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Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discriminability. In this study, a novel keypoint detection approach based on the local variation of surface (LVS) is proposed. The LVS keypoint detection method comprises three main steps. Firstly, the surface variation index for each point is calculated using the local coordinate system. Subsequently, points with a surface variation index lower than the local average are identified as initial keypoints. Lastly, the final keypoints are determined by selecting the minimum value within the neighborhood from the initial keypoints. Additionally, a sampling consensus correspondence estimation algorithm based on geometric constraints (SAC-GC) for efficient and robust estimation of optimal transformations in correspondences is proposed. By combining LVS and SAC-GC, we propose a coarse-to-fine point cloud registration algorithm. Experimental results on four public datasets demonstrate that the LVS keypoint detection algorithm offers improved repeatability and robustness, particularly when dealing with noisy, occluded, or cluttered point clouds. The proposed coarse-to-fine point cloud registration algorithm also exhibits enhanced robustness and computational efficiency. [ABSTRACT FROM AUTHOR]

We introduce a novel discrete cosine transform-based feature (DCTF) descriptor designed for both robustly matching features in aerial video and tracking features across wide-baseline oblique views in aerial wide area motion imagery (WAMI). Our DCTF descriptor preserves local structure more compactly in the frequency domain by utilizing the mathematical properties of the discrete cosine transform (DCT) and outperforms widely used the spatial-domain feature extraction methods, such as speeded up robust features (SURF) and scale-invariant feature transform (SIFT). The DCTF descriptor can be used in combination with other feature detectors, such as SURF and features from accelerated segment test (FAST), for which we provide experimental results. The performance of DCTF for image matching and feature tracking is evaluated on two city-scale aerial WAMI data sets (ABQ-215 and LA-351) and a synthetic aerial drone video data set digital imaging and remote sensing image generation (Rochester Institute of Technology (RIT)-DIRSIG). DCTF is a compact 120-D descriptor that is less than half the dimensionality of state-of-the-art deep learning-based approaches, such as SuperPoint, LF-Net, and DeepCompare, which requires no learning and is domain-independent. Despite its small size, the DCTF descriptor surprisingly produces the highest image matching accuracies (F1 = 0.76 and ABQ-215), the longest maximum and average feature track lengths, and the lowest tracking error (0.3 pixel, LA-351) compared with both handcrafted and deep learning features. [ABSTRACT FROM AUTHOR]

Abstract We present an efficient approach for the parameterization of triangle soups. Our technique tackles the problem by first approximating the triangle soup by a watertight 2-manifold offset mesh proxy. By establishing point correspondences between the triangle soup and the shape proxy by tracing electrostatic field lines in time O (kn) instead of O (kn 2), we can transfer properties computed on the surface, such as UV-coordinates even for large input meshes. The technique can process large triangle soups by replacing the computational intensive physical calculations by approximations developed initially for large-scale physical simulations. Thus, it is possible to decrease the time complexity of the initial charge distribution algorithm from O (n 3) to O (n). We demonstrate our method on a multitude of challenging triangle soups and focus on the transfer of UV-coordinates during our experiments. An intensive run-time analysis and a comparison of our results to state of the art techniques in standard modeling tools concludes our study. Graphical abstract [ABSTRACT FROM AUTHOR]

Highlights • Rejecting outliers from a set of point correspondences without assuming an underlying model. • Not assuming a model interpreting the scene, the method is applicable to non-rigid scenes. • The method optimizes the point topologies in two views. • The approach is superior to the SOA in term of outlier filtering accuracy. • Iteratively applying graph-cut and re-fitting makes the method real-time. Abstract An approach is proposed for outlier rejection from a set of 2D point correspondences which does not require any underlying models, e.g. fundamental matrix. The solution is obtained by minimizing an energy originated from the neighborhood-graphs in both images using a grab-cut-like algorithm: iterated graph-cut and re-fitting. The method is validated on publicly available datasets, it is real time for most of the problems and achieves more accurate results than RANSAC and its state-of-the-art variants in terms of outlier rejection ratio. It is applicable to scenes where a single fundamental matrix is not estimable, e.g. non-rigid or degenerate ones. [ABSTRACT FROM AUTHOR]

Liver is considered as a tissue with large shape variations. Representation of such a complex shape cannot be accurately performed using conventional SSM algorithms. We propose to decompose a human liver into its anatomical parts and use them as a guide when considering point correspondences. To cope with shape complexity, a modified coherent point drift (CPD) algorithm is proposed too. The modified CPD algorithm assigns fuzzy correspondences to points and follows a simulated annealing approach to convert fuzzy correspondences into binary ones. Our modification includes automatic parameter settings which results in robustness of the algorithm. The proposed algorithm was compared to the thin plate spline-robust point matching (TPS-RPM) and minimum description length (MDL) techniques. Our method is twice faster than the MDL algorithm. Compared to the TPS-RPM algorithm, our method improved mean Specificity of the right lobe by 0.09 and Compactness of the model by two less modes. [ABSTRACT FROM AUTHOR]

Different concepts of correspondence findings in contactless optical three-dimensional (3-D) measurement systems using fringe projection are analyzed concerning the accuracy of the 3-D point calculation. These concepts are different concerning the kind of performance of the triangulation procedure in order to calculate the resulting 3-D points and the use of geometric constraints versus second projection sequence. Triangulation may be alternatively performed between camera pixels and the phase origin of the projection, between one camera pixel in the prior camera and the image of the corresponding measured phase value in the second camera, or between the image points of certain raster phase values in the two observation cameras. Additionally, triangulation procedures can be distinguished concerning the use of two perpendicular projection directions of the fringes versus the use of geometric constraints, i.e., epipolar geometry instead of the second projection direction. Advantages and disadvantages of the different techniques are discussed. In addition, a theoretical analysis of the application of synthetic data has been simulated as well as experiments performed on real measurement data. Both simulations and real data experiments confirm the theoretical assumptions concerning the magnitudes of the random errors occurring in 3-D point determination. [ABSTRACT FROM AUTHOR]

A novel method ICF (Identifying point correspondences by Correspondence Function) is proposed for rejecting mismatches from given putative point correspondences. By analyzing the connotation of homography, we introduce a novel concept of correspondence function for two images of a general 3D scene, which captures the relationships between corresponding points by mapping a point in one image to its corresponding point in another. Since the correspondence functions are unknown in real applications, we also study how to estimate them from given putative correspondences, and propose an algorithm IECF (Iteratively Estimate Correspondence Function) based on diagnostic technique and SVM. Then, the proposed ICF method is able to reject the mismatches by checking whether they are consistent with the estimated correspondence functions. Extensive experiments on real images demonstrate the excellent performance of our proposed method. In addition, the ICF is a general method for rejecting mismatches, and it is applicable to images of rigid objects or images of non-rigid objects with unknown deformation. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the sparse view matching problem where the camera parameters lie within ranges depending on the sensor used. An approach, based on homographic transformation, is proposed. The operation is split into two phases. The first phase deals with matches on the ground surface, which is considered planar. The second phase detects matches on arbitrary planes. This is done by detecting junctions of different shapes and reconstructing planes inferred by them. Two versions of that approach are suggested based on the sum of absolute differences and the variance normalized correlation techniques. The first technique is computationally inexpensive while the later is more robust to changes in lighting condition between views. Experiments show that our approach outperforms non-homographic correlation. [Copyright &y& Elsevier]

A visual servoing strategy for keeping features in the field of view is proposed which consists of a switching among position-based con- trol strategies and backward motion. In the absence of uncertainty on the extrinsic parameters, all features are kept in the field of view. Moreover, if the intrinsic parameters are also known, the trajectory length is minimized in the rotational space and, for some cases, also minimized in the translational space. Simulation results also show a certain degree of robustness against uncertainty on the intrinsic parameters. [ABSTRACT FROM AUTHOR]

A novel image sequence segmentation method which combines both spatial and temporal information is presented in this paper. The first step is an intensity segmentation scheme based on the edgeflow method. The temporal information is introduced through three-dimensional (3-D) motion estimation parameters. In the second step, regions obtained from the first step are clustered according to their 3-D motion models. In order to reduce the noise sensitivity of the motion estimation process, we introduce a robust method which produces accurate motion parameters and facilitates the correct clustering that follows. This ensures that rigid objects with luminance discontinuities can be segmented correctly. The method has been successfully tested in real imagery and typical examples are presented in this paper. [ABSTRACT FROM AUTHOR]

We present a novel method for estimating motion parameters and point correspondences between 3D surfaces under small nonrigid motion. A vector point function is utilized as the motion parameter, called the displacement function. Differential-geometric changes of surfaces are then used in tracking small deformations. Discriminant (of first fundamental form), unit-normal and Gaussian curvature are the invariant differential-geometric parameters that have been utilized for nonrigid motion analysis.Tests were performed by generating nonrigid motion on a simulated data set to illustrate performance and accuracy of our algorithms. Experiments were then performed on a Cyberware range data sequence of facial motion. A total of 16 sets of facial motion images were used in our experiments, belonging to eight different persons, each having two facial expressions. We have demonstrated the correct point correspondence recovery by tracking features of the face during each facial expression and comparing against the manual tracking of feature points by different users. In addition, nonrigid motion segmentation and interpolation of intermediate frames of data were successfully performed on these images. We have also performed experiments on cardiac data in order to estimate the motion parameters related to the abnormality in cardiac motion. Two sets of volumetric CT data of the left ventricle of a dog''s heart in cardiac cycle were used in our experiments. All our experiments indicate that the system performs very well and proves to be extremely useful in other nonrigid motion analysis applications. [Copyright &y& Elsevier]