TIF-Reg: Point Cloud Registration with Transform-Invariant Features in SE(3)

Three-dimensional point cloud registration (PCReg) has a wide range of applications in computer vision, 3D reconstruction and medical fields. Although numerous advances have been achieved in the field of point cloud registration in recent years, large-scale rigid transformation is a problem that most algorithms still cannot effectively handle. To solve this problem, we propose a point cloud registration method based on learning and transform-invariant features (TIF-Reg). Our algorithm includes four modules, which are the transform-invariant feature extraction module, deep feature embedding module, corresponding point generation module and decoupled singular value decomposition (SVD) module. In the transform-invariant feature extraction module, we design TIF in SE(3) (which means the 3D rigid transformation space) which contains a triangular feature and local density feature for points. It fully exploits the transformation invariance of point clouds, making the algorithm highly robust to rigid transformation. The deep feature embedding module embeds TIF into a high-dimension space using a deep neural network, further improving the expression ability of features. The corresponding point cloud is generated using an attention mechanism in the corresponding point generation module, and the final transformation for registration is calculated in the decoupled SVD module. In an experiment, we first train and evaluate the TIF-Reg method on the ModelNet40 dataset. The results show that our method keeps the root mean squared error (RMSE) of rotation within 0.5∘ and the RMSE of translation error close to 0 m, even when the rotation is up to [−180∘, 180∘] or the translation is up to [−20 m, 20 m]. We also test the generalization of our method on the TUM3D dataset using the model trained on Modelnet40. The results show that our method’s errors are close to the experimental results on Modelnet40, which verifies the good generalization ability of our method. All experiments prove that the proposed method is superior to state-of-the-art PCReg algorithms in terms of accuracy and complexity.

The Value of Brain Resting-State Functional Magnetic Resonance Imaging on Image Registration Algorithm in Analyzing Abnormal Changes of Neuronal Activity in Patients with Type 2 Diabetes

The aim of this paper was to analyze the application value of resting-state functional magnetic resonance imaging (FMRI) parameters and rigid transformation algorithm in patients with type 2 diabetes (T2DM), which could provide a theoretical basis for the registration application of FMRI. 107 patients confirmed pathologically as T2DM and 51 community medical healthy volunteers were selected and divided into an experimental group and a control group, respectively. Besides, all the subjects were scanned with FMRI. Then, the rigid transformation-principal axis algorithm (RT-PAA), Levenberg–Marquardt iterative closest point (LMICP), and Demons algorithm were applied to magnetic resonance image registration. It was found that RT-PAA was superior to LMICP and Demons in image registration. The amplitude of low-frequency fluctuation (ALFF) values of the left middle temporal gyrus, right middle temporal gyrus, left fusiform gyrus, right inferior occipital gyrus, and left middle occipital gyrus in patients from the experimental group were lower than those of the control group P < 0.05 . The Montreal cognitive assessment (MoCA) score was extremely negatively correlated with the ALFF of the left middle temporal gyrus (r = −0.451 and P < 0.001 ) and highly positively associated with the ALFF of the right posterior cerebellar lobe (r = −0.484 and P < 0.001 ). In addition, the MoCA score of patients had a dramatically negative correlation with the ALFF of the left middle temporal gyrus (r = −0.602 and P < 0.001 ) and had a greatly positive correlation with the ALFF of the right posterior cerebellar lobe (r = −0.516 and P < 0.001 ). The results showed that RT-PAA based on rigid transformation in this study had a good registration effect on magnetic resonance images. Compared with healthy volunteers, the left middle temporal gyrus, right middle temporal gyrus, left fusiform gyrus, right inferior occipital gyrus, and left middle occipital gyrus in patients with T2DM showed abnormal neuronal changes and reduced cognitive function.

Unsupervised end-to-end Brain Tumor Magnetic Resonance Image Registration using RBCNN: Rigid Transformation, B-Spline Transformation and Convolutional Neural Network

Background: Image registration is the process of aligning two or more images in a single coordinate. Now a days, medical image registration plays a significant role in computer assisted disease diagnosis, treatment, and surgery. The different modalities available in the medical image makes medical image registration as an essential step in Computer Assisted Diagnosis(CAD), Computer-Aided Therapy (CAT) and Computer-Assisted Surgery (CAS). Problem definition: Recently many learning based methods were employed for disease detection and classification but those methods were not suitable for real time due to delayed response and need of pre alignment,labeling. Method: The proposed research constructed a deep learning model with Rigid transform and B-Spline transform for medical image registration for an automatic brain tumour finding. The proposed research consists of two steps. First steps uses Rigid transformation based Convolutional Neural Network and the second step uses B-Spline transform based Convolutional Neural Network. The model is trained and tested with 3624 MR (Magnetic Resonance) images to assess the performance. The researchers believe that MR images helps in success the treatment of brain tumour people. Result: The result of the proposed method is compared with the Rigid Convolutional Neural Network (CNN), Rigid CNN + Thin-Plat Spline (TPS), Affine CNN, Voxel morph, ADMIR (Affine and Deformable Medical Image Registration) and ANT(Advanced Normalization Tools) using DICE score, average symmetric surface distance (ASD), and Hausdorff distance. Conclusion: The RBCNN model will help the physician to automatically detect and classify the brain tumor quickly(18 Sec) and efficiently with out doing any pre-alignment and labeling.

Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover

A holistic strategy is established for automated UAV-LiDAR strip adjustment for plantation forests, based on hierarchical density-based clustering analysis of the canopy cover. The method involves three key stages: keypoint extraction, feature similarity and correspondence, and rigid transformation estimation. Initially, the HDBSCAN algorithm is used to cluster the scanned canopy cover, and the keypoints are marked using topological persistence analysis of the individual clusters. Afterward, the feature similarity is calculated by considering the linear and angular relationships between each point and the pointset centroid. The one-to-one feature correspondence is retrieved by solving the assignment problem on the similarity score function using the Kuhn–Munkres algorithm, generating a set of matching pairs. Finally, 3D rigid transformation parameters are determined by permutations over all conceivable pair combinations within the correspondences, whereas the best pair combination is that which yields the maximum count of matched points achieving distance residuals within the specified tolerance. Experimental data covering eighteen subtropical forest plots acquired from the GreenValley and Riegl UAV-LiDAR platforms in two scan modes are used to validate the method. The results are extremely promising for redwood and poplar tree species from both the Velodyne and Riegl UAV-LiDAR datasets. The minimal mean distance residuals of 31 cm and 36 cm are achieved for the coniferous and deciduous plots of the Velodyne data, respectively, whereas their corresponding values are 32 cm and 38 cm for the Riegl plots. Moreover, the method achieves both higher matching percentages and lower mean distance residuals by up to 28% and 14 cm, respectively, compared to the baseline method, except in the case of plots with extremely low tree height. Nevertheless, the mean planimetric distance residual achieved by the proposed method is lower by 13 cm.

Object tracking using motion flow projection for pan-tilt configuration

We propose a new object tracking model for two degrees of freedom mechanism. Our model uses a reverse projection from a camera plane to a world plane. Here, the model takes advantage of optic flow technique by re-projecting the flow vectors from the image space into world space. A pan-tilt (PT) mounting system is used to verify the performance of our model and maintain the tracked object within a region of interest (ROI). This system contains two servo motors to enable a webcam rotating along PT axes. The PT rotation angles are estimated based on a rigid transformation of the the optic flow vectors in which an idealized translation matrix followed by two rotational matrices around PT axes are used. Our model was tested and evaluated using different objects with different motions. The results reveal that our model can keep the target object within a certain region in the camera view.

RIDF: A ROBUST ROTATION-INVARIANT DESCRIPTOR FOR 3D POINT CLOUD REGISTRATION IN THE FREQUENCY DOMAIN

Registration of point clouds is a fundamental problem in the community of photogrammetry and 3D computer vision. Generally, point cloud registration consists of two steps: the search of correspondences and the estimation of transformation parameters. However, to find correspondences from point clouds, generating robust and discriminative features is of necessity. In this paper, we address the problem of extracting robust rotation-invariant features for fast coarse registration of point clouds under the assumption that the pairwise point clouds are transformed with rigid transformation. With a Fourier-based descriptor, point clouds represented by volumetric images can be mapped from the image to feature space. It is achieved by considering a gradient histogram as a continuous angular signal which can be well represented by the spherical harmonics. The rotation-invariance is established based on the Fourier-based analysis, in which high-frequency signals can be filtered out. This makes the extracted features robust to noises and outliers. Then, with the extracted features, pairwise correspondence can be found by the fast search. Finally, the transformation parameters can be estimated by fitting the rigid transformation model using the corresponding points and RANSAC algorithm. Experiments are conducted to prove the effectiveness of our proposed method in the task of point cloud registration. Regarding the experimental results of the point cloud registration using two TLS benchmark point cloud datasets, featuring with limited overlaps and uneven point densities and covering different urban scenes, our proposed method can achieve a fast coarse registration with rotation errors of less than 1 degree and translation errors of less than 1m.

Parallel calibration based on modified trim strategy

Purpose Partial alignment for 3 D point sets is a challenging problem for laser calibration and robot calibration due to the unbalance of data sets, especially when the overlap of data sets is low. Geometric features can promote the accuracy of alignment. However, the corresponding feature extraction methods are time consuming. The purpose of this paper is to find a framework for partial alignment by an adaptive trimmed strategy. Design/methodology/approach First, the authors propose an adaptive trimmed strategy based on point feature histograms (PFH) coding. Second, they obtain an initial transformation based on this partition, which improves the accuracy of the normal direction weighted trimmed iterative closest point (ICP) method. Third, they conduct a series of GPU parallel implementations for time efficiency. Findings The initial partition based on PFH feature improves the accuracy of the partial registration significantly. Moreover, the parallel GPU algorithms accelerate the alignment process. Research limitations/implications This study is applicable to rigid transformation so far. It could be extended to non-rigid transformation. Practical implications In practice, point set alignment for calibration is a technique widely used in the fields of aircraft assembly, industry examination, simultaneous localization and mapping and surgery navigation. Social implications Point set calibration is a building block in the field of intelligent manufacturing. Originality/value The contributions are as follows: first, the authors introduce a novel coarse alignment as an initial calibration by PFH descriptor similarity, which can be viewed as a coarse trimmed process by partitioning the data to the almost overlap part and the rest part; second, they reduce the computation time by GPU parallel coding during the acquisition of feature descriptor; finally, they use the weighted trimmed ICP method to refine the transformation.