Mammography screening also leads to a high rate of false positive results. This may lead to unnecessary worry, inconvenient follow-up care, additional imaging studies, and sometimes the need for tissue. blood draws (often a needle biopsy). Convolutional neural networks (CNN) are one of the most important networks in the field of deep learning. The neural networks form some feature vectors often contain weak features. There are known methods for eliminating weak features based on the mutual information. In this paper, we propose a convolutional neural network based to recognize local geometrical features. Computer simulation results are provided to illustrate the performance of the proposed method.
3D point cloud registration is of great importance in robotics and computer vision to find a rigid body transformation to align a pair of point clouds with unknown point correspondences. In recent years, the deep learning model has dominated the field of computer vision. The important part of registration is the estimation of correspondences between point clouds. The main idea of studying correspondences between point clouds is to establish correspondences through the multidimensional features of each point. In this paper, we propose a simple neural network algorithm to register incongruent point clouds. The proposed algorithm utilizes the virtual points and is partially based on the PointNet++ neural network. Computer simulation results are provided to illustrate the performance of the proposed method.
Simultaneous Localization and Mapping (SLAM) is the task of reconstructing an environmental model passed using onboard sensors and at the same time maintaining an estimate of the mobile sensor location within the model. One of the known approaches to the SLAM problem is the Kalman filter. The Kalman filter efficiency is based on the fact that it contains a fully correlated posterior over feature maps and mobile sensor poses. The important element of the SLAM problem is the reconstruction of the environmental 3D scene. In this paper, we propose an algorithm to restore the 3D scene using consistent condition and a modified version of the Kalman filter. The reconstruction algorithm is noniterative. Computer simulation results are provided to illustrate the performance of the proposed method.
KEYWORDS: Point clouds, 3D modeling, Clouds, Neural networks, Databases, Education and training, Matrices, Evolutionary algorithms, Network architectures, Singular value decomposition
Recently, there has been essential progress in the field of deep learning, which has led to compelling advances in most of the semantic tasks of computer vision, such as classification, detection, and segmentation. Point cloud registration is a task that aligns two or more different point clouds by evaluating the relative transformation between them. The Iterative Closest Points (ICP) algorithm and its variants have relatively good computational efficiency but are known to be subject to local minima, so rely on the quality of the initialization. In this paper, we propose a neural network based on the Deep Closest Points (DCP) neural network to solve the point cloud registration problem for incongruent point clouds. Computer simulation results are provided to illustrate the performance of the proposed method.
The important task of 2D image classification and segmentation is the extraction of the local geometrical features. The convolution neural network is the common approach last years in this field. Usually, the neighborhood of each pixel of the image is implemented to collect local geometrical information. The information for each pixel is stored in a matrix. Then, Convolutional Auto-Encoder (CAE) is utilized to extract the main geometrical features. In this paper, we propose a neural network based on CAE to solve the extraction of local geometrical features problem for noisy images. Computer simulation results are provided to illustrate the performance of the proposed method.
KEYWORDS: Point clouds, 3D modeling, Clouds, Matrices, Databases, Computer simulations, Reconstruction algorithms, Singular value decomposition, Sensors, Mobile robots
Point cloud registration is a central problem in many mapping and monitoring applications such as 3D model reconstruction, computer vision, autonomous driving, and others. Generating maps of the environment is often referred to as the Simultaneous Localization and Mapping (SLAM) problem. Note that some point clouds from the considered set may not have intersections. In this paper, we propose an algorithm to align the multiple point clouds based on an effective pairwise registration and a global refinement algorithm. The global refinement algorithm is non-iterative. Computer simulation results are provided to illustrate the performance of the proposed method.
Point cloud is an important type of geometric data structure. Various applications require high-level point cloud processing. Instead of defining geometric elements such as corners and edges, state-of-the-art algorithms use semantic matching. These methods require learning-based approaches that rely on a statistical analysis of labeled datasets. Adapting deep learning techniques to handle 3D point clouds remains challenging. The standard deep neural network model requires regular inputs such as vectors and matrices. Three-dimensional point clouds are fundamentally irregular; that is, the positions of points are continuously distributed in space, and any permutation of their order does not change the spatial distribution. Modern deep neural networks are designed specifically to process point clouds directly, without going to an intermediate regular representation. The Deep Closest Point (DCP) network is a neural network that implements the ICP algorithm. DCP utilizes the point-to-point functional for error metric minimization. In this paper, we propose the modified variant of DCP based on other types of ICP error minimization functionals. Computer simulation results are provided to illustrate the performance of the proposed algorithm.
Registration of point clouds in three-dimensional space is an important task in many areas of computer vision, including robotics and autonomous driving. The purpose of registration is to find a rigid geometric transformation to align two point clouds. The registration problem can be affected by noise and incomplete data availability (partiality). Iterative Closed Point (ICP) algorithm is a common method for solving the registration problem. Usually, the ICP algorithm monotonically reduces functional values, but owing to the problem of non-convexity, the algorithm often stops at suboptimal local minima. Thus, an important characteristic of the registration algorithm is its ability to avoid local minima. The probability of obtaining an acceptable transformation as a result of the ICP algorithm is a comparative criterion for different types of ICP algorithms and other types of registration algorithms. In this paper, we propose an ICP-type registration algorithm that uses a new type of error metric functional. The functional uses fine geometrical characteristics of the point cloud. Computer simulation results are provided to illustrate the performance of the proposed method.
Point cloud registration is an important method in 3D point cloud processing, which is used in computer vision, autonomous driving, and other fields. Point cloud registration looks for the optimal rigid transformation that can align two input point clouds to a common coordinate system. The most common method of alignment using geometric characteristics is the Iterative Closest Point (ICP) algorithm. The disadvantage of classical ICP variants, such as pointto-point and point-to-plane, is their dependence on the initial placement of point clouds. If the rotation that can align two point clouds is sufficiently large, the ICP algorithm can converge to a local minimum. Coarse point clouds registration algorithms are used to find a suitable initial alignment of two clouds. In particular, feature-based methods for coarse registration are known. In this paper, we propose an algorithm to extract the common parts of the incongruent point clouds and coarsely aligning them. We use the SHOT algorithm to find a match between two point clouds. The corresponding neighborhoods are obtained by the correspondence between points. The neighborhoods define local vector bases that allow computing an orthogonal transformation. The proposed algorithm extracts common parts of incongruent point clouds. Computer simulation results are provided to illustrate the performance of the proposed method.
Alignment two point clouds means finding the orthogonal or affine transformation in three-dimensional space that maximizes the consistent overlap between two clouds. The ICP (Iterative Closest Points) algorithm is the most known technique of the point clouds registration based on the exclusively geometric characteristics. The ICP algorithm consists of the following iteratively applied main steps: determine the correspondence between the points of the two clouds; minimize error metrics (variational subproblem of the ICP algorithm). The key element of the ICP algorithm is the search for an orthogonal or affine transformation, which is the best in sense of a metric combining two clouds of points with a given correspondence between the points. The correspondence between point clouds in real applications is far from ideal. The bad correspondence significantly reduces the probability to obtain a good answer for orthogonal variants of the variational problem. Thus, the probability of obtaining an acceptable transformation as a result of the ICP algorithm with poor correspondence is the comparative criterion for different types of variational problems. In this paper, we propose a regularized variant of the ICP variational problem use a rough point clouds alignment that improved convergence frequency in the case of poor correspondence between point clouds. The proposed modified approach essentially increases the performance of the algorithm. Computational experiments show that the proposed point clouds alignment approach calculates true transformation for almost all synthetic 3D models for any relative placement of the point clouds.
3D reconstruction has been widely applied in medical images, industrial inspection, self-driving cars, and indoor modeling. The 3D model is built by the steps of data collection, point cloud registration, surface reconstruction, and texture mapping. In the process of data collection, due to the limited visibility of the scanning system, the scanner needs to scan multiple angles and then splice the data to obtain a complete point cloud model. The point clouds from different angles must be merged into a unified coordinate system, which is known as point cloud registration. The result of point cloud registration can directly affect the accuracy of the point cloud model; thus, point cloud registration is a key step in the construction of the point cloud model. The ICP (Iterative Closest Points) algorithm is the most known technique of the point cloud registration. The variational ICP problem can be solved not only by deterministic but also by stochastic methods. One of them is Grey Wolf Optimizer (GWO) algorithm. Recently, GWO has been applied to rough point clouds alignment. In the proposed paper, we apply the GWO approach to the realization of the point-to-point ICP algorithms. Computer simulation results are presented to illustrate the performance of the proposed algorithm.
Geometric registration is a key task in many computational fields, including medical imaging, robotics, autonomous driving. The registration involves the prediction of a rigid motion to align one point cloud to another, potentially distorted by noise and partiality. The most popular point cloud registration algorithm, Iterative Closest Point (ICP), alternates between estimating the rigid motion based on a fixed correspondence estimate and updating the correspondences to their closest matches. Recently, the success of deep neural networks for image processing has motivated an approach to learning features on point clouds. Adaptation of deep learning to analyze point cloud data is far from straightforward. Most critically, standard deep neural network models require input data with regular structure, while point clouds are fundamentally irregular: Point positions are continuously distributed in the space, and any permutation of their ordering does not change the spatial distribution. Several neural networks have recently been proposed for analyzing point clouds data such as PointNet and DGCNN. In this paper, we propose a permutation invariant neural network to identify matching pairs of points in the clouds. Computer simulation results are provided to illustrate the performance of the proposed algorithm.
Denoising has numerous applications in communications, control, machine learning, and many other fields of engineering and science. Total variation (TV) regularization is a widely used technique for signal and image restoration. There are two types of TV regularization problem: anisotropic TV and isotropic TV. One of the key difficulties in the TV-based image denoising problem is the nonsmoothness of the TV norms. There are known exact solutions methods for 1D TV regularization problem. Strong and Chan derived exact solutions to TV regularization problem for onedimensional case. They obtained the exact solutions when the original noise-free function, noise and the regularization parameter are subject to special constraints. Davies and Kovac considered the problem as non-parametric regression with emphasis on controlling the number of local extrema, and in particular consider the run and taut string methods. Condat proposed a direct fast algorithm for searching the exact solutions to the one-dimensional TV regularization problem for discrete functions. In the 2D case, some methods are used to approximate exact solutions to the TV regularization problem. In this presentation, we propose a new approximation method for 2D TV regularization problem based on the fast exact 1D TV approach. Computer simulation results of are presented to illustrate the performance of the proposed algorithm for the image restoration.
Over the past two decades, methods have been proposed for deaerating images, and most of them use a method of improving or restoring images. An image without haze should have a higher contrast than the original hazed image. It is possible remove haze by increasing the local contrast of the restored image. Some haze removal approaches estimate a hazed image from the observed hazed scene by solving an objective function whose parameters are adapted to the local statistics of the hazed image inside a moving window. Common image dehazing techniques use only one observed image for processing. Various variants of local adaptive algorithms for single image dehazing are known. A dehazing method based on spatially displaced sensors is also described. In this presentation, we propose a new dehazing algorithm that uses several scene images. Using a set of observed images, the dehazing of the image is carried out by solving a system of equations, which is derived from the optimization of the objective function. These images are made in such a way that they are spatially offset relative to each other and made in different time. Computer simulation results of are presented to illustrate the performance of the proposed algorithm for the restoration of hazed images.
Human facial expressions describe a set of signals, which can be associated with mental states such as emotions depending on physiological conditions. There are many potential applications of expression recognition systems. They take into account about two hundred emotional states. Expression recognition is a challenging problem, not only due to the variety of expressions, but also due to difficulty in extraction of effective features from facial images. Depending on a 3D reconstruction technique, 3D data can be immune to a great range of illumination and texture variations, and they are no sensitive as 2D images to out-of-plane rotations. Moreover, 2D images may fail to capture subtle but discriminative change on the face if there is no sufficient change in brightness, such as bulges on the cheeks and protrusion of the lips. In fact, 3D data yield better recognition than conventional 2D data for many types of facial actions The most effective tool for solution of the problem of human face recognition is neural networks. But the result of recognition can be spoiled by facial expressions and other deviation from canonical face representation. In the proposed presentation we describe a resampling method of human faces represented by 3D point clouds. The method based on the non-rigid ICP (Iterative Closest Point) algorithm. We consider the combined using of this method and convolutional neural network (CNN) in the face recognition task. Computer simulation results are provided to illustrate the performance of the proposed algorithm.
Reconstruction of 3D map of the observed scene in real space is based on the information on 3D points coordinates. Triangulation is the known method for surface representation in three-dimensional space. Two consistently obtained frames with point clouds corresponds to two partially overlapping triangulated surfaces. There is known an algorithm for the correct construction of the triangulation of the overlapping area. Another approach to the build of a 3D map is the use of surfels. Surfel is a round patch on the surface. The characteristics of the surfels are described by a triple of elements: the position of the surfel, the normal to the surfel, the radius of the surfel. One more recently developed method for constructing a three-dimensional map of the scene is the so-called “octree”. Octrees are a hierarchical data structure for a spatial representation of an object. Each octree node represents a space contained in a cubic volume called a voxel. This volume is recursively subdivided into eight sub-volumes until the specified minimum voxel size is reached. The minimum voxel size determines the resolution of the octree. Since octree is a hierarchical data structure, a tree can be reduced at any level to get a coarser spatial representation of the object. The decision on whether a given voxel is a busy object or not is made on the basis of a probabilistic approach. In the proposed paper we describe new efficient algorithm for surface reconstruction in three-dimensional space and with the help of computer simulation, the proposed method is compared with known algorithms for 3D map reconstruction.
3D fitting algorithms are very important for searching particular facial parts and key points, which can be used for recognition of facial expressions with various face deformations. Common non-rigid ICP variants are usually based on the affine point-to-point approach. In this paper, in order to apply a non-rigid ICP to facial surfaces we build a part-based model of the 3D facial surface and combine it with a new non-rigid ICP algorithm. Computer simulation results are provided to illustrate the performance of the proposed algorithm.
Images of outdoor scenes are often degraded by particles and water droplets in the atmosphere. Haze, fog, and smoke are such phenomena due to atmospheric absorption and scattering. Most image dehazing (haze removal) methods proposed for the last two decades employ image enhancing or restoration approach. Different variants of local adaptive algorithms for single image dehazing are suggested. These methods are successful when a haze-free image has higher contrast compared with that of the input hazy image. Other haze removal approaches estimate a dehazed image from the observed scene by solving an objective function whose parameters are adapted to local statistics of the hazed image inside a moving window. In this presentation we propose a new dehazing algorithm that utilizes several scene images. These images are captured in such a way to be spatially shifted relatively each other. Computer simulation results are provided to illustrate the performance of the proposed algorithm for restoration of hazed images.
The registration of two surfaces is finding a geometrical transformation of a template surface to a target surface. The transformation combines the positions of the semantically corresponding points. The transformation can be considered as warping the template onto the target. To choose the most suitable transformation from all possible warps, a registration algorithm must satisfies some constraints on the deformation. This is called regularization of the deformation field. Often use regularization based on minimizing the difference between transformations for different vertices of a surface. The variational functional consists of the several terms. One of them is the functional of the ICP (Iterative Closest Point algorithm) variational subproblem for the point-to-point metric for affine transformations. Other elements of the functional are stiffness and landmark terms. In proposed presentation we use variational functional based on the point-toplane metric for affine transformations. In addition, the use of orthogonal transformations is considered. The proposed algorithm is robust relative to bad initialization and incomplete surfaces. For noiseless and complete data, the registration is one-to-one. With the help of computer simulation, the proposed method is compared with known algorithms for the searching of optimal geometrical transformation.
The key point of the ICP algorithm is the search of either an orthogonal or affine transformations, which is the best in sense of the quadratic metric to combine two point clouds with a given correspondence between points. The point-toplane metric performs better than the point-point metric in terms of the accuracy and convergence rate. A closed-form solution to the point-to-plane case for orthogonal transformations is an open problem. In this presentation, we propose an approximation of the closed-form solution to the point-to-plane problem for orthogonal transformations.
ICP is the most commonly used algorithm in tasks of point clouds mapping, finding the transformation between clouds, building a three-dimensional map. One of the key steps of the algorithm is the removal a part of the points and the searching a correspondence of clouds. In this article, we propose a method for removing some points from the clouds. Reducing the number of points decrease an execution time of the next steps and, as a result, increase performance. The paper describes an approach based on the analysis of the geometric shapes of the scene objects. In the developed algorithm, the points lying on the boundaries of the planes intersections, the so-called edges of objects, are selected from the clouds. Then the intersection points of the found edges are checked to belong the main vertices of the objects. After that, additional vertices are excluded from the edges and, if necessary, new ones are added. The described approach is performed for both point clouds. All further steps of the ICP algorithm are performed with new clouds. In the next step, after finding the correspondence, the vertices found in the previous step are taken from the first cloud, with all the edges connected with them. For each such group it is necessary to find the corresponding group from the second cloud. The method looks for correspondence for geometrically similar parts of point clouds. After finding the intermediate transformation, the current error is calculated. The original point clouds are used for the error calculation. This approach significantly reduces the number of points participating the deciding of the ICP variational subproblem.
The problem of aligning of 3D point data is the known registration task. The most popular registration algorithm is the Iterative Closest Point (ICP) algorithm. The traditional ICP algorithm is a fast and accurate approach for rigid registration between two point clouds but it is unable to handle affine case. Recently, extension of the ICP algorithm for composition of scaling, rotation, and translation is proposed. A generalized ICP version for an arbitrary affine transformation is also suggested. In this paper, a new iterative algorithm for registration of point clouds based on the point-to-plane ICP algorithm with affine transformations is proposed. At each iteration, a closed-form solution to the affine transformation is derived. This approach allows us to get a precise solution for transformations such as rotation, translation, and scaling. With the help of computer simulation, the proposed algorithm is compared with common registration algorithms.
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