scholarly journals Quintuple Local Coordinate Images for Local Shape Description

2020 ◽  
Vol 86 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Wuyong Tao ◽  
Xianghong Hua ◽  
Ruisheng Wang ◽  
Dong Xu
Keyword(s):  
Spatial Information ◽  
Density Variation ◽  
Feature Representation ◽  
Data Sets ◽  
Local Shape ◽  
Noise Point ◽  
Point Density ◽  
Local Reference ◽  
Local Shape Descriptors ◽  
Partial Overlap

Owing to poor descriptiveness, weak robustness, and high computation complexity of local shape descriptors (<small>LSDs</small>), point-cloud registration in the case of partial overlap and object recognition in a cluttered environment are still challeng- ing tasks. For this purpose, an <small>LSD</small> is developed in this article by proposing a new local reference frame (<small>LRF</small>) method and designing a novel feature representation. In the <small>LRF</small> method, two weighting methods are applied to obtain robustness to noise, point-density variation, and incomplete shape. Additionally, a vector representation is calculated to disambiguate the sign of the x-axis. The feature representation encodes the local information by generating the local coordinate images from five views. Thus, more geometric and spatial information is included in the descriptor. Finally, the performance of the <small>LRF</small> method and the <small>LSD</small> is evaluated on several popular data sets. The experimental results demonstrate well that the <small>LRF</small> is robust to noise, point-density variation, and incomplete shape, and the <small>LSD</small> holds strong robustness, superior descriptiveness, and high computational efficiency.

scholarly journals A Comparative Study of Weighting Methods for Local Reference Frame

2020 ◽  
Vol 10 (9) ◽  
pp. 3223
Author(s):  
Wuyong Tao ◽  
Xianghong Hua ◽  
Kegen Yu ◽  
Ruisheng Wang ◽  
Xiaoxing He
Keyword(s):  
Reference Frame ◽  
Research Area ◽  
Density Variation ◽  
3D Registration ◽  
Local Reference Frame ◽  
Local Shape ◽  
Advantages And Disadvantages ◽  
Local Reference ◽  
Local Shape Descriptors ◽  
Weighting Methods

In the field of photogrammetric engineering, computer vision, and graphics, local shape description is an active research area. A wide variety of local shape descriptors (LSDs) have been designed for different applications, such as shape retrieval, object recognition, and 3D registration. The local reference frame (LRF) is an important component of the LSD. Its repeatability and robustness directly influence the descriptiveness and robustness of the LSD. Several weighting methods have been proposed to improve the repeatability and robustness of the LRF. However, no comprehensive comparison has been implemented to evaluate their performance under different data modalities and nuisances. In this paper, we focus on the comparison of weighting methods by using six datasets with different data modalities and application contexts. We evaluate the repeatability of the LRF under different nuisances, including occlusion, clutter, partial overlap, varying support radii, Gaussian noise, shot noise, point density variation, and keypoint localization error. Through the experiments, the traits, advantages, and disadvantages of weighting methods are summarized.

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals Mapping malaria by sharing spatial information between incidence and prevalence data sets

2021 ◽  
Author(s):  
Tim C. D. Lucas ◽  
Anita K. Nandi ◽  
Elisabeth G. Chestnutt ◽  
Katherine A. Twohig ◽  
Suzanne H. Keddie ◽  
...  
Keyword(s):  
Spatial Information ◽  
Data Sets ◽  
Prevalence Data

scholarly journals Collecting public RGB-D datasets for human daily activity recognition

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141770907 ◽  
Author(s):  
Hanbo Wu ◽  
Xin Ma ◽  
Zhimeng Zhang ◽  
Haibo Wang ◽  
Yibin Li
Keyword(s):  
Activity Recognition ◽  
Daily Activity ◽  
Visual Cues ◽  
Large Scale ◽  
Hot Spot ◽  
Feature Representation ◽  
Data Sets ◽  
Activity Data ◽  
Data Set ◽  
Depth Motion Maps

Human daily activity recognition has been a hot spot in the field of computer vision for many decades. Despite best efforts, activity recognition in naturally uncontrolled settings remains a challenging problem. Recently, by being able to perceive depth and visual cues simultaneously, RGB-D cameras greatly boost the performance of activity recognition. However, due to some practical difficulties, the publicly available RGB-D data sets are not sufficiently large for benchmarking when considering the diversity of their activities, subjects, and background. This severely affects the applicability of complicated learning-based recognition approaches. To address the issue, this article provides a large-scale RGB-D activity data set by merging five public RGB-D data sets that differ from each other on many aspects such as length of actions, nationality of subjects, or camera angles. This data set comprises 4528 samples depicting 7 action categories (up to 46 subcategories) performed by 74 subjects. To verify the challengeness of the data set, three feature representation methods are evaluated, which are depth motion maps, spatiotemporal depth cuboid similarity feature, and curvature space scale. Results show that the merged large-scale data set is more realistic and challenging and therefore more suitable for benchmarking.

Approximate Matching Between XML Documents and Schemas with Applications in XML Classification and Clustering

2011 ◽  
pp. 99-124 ◽  
Author(s):  
Guangming Xing
Keyword(s):  
Structural Information ◽  
Experimental Studies ◽  
Feature Representation ◽  
Document Management ◽  
Data Sets ◽  
Structural Classification ◽  
Approximate Matching ◽  
Xml Documents ◽  
Efficiency And Effectiveness ◽  
The Cost

Classification/clustering of XML documents based on their structural information is important for many tasks related with document management. In this chapter, we present a suite of algorithms to compute the cost for approximate matching between XML documents and schemas. A framework for classifying/clustering XML documents by structure is then presented based on the computation of distances between XML documents and schemas. The backbone of the framework is the feature representation using a vector of the distances. Experimental studies were conducted on various XML data sets, suggesting the efficiency and effectiveness of our approach as a solution for structural classification/clustering of XML documents.

From CAD to GIS to the Geoweb: A Natural Evolution

2010 ◽  
Author(s):  
Steve Adam
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Oil And Gas ◽  
Spatial Information ◽  
Digital Data ◽  
Computer Hardware ◽  
Disruptive Technology ◽  
Data Sets ◽  
Spatial Data Sets ◽  
Pipeline Design

Computer hardware and software have played a significant role in supporting the design and maintenance of pipeline systems. CAD systems allowed designers and drafters to compile drawings and make edits at a pace unmatched by manual pen drawings. Although CAD continues to provide the environment for a lot of pipeline design, Geographic Information Systems (GIS) are also innovating pipeline design through routines such as automated alignment sheet generation. What we have seen over the past two or three decades is an evolution in how we manage the data and information required for decision making in pipeline design and system operation. CAD provided designers and engineers a rapid electronic method for capturing information in a drawing, editing it, and sharing it. As the amount of digital data available to users grows rapidly, CAD has been unable to adequately exploit data’s abundance and managing change in a CAD environment is cumbersome. GIS and spatial data management have proven to be the next evolution in situations where engineering, integrity, environmental, and other spatial data sets dominate the information required for design and operational decision making. It is conceivable that GIS too will crumble under the weight of its own data usage as centralized databases become larger and larger. The Geoweb is likely to emerge as the geospatial world’s evolution. The Geoweb implies the merging of spatial information with the abstract information that currently dominates the Internet. This paper and presentation will discuss this fascinating innovation, it’s force as a disruptive technology, and oil and gas applications.

SAR images matching based on local shape descriptors

2009 ◽  
Author(s):  
J. Lu ◽  
B. Wang ◽  
H.M. Gao ◽  
Z.Q. Zhou
Keyword(s):  
Shape Descriptors ◽  
Sar Images ◽  
Local Shape ◽  
Local Shape Descriptors

scholarly journals Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm

2020 ◽  
Vol 12 (23) ◽  
pp. 4007
Author(s):  
Kasra Rafiezadeh Shahi ◽  
Pedram Ghamisi ◽  
Behnood Rasti ◽  
Robert Jackisch ◽  
Paul Scheunders ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Data Sets ◽  
Data Types ◽  
Data Set ◽  
Multiple Data Sets ◽  
Imaging Sensors

The increasing amount of information acquired by imaging sensors in Earth Sciences results in the availability of a multitude of complementary data (e.g., spectral, spatial, elevation) for monitoring of the Earth’s surface. Many studies were devoted to investigating the usage of multi-sensor data sets in the performance of supervised learning-based approaches at various tasks (i.e., classification and regression) while unsupervised learning-based approaches have received less attention. In this paper, we propose a new approach to fuse multiple data sets from imaging sensors using a multi-sensor sparse-based clustering algorithm (Multi-SSC). A technique for the extraction of spatial features (i.e., morphological profiles (MPs) and invariant attribute profiles (IAPs)) is applied to high spatial-resolution data to derive the spatial and contextual information. This information is then fused with spectrally rich data such as multi- or hyperspectral data. In order to fuse multi-sensor data sets a hierarchical sparse subspace clustering approach is employed. More specifically, a lasso-based binary algorithm is used to fuse the spectral and spatial information prior to automatic clustering. The proposed framework ensures that the generated clustering map is smooth and preserves the spatial structures of the scene. In order to evaluate the generalization capability of the proposed approach, we investigate its performance not only on diverse scenes but also on different sensors and data types. The first two data sets are geological data sets, which consist of hyperspectral and RGB data. The third data set is the well-known benchmark Trento data set, including hyperspectral and LiDAR data. Experimental results indicate that this novel multi-sensor clustering algorithm can provide an accurate clustering map compared to the state-of-the-art sparse subspace-based clustering algorithms.

IMPACT ANALYSIS OF THE INSPIRE DIRECTIVE ON LAND-USE PLANNING PUBLICATION AND DEVELOPMENT

2017 ◽  
Vol 165 (45) ◽  
pp. 76-85
Author(s):  
WALDEMAR IZDEBSKI ◽  
ZBIGNIEW MALINOWSKI
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Spatial Data ◽  
Local Governments ◽  
Impact Analysis ◽  
Spatial Information ◽  
Technological Development ◽  
Data Sets ◽  
Gis Technology ◽  
Spatial Data Sets

The INSPIRE Directive went into force in May 2007 and it resulted in changing the way of thinking about spatial data in local government. Transposition of the Directive on Polish legislation is the Law on spatial information infrastructure from 4 March 2010., which indicates the need for computerization of spatial data sets (including land-use planning). This act resulted in an intensification of thinking about the computerization of spatial data, but, according to the authors, the needs and aspirations of the digital land-use planning crystallized already before the INSPIRE Directive and were the result of technological development and increasing the awareness of users. The authors analyze the current state of land-use planning data computerization in local governments. The analysis was conducted on a group of more than 1,700 local governments, which are users of spatial data management (GIS) technology eGmina.

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