Point cloud and BIM model registration based on genetic algorithm and ICP algorithm

With the rapid development of the construction industry, BIM technology, and 3D laser scanning technology are being used more and more widely, and there are many applications of combining BIM technology with 3D laser scanning technology, such as quality inspection, progress inspection, or digital preservation of ancient buildings. Therefore, this paper proposes a 3D point cloud and BIM model registration scheme based on genetic algorithm and ICP algorithm, firstly, the point cloud data is pre-processed by statistical denoising method for denoising and downsampling, and the BIM model data is converted to format data; then the coarse registration is performed by genetic algorithm, and the accurate registration is performed by ICP algorithm based on KD-tree, and finally, we experimentally verify the feasibility of the algorithm in this paper, and compared with the ICP algorithm, the registration efficiency and accuracy of the algorithm in this paper are greatly improved.

Определение геометро-акустических свойств сварного соединения как решение обратной коэффициентной задачи для скалярного волнового уравнения

The article is devoted to the development of ultrasonic tomographic methods of non-destructive testing of objects in order to determine the geometry of a welded joint and estimate the velocity field in it. The article offers a solution to the inverse coefficient problem for the echosignal registration scheme in the mirror-shadow mode. Numerical simulations were performed for various tomographic schemes on samples with acoustic parameters and geometry corresponding to the real experiment using an antenna array with an operating frequency of 2.25 MHz. Numerical methods have been used to optimize tomographic schemes for various applied problems. It is shown that with the help of the developed tomographic schemes, it is possible not only to detect the boundaries of the welded joint, but also to determine the velocity field inside the control object.

Inter-species cortical registration between macaques and humans using a functional network property under a spherical demons framework

Systematic evaluation of cortical differences between humans and macaques calls for inter-species registration of the cortex that matches homologous regions across species. For establishing homology across brains, structural landmarks and biological features have been used without paying sufficient attention to functional homology. The present study aimed to determine functional homology between the human and macaque cortices, defined in terms of functional network properties, by proposing an iterative functional network-based registration scheme using surface-based spherical demons. The functional connectivity matrix of resting-state functional magnetic resonance imaging (rs-fMRI) among cortical parcellations was iteratively calculated for humans and macaques. From the functional connectivity matrix, the functional network properties such as principal network components were derived to estimate a deformation field between the human and macaque cortices. The iterative registration procedure updates the parcellation map of macaques, corresponding to the human connectome project’s multimodal parcellation atlas, which was used to derive the macaque’s functional connectivity matrix. To test the plausibility of the functional network-based registration, we compared cortical registration using structural versus functional features in terms of cortical regional areal change. We also evaluated the interhemispheric asymmetry of regional area and its inter-subject variability in humans and macaques as an indirect validation of the proposed method. Higher inter-subject variability and interhemispheric asymmetry were found in functional homology than in structural homology, and the assessed asymmetry and variations were higher in humans than in macaques. The results emphasize the significance of functional network-based cortical registration across individuals within a species and across species.

CT-MRI Dual Information Registration for the Diagnosis of Liver Cancer: A Pilot Study Using Point-based Registration

Background: Early diagnosis of liver cancer may increase life expectancy. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) play a vital role in diagnosing liver cancer. Together, both modalities offer significant individual and specific diagnosis data to physicians; however, they lack the integration of both types of information. To address this concern, a registration process has to be utilized for the purpose, as multimodal details are crucial in providing the physician with complete information. Objective: The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities. Methods: In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel (R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively. Results: The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution. Conclusion: The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.

Improvement and Performance Evaluation When Implementing a Distance-Based Registration

An efficient location registration scheme is essential to continuously accommodate the increasing number of mobile subscribers and to offer a variety of multimedia services with good quality. The objective of this study was to analyze the optimal size for the location area of a distance-based registration (DBR) scheme by varying the number of location areas on a cell-by-cell basis, not on a ring-by-ring basis. Using our proposed cell-by-cell distance-based registration scheme with a random walk mobility model, a variety of circumstances were analyzed to obtain the optimal number of cells for location area for minimizing the total signaling cost on radio channels. Analysis results showed that the optimal number of cells for location area was between 4 and 7 in most cases. Our cell-by-cell distance-based location registration scheme had less signaling cost than an optimal ring-by-ring distance-based location registration scheme with an optimal distance threshold of 2 (the optimal number of cells for location area was 7). Therefore, when DBR is adopted, it must be implemented with an LA increasing on a cell-by-cell basis to achieve optimal performance.

Unsupervised Multistep Deformable Registration of Remote Sensing Imagery Based on Deep Learning

Image registration is among the most popular and important problems of remote sensing. In this paper we propose a fully unsupervised, deep learning based multistep deformable registration scheme for aligning pairs of satellite imagery. The presented method is based on the expression power of deep fully convolutional networks, regressing directly the spatial gradients of the deformation and employing a 2D transformer layer to efficiently warp one image to the other, in an end-to-end fashion. The displacements are calculated with an iterative way, utilizing different time steps to refine and regress them. Our formulation can be integrated into any kind of fully convolutional architecture, providing at the same time fast inference performances. The developed methodology has been evaluated in two different datasets depicting urban and periurban areas; i.e., the very high-resolution dataset of the East Prefecture of Attica, Greece, as well as the high resolution ISPRS Ikonos dataset. Quantitative and qualitative results demonstrated the high potentials of our method.

Systematic Review on Adverse Effect of Pesticide on Top Ten Importers of African Countries

As agricultural production in African countries intensifies; pesticide utilization becomes more widespread and the users are extremely exposed to these pesticides due to lack of pesticide registration scheme; importing highly toxic pesticides; no national plan for pesticide residue; involvement of children and women. The purpose of this systematic review was to review adverse effect of pesticide among top ten imported African countries. In this review, top ten importers African countries were selected based of imported amount for ten years were considered from imported period of 2002 to 2017. The articles were searched from PUBMED, GOOGLE SCHOLAR, and MEDLINE and EMBASE engines. The first leading three continents for pesticides exported were European (48.2%), Asian (33.7%) and North America (12.7%), while the countries were China (14.3%), Germany (11.8%) and United States (11.5%) at the end of 2017. The first three leading importer of African countries were South Africa shared (25.7%), Nigeria (15.8%) and Ghana (14.5%). The three major imported pesticides were Fungicides, herbicides and insecticides. In this review, Ethiopia (827), Kenya (801), and Morocco (542) are the main importers of pesticides until end of 2017. The review also found that farmers were faced with endocrine disruption, carcinogenicity, mutagenicity, teratogenicity, cardiovascular, dermatitis and birth defects. The main associated factor for these problems were low awareness, improper handling of pesticide, and lack of training, and careless disposal of empty pesticides containers. The study concluded that more than one billion US$ of pesticides sales was carried out into ten African countries. The farmers within these country were faced different health problems due to different determinant factors. Proper training and education should be advised for farmers Keywords: Adverse effect, African countries, Import, Pesticide

PERFORMANCE ANALYSIS OF SELECTED FEATURE DESCRIPTORS USED FOR AUTOMATIC IMAGE REGISTRATION

Automatic detection and extraction of corresponding features is very crucial in the development of an automatic image registration algorithm. Different feature descriptors have been developed and implemented in image registration and other disciplines. These descriptors affect the speed of feature extraction and the measure of extracted conjugate features, which affects the processing speed and overall accuracy of the registration scheme. This article is aimed at reviewing the performance of most-widely implemented feature descriptors in an automatic image registration scheme. Ten (10) descriptors were selected and analysed under seven (7) conditions viz: Invariance to rotation, scale and zoom, their robustness, repeatability, localization and efficiency using UAV acquired images. The analysis shows that though four (4) descriptors performed better than the other Six (6), no single feature descriptor can be affirmed to be the best, as different descriptors perform differently under different conditions. The Modified Harris and Stephen Corner Detector (MHCD) proved to be invariant to scale and zoom while it is excellent in robustness, repeatability, localization and efficiency, but it is variant to rotation. Also, the Scale Invariant feature Transform (SIFT), Speeded Up Robust Features (SURF) and the Maximally Stable Extremal Region (MSER) algorithms proved to be invariant to scale, zoom and rotation, and very good in terms of repeatability, localization and efficiency, though MSER proved to be not as robust as SIFT and SURF. The implication of the findings of this research is that the choice of feature descriptors must be informed by the imaging conditions of the image registration analysts.