In the measurement of buildings using terrestrial laser scanners, the measurement data contains a large amount of noise that is erroneously measured due to reflections from reflective and transparent glass objects. Measurement data containing such reflection noise adversely affects the analysis that uses it. In this study, we propose an efficient method to remove these erroneous measurements. First, we estimate the glass regions that are the source of the reflection noise based on the reflection intensity of the measured point cloud. Next, we trace the path of the laser beams using the estimated glass regions and the laser scanner position to estimate the area where reflection noise can occur. Finally, we detect reflection noises based on a combination of (1) the symmetry of the noise point cloud and its source entity due to reflection by the window and (2) the geometric similarity of those point clouds, and only reflection noise is removed based on them. Experimental results showed that the reflection noise removal rate reached around 90%, confirming that only the reflection noise can be selectively removed from a measurement point cloud.

Damages of reinforced concrete buildings caused by aging or complicated environmental factors have become a worldwide problem. It is critical to accurately obtain damage information to determine the current state of the aging structure or its levels of decay. However, the inspection of multiple damages o the whole surface of a concrete structure is challenging. In this study, we present a two-stage method for damage detection of surface deflects of reinforced concrete buildings in point clouds captured by a terrestrial laser scanner using a 3D neural network and a novel cluster analysis technique. In the first stage, we divide the whole building into 3D grids, then a classification of multiple damages is performed using PointNet++ along with 3D data with color information mapped on it. In the second stage, we propose a four-step post-processing method based on cluster analysis including removal of isolated damaged clusters, dilation, and filling to develop the detection results. Experimental results show that the voxel-based recall rate reaches 0.928 and the precision rate reaches 0.753. The proposed method offers an acceptable damage detection performance on aging concrete surfaces.

We are working on a research project to evaluate the safety and structure of reinforced concrete buildings damaged by earthquakes using point cloud data acquired by a terrestrial laser scanner. We propose a framework for damage analysis as a classification problem that divides a building in point clouds into small 3D voxel grids and determines which voxels are damaged, instead of detecting the damaged parts from the point cloud of the whole building. Our framework is divided into three steps: First, the damaged building in point clouds is divided into small 3d voxel grids. Second, every voxel is fed into the deep neural network for damage classification. As a deep neural network to classify the voxel grids, we used PointNet++. Finally, the original damage map is refined by a simple cluster analysis. After post-processing, the recall reaches 0.929. That is, 92.9% of damage portions are correctly detected although the damage map still contains some FPs.

In this paper, we will discuss properties of different computational methods of the logistic map over integers. We will give four computational methods of the map in order to implement the map on a computer with limited memories, and then will evaluate average processing times and lengths of sequences generated by these methods. By these results, we will provide a constructive use of these four computational methods of the logistic map over integers for a design of the pseudorandom number generator.

Building a 3D model that can well represent the realistic is always the upmost goal in 3D landscape modeling. One purpose of our research is to propose an automatic generation method of building polygon models from point clouds created by a mobile mapping system. In this paper, as a first step, we show an extraction method of individual building walls' points from mobile mapping data.

We are investigating the logistic map over integers and its pseudorandom number generator. Focusing on differences between the logistic maps over floating-point numbers and over integers, we herein show that the integer format is a suitable representation for implementing the logistic map on computers with limited memories.

We report the implementation and evaluation of a method to automatically extract a workflow during a user's ordinary use of a personal information management system (PIM). Our method accumulates the target event and time of the user's operations such as registrations and references. In order to organize related work events, our method extracts relations between events by calculating the frequency of a user's sequential reference using a balloon-help-function and connects related work events based on the relations. Furthermore, we improve the accuracy of our organizing method by introducing a function that offers the user a list of events related to his target event in order to encourage the user to make sequential references to related work events. We have constructed a prototype PIM and tested it with 6 users. We confirm that our method could make groups of related work events and display them as connected graphs with relations shown. We discuss the efficiency of sharing this extracted workflow using sample graphs.

This paper proposes a method to automatically organize and visualize related work events as a work-project structure. This is accomplished by accumulating and analyzing the target event and time of the user's operations such as registrations and references on a personal information management system (PIM). Our processes for organization are: (1) extracting relations between events from the records of user's reference operations using balloon-help function, (2) intensifying the relations between events during interaction with the user by visualizing the relations and presenting them to the user, (3) making connections between events based on the extracted relations. Our visualization method represents the organized work events as connected graphs in which nodes and arcs indicate events and relations between events, and in which the intensity of the relation is represented with arcs of width in proportion to the intensity. Furthermore, in order to visualize a structure of collaboration among several users, our visualization method places each user's connected graph on the side planes of a three-dimensional multi-angular prism.

In order to improve the efficiency of any business, it is very important to reuse knowledge by accumulating it in a computer. We propose a method that accumulates and reuses the knowledge of work by automatically extracting it from a user's operation records during ordinary use on a personal information management (PIM) system. We assume that related work events on the PIM are input and referred to consecutively by the user. Our method of extracting the available knowledge constructs a workflow by grouping all related events and detecting milestones based on the time and order of operating target events. Usually, the reference to events is the user's view of events on the calendar, without involving any operations, so recording a target event and the time of reference is difficult. In order to record the user's reference behavior exactly, we introduce a balloon-help-based function for event reference support.

Article Free Access Share on Generating the animation of a 3D agent from explanation text Author: Ken'ichi Kakizaki Department of Computer Science and Electronics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan Department of Computer Science and Electronics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, JapanView Profile Authors Info & Claims MULTIMEDIA '98: Proceedings of the sixth ACM international conference on MultimediaSeptember 1998 Pages 139–144https://doi.org/10.1145/290747.290765Published:01 September 1998Publication History 1citation271DownloadsMetricsTotal Citations1Total Downloads271Last 12 Months20Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF