.

Application of Feature Tracking Using K-Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking

Application of Feature Tracking Using <i>K</i>-Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking

Robust Topological Navigation via Convolutional Neural Network Feature and Sharpness Measure

Visual navigation for mobile robots has emerged in recent years. Among the various methods, topological navigation using visual information provides a scalable map representation for large-scale mapping and navigation. A topological map is essentially a graph with keyframes as its nodes and adjacency relations as its edges. Previous topological mapping uses local feature descriptors, such as scale-invariant feature transform or Speeded-Up Robust Features, to select keyframes in mapping, localization, and estimate relative pose. In practice, local features are not robust for severe motion blur or large illumination change. In this paper, we improve topological mapping to make it more efficient and robust. First, we use a convolutional neural network (CNN) feature as the holistic image representation. The CNN feature can be used to effectively retrieve keyframes that have similar appearance from a topological map, and it is robust to motion blur and illumination change. Thus, it improves the performance for place recognition and robot relocalization. Second, we use sharpness measure to select high-quality keyframes and avoid selecting blurry ones. Third, an efficient and robust non-rigid matching method, vector field consensus, is used for efficient geometric verification and to retrieve the most similar keyframe. The qualitative and quantitative experimental results demonstrate that our method is satisfactory.

Image Feature Matching via Progressive Vector Field Consensus

In this letter, we propose a simple yet effective approach, named Progressive Vector Field Consensus (PVFC), for addressing the problem of finding more true feature correspondences between images. The key idea is to progressively perform feature matching based on Vector Field Consensus, and hence greatly boost the number of true matches as well as avoid false matches. More specifically, it uses matching results on a small putative correspondence set with high inlier ratio to guide the matching on a large putative correspondence set which probably covers the whole true correspondences. We model the transformation between images in a reproducing kernel Hilbert space, and a sparse approximation is applied to the transformation to avoid high computational complexity. Our results quantitatively show that our PVFC outperforms state-of-the-art methods, both in accuracy and in efficiency. Moreover, the progressive framework is general and can be applied to other cases for robust estimation.

Robust point matching via vector field consensus.

In this paper, we propose an efficient algorithm, called vector field consensus, for establishing robust point correspondences between two sets of points. Our algorithm starts by creating a set of putative correspondences which can contain a very large number of false correspondences, or outliers, in addition to a limited number of true correspondences (inliers). Next, we solve for correspondence by interpolating a vector field between the two point sets, which involves estimating a consensus of inlier points whose matching follows a nonparametric geometrical constraint. We formulate this a maximum a posteriori (MAP) estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. We impose nonparametric geometrical constraints on the correspondence, as a prior distribution, using Tikhonov regularizers in a reproducing kernel Hilbert space. MAP estimation is performed by the EM algorithm which by also estimating the variance of the prior model (initialized to a large value) is able to obtain good estimates very quickly (e.g., avoiding many of the local minima inherent in this formulation). We illustrate this method on data sets in 2D and 3D and demonstrate that it is robust to a very large number of outliers (even up to 90%). We also show that in the special case where there is an underlying parametric geometrical model (e.g., the epipolar line constraint) that we obtain better results than standard alternatives like RANSAC if a large number of outliers are present. This suggests a two-stage strategy, where we use our nonparametric model to reduce the size of the putative set and then apply a parametric variant of our approach to estimate the geometric parameters. Our algorithm is computationally efficient and we provide code for others to use it. In addition, our approach is general and can be applied to other problems, such as learning with a badly corrupted training data set.

MANIFOLD IMAGE PROCESSING FOR SEE-THROUGH EFFECT IN LAPAROSCOPIC SURGERIES

This approach shows the processing of multiple images of a virtually transparent epidermal imagery (VTEI) system for LESS surgical procedure. In this paper, a novel Homographic Image Mosaicking and Morphing (HIMM) procedure is proposed for image-stitching and also for compensating the irregular surfaces, where scale invariant feature detection (SIFT) algorithm for detecting a set of feature points, vector field consensus (VFC) algorithm for mismatch rejection, Gauss Newton algorithm for non – linear minimization and backward image warping algorithms are used to obtain the see-through effect. This view is then noticeable to the surgeon in VTEI for laparoscopic surgeries.

Regularized vector field learning with sparse approximation for mismatch removal

In vector field learning, regularized kernel methods such as regularized least-squares require the number of basis functions to be equivalent to the training sample size, N. The learning process thus has O(N3) and O(N2) in the time and space complexity, respectively. This poses significant burden on the vector learning problem for large datasets. In this paper, we propose a sparse approximation to a robust vector field learning method, sparse vector field consensus (SparseVFC), and derive a statistical learning bound on the speed of the convergence. We apply SparseVFC to the mismatch removal problem. The quantitative results on benchmark datasets demonstrate the significant speed advantage of SparseVFC over the original VFC algorithm (two orders of magnitude faster) without much performance degradation; we also demonstrate the large improvement by SparseVFC over traditional methods like RANSAC. Moreover, the proposed method is general and it can be applied to other applications in vector field learning.