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In recent years, it has become important in forestry and forest research to accurately calculate tree stem traits from point clouds captured using the terrestrial laser scanner. However, it is difficult to accurately align a large number of trees in a forest over a large area. Therefore, the reliability of traits calculated from point clouds has been problematic. In this paper, we propose a method to automatically correct misaligned point-clouds and calculate accurate tree stem traits. In our method, a different registration matrix is calculated for each tree to correct the misalignment. When the target tree is specified, point-clouds measured in the vicinity of the target tree and points of the neighbor trees are selected for multi-view registration, and a registration matrix suitable for the target tree is calculated. The experimental results show that the proposed method is effective in correcting misalignment and precisely calculating tree stem traits.

Mobile Mapping System (MMS) equipped with a high-density LiDAR scanner is widely used for mapping. Various automatic mapping methods have been proposed for point clouds measured by the high-density LiDAR scanner on the MMS. However, careful parameter tuning is often required according to measurement conditions. In this paper, we propose a method to generate normalized scanlines from point clouds captured using the MMS. Normalized scanlines are useful to avoid parameter tuning depending on the measurement conditions. In order to evaluate the validity of our method, we extracted road boundaries with the same parameters from two point clouds measured under different conditions. In our evaluation, our method could detect almost the same road boundaries from the two point clouds using the same parameter settings.

The mobile mapping system (MMS) can acquire dense point-clouds of roads and roadside features. Roads are often separated into roadways and walkways in many urban areas. Since guardrails are installed to separate roadways and sidewalks, it is important to detect guardrails from point-clouds and reconstruct their 3D models for 3D street maps. Since there are a large variety of designs for guardrails in Japan, flexible methods are required for detection and reconstruction of guardrails. In this paper, we propose a new method for extracting guardrails from point-clouds, and reconstructing their 3D models. Since the MMS captures point-clouds and camera images synchronously, guardrails are detected using both point-clouds and images. In our method, point-clouds are segmented into small segments, and corresponding images are cropped from camera images. Then cropped images are classified into two classes of guardrails and others using the convolutional neural network. When guardrail points are obtained, 3D models of guardrails are reconstructed. However, point-clouds of guardrails are too sparse to reconstruct 3D shapes when guardrails consist of thin pipes. Since the same unit shape repeatedly appears in a guardrail, we create dense point-clouds by superimposing points of unit shapes. Then we reconstruct 3D shapes of pipes, beams, and poles of guardrails. In our evaluation using point-clouds in urban areas, our method could achieve good results of extraction and shape reconstruction of guardrails.

Efficient management of roadside trees for local governments is important. Mobile Mapping System (MMS) equipped with a high-density LiDAR scanner has the possibility to be applied to estimate DBH of roadside trees using point clouds. In this study, we propose a method for detecting roadside trees and estimating their DBHs automatically from MMS point clouds. In our method, point clouds captured using the MMS are mapped on a 2D image plane, and they are converted into a wireframe model by connecting adjacent points. Then, geometric features are calculated for each point in the wireframe model. Tree points are detected using a machine learning technique. The DBH of each tree is calculated using vertically aligned circles extracted from the wireframe model. Our method allows robustly calculating the DBH even if there is a hump at breast height. We evaluated our method using actual MMS data measured in an urban area in Tokyo. Our method achieved a high extraction performance of 100 percent of precision and 95.1 percent of recall for 102 roadside trees. The average accuracy of the DBH was 2.0 cm. These results indicate that our method is useful for the efficient management of roadside trees.

In residential areas, maintenance of power and communication lines is an important issue. In recent years, the frequency and power of typhoons and storms have significantly increased. If utility poles incline due to strong winds, slack cables may contact with large vehicles. For automatically detecting loose road-crossing cables in wide areas, the MMS is very promising. However, when roadcrossing cables are measured using the MMS, large portions of points on cables may be lost, because the directions of laser beams are nearly parallel to the directions of road-crossing cables, and therefore, the laser beams cross road-crossing cables only a small number of times. In this paper, we propose a new method for reconstructing cables crossing roads. In our method, road-crossing cables are reconstructed using both point clouds and camera images. While point clouds of road-crossing cables may be partly missing, their camera images can be captured with little occlusion. Missing portions are recovered using lines extracted from camera images. First, points of each cable are extracted from a point cloud, and the 3D vertical plane on which the cable exists are calculated. From camera images, 2D line segments are extracted as candidates of cable lines. 2D line segments are projected onto the 3D vertical plane using the pinhole camera model. Finally, 3D cable lines are reconstructed on the 3D vertical plane from the merged points. In our experiments, road-crossing cables could be sufficiently extracted using our method.

Laser scanners on a vehicle-based mobile mapping system can capture 3D point-clouds of roads and roadside objects. Since roadside objects have to be maintained periodically, their 3D models are useful for planning maintenance tasks. In our previous work, we proposed a method for detecting cylindrical poles and planar plates in a point-cloud. However, it is often required to further classify pole-like objects into utility poles, streetlights, traffic signals and signs, which are managed by different organizations. In addition, our previous method may fail to extract low pole-like objects, which are often observed in urban residential areas. In this paper, we propose new methods for extracting and classifying pole-like objects. In our method, we robustly extract a wide variety of poles by converting point-clouds into wireframe models and calculating cross-sections between wireframe models and horizontal cutting planes. For classifying pole-like objects, we subdivide a pole-like object into five subsets by extracting poles and planes, and calculate feature values of each subset. Then we apply a supervised machine learning method using feature variables of subsets. In our experiments, our method could achieve excellent results for detection and classification of pole-like objects.

MMSs allow us to obtain detailed 3D information around roads. Especially, LiDAR point clouds can be used for map generation and infrastructure management. For practical uses, however, it is necessary to add labels to a part of the points since various objects can be included in the point clouds. Existing automatic classification methods are not completely error-free, and may incorrectly classify objects. Therefore, even though automatic methods are applied to the point clouds, operators have to verify the labels. While operators classify the point clouds manually, selecting 3D points tasks in 3D views are difficult. In this paper, we propose a new point-cloud image based on the trajectories of MMSs. We call our point-cloud image trajectory-based point-cloud image. Although the image is distorted because it is generated based on rotation angles of laser scanners, we confirmed that most objects can be recognized from point-cloud images by checking main road facilities. We evaluated how efficient the annotation can be done using our method, and the results show that operators could add annotations to point-cloud images more efficiently.

Background: Some studies have suggested that statins, which have cholesterol-lowering and anti-inflammatory properties, may have antitumor effects. Effects of statins on inflammatory breast cancer (IBC) have never been studied. Methods: We reviewed 723 patients diagnosed with primary IBC in 1995–2011 and treated at The University of Texas MD Anderson Cancer Center. Statin users were defined as being on statins at the initial evaluation. Based on Ahern et al's statin classification (JNCI, 2011), clinical outcomes were compared by statin use and type (weakly lipophilic to hydrophilic (H-statin) vs lipophilic statins (L-statin)). We used the Kaplan–Meier method to estimate the median progression-free survival (PFS), overall survival (OS) and disease-specific survival (DSS), and a Cox proportional hazards regression model to test the statistical significance of potential prognostic factors. Results: In the multivariable Cox model, H-statins were associated with significantly improved PFS compared with no statin (hazard ratio=0.49; 95% confidence interval=0.28–0.84; P <0.01); OS and DSS P -values were 0.80 and 0.85, respectively. For L-statins vs no statin, P -values for PFS, DSS, and OS were 0.81, 0.4, and 0.74, respectively. Conclusion: H-statins were associated with significantly improved PFS. A prospective randomised study evaluating the survival benefits of statins in primary IBC is warranted.

A mobile mapping system is effective for capturing dense point-clouds of roads and roadside objects．Point-clouds of urban areas, residential areas, and arterial roads are useful for maintenance of infrastructure, map creation, and automatic driving. However, the data size of point-clouds measured in large areas is enormously large. A large storage capacity is required to store such point-clouds, and heavy loads will be taken on network if point-clouds are transferred through the network. Therefore, it is desirable to reduce data sizes of point-clouds without deterioration of quality. In this research, we propose a novel point-cloud compression method for vehicle-based mobile mapping systems. In our compression method, point-clouds are mapped onto 2D pixels using GPS time and the parameters of the laser scanner. Then, the images are encoded in the Portable Networking Graphics (PNG) format and compressed using the PNG algorithm. In our experiments, our method could efficiently compress point-clouds without deteriorating the quality.

Woody biomass is an important parameter in forestry and forest research. In order to estimate of woody biomass, it is important to precisely and efficiently calculate section areas and volumes of tree stems in the forest. In this paper, we propose a method for calculating the cross-sectional area and the stem volume of trees from point clouds captured using the terrestrial laser scanner. In our method, each point cloud is converted into a wireframe model, and cross-section points are calculated as intersection between the wireframe and the horizontal planes placed at equal intervals. Cross-sectional shapes on each horizontal plane are approximated as n-sided polygons and refined using the subdivision scheme. The section areas and stem volumes are calculated using the subdivision curves of stem contours. In our evaluation, our method could calculate section areas and stem volumes of trees with sufficient accuracy in practical use.