Classification and information structure of the Terrestrial Laser Scanner: methodology for analyzing the registered data of Vila Vella, historic center of Tossa de Mar

This paper presents a methodology for an architectural survey, based on the Terrestrial Laser Scanning technology TLS, not as a simple measurement and representation work, but with the purpose understanding the projects being studied, starting from the analysis, as a process of distinction and separation of the parts of a whole, in order to know their principles or elements. As a case study we start from the Vila Vella recording, conducted by the City’s Virtual Modeling Laboratory in 2008, being taken up from the start, in relation to the registration, georeferencing, filtering and handling. Aimed at a later stage of decomposition and composition of data, in terms of floor plan and facades, using semiautomatic classification techniques, for the detection of vegetation as well as the relationship of the planes of the surfaces, leading to reorganize the information from 3D data to 2D and 2.5D, considering information management, as well as the characteristics of the case study presented, in the development of methods for the construction and exploitation of new databases, to be exploited by the Geographic Information Systems and Remote Sensing.Peer Reviewe

Classification and information structure of the Terrestrial Laser Scanner: methodology for analyzing the registered data of Vila Vella, historic center of Tossa de Mar

This paper presents a methodology for an architectural survey, based on the Terrestrial Laser Scanning technology TLS, not as a simple measurement and representation work, but with the purpose understanding the projects being studied, starting from the analysis, as a process of distinction and separation of the parts of a whole, in order to know their principles or elements. As a case study we start from the Vila Vella recording, conducted by the City’s Virtual Modeling Laboratory in 2008, being taken up from the start, in relation to the registration, georeferencing, filtering and handling. Aimed at a later stage of decomposition and composition of data, in terms of floor plan and facades, using semiautomatic classification techniques, for the detection of vegetation as well as the relationship of the planes of the surfaces, leading to reorganize the information from 3D data to 2D and 2.5D, considering information management, as well as the characteristics of the case study presented, in the development of methods for the construction and exploitation of new databases, to be exploited by the Geographic Information Systems and Remote Sensing.Peer Reviewe

Towards key-frame extraction methods for 3D video: a review

The increasing rate of creation and use of 3D video content leads to a pressing need for methods capable of lowering the cost of 3D video searching, browsing and indexing operations, with improved content selection performance. Video summarisation methods specifically tailored for 3D video content fulfil these requirements. This paper presents a review of the state-of-the-art of a crucial component of 3D video summarisation algorithms: the key-frame extraction methods. The methods reviewed cover 3D video key-frame extraction as well as shot boundary detection methods specific for use in 3D video. The performance metrics used to evaluate the key-frame extraction methods and the summaries derived from those key-frames are presented and discussed. The applications of these methods are also presented and discussed, followed by an exposition about current research challenges on 3D video summarisation methods

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

A Novel Bridge Information Modeling (BrIM) Based Framework for Bridge Inspections

Bridges are critical components of civil infrastructure. According to the National Bridge Inventory (NBI), there are more than ten thousand structurally deficient bridges in the United States. It becomes critical for the authorities to maintain their serviceability and reliability in order to keep the transportation system operational. Current bridge condition assessment practices are mainly based on visual inspections carried out by technical experts, which is subjective as observations and opinions may vary from one individual to another, and is expensive and prone to human errors. The main focus of this study is to help improve current inspection practices by implementing image processing algorithms to detect concrete surface cracks and integrate the results into a bridge information modeling (BrIM) based framework. Integrating crack detection algorithm results with BrIM will allow users to view and explore cracks and their properties linked to a three dimensional (3D) model of the inspected bridge component. The proposed methodology processes 2D images by adjusting pixel parameters of gray scale images and detects cracks with their dimensional aspects. It implements existing crack detection algorithms, a scaling tool to automatically measure crack dimensions, and includes a framework to integrate crack detection results with BrIM for inspecting bridges in a more efficient manner. This will enable more effective repairs and maintenance operations, saving a considerable amount of effort, time, and money

Development and Implementation of Image-based Algorithms for Measurement of Deformations in Material Testing

This paper presents the development and implementation of three image-based methods used to detect and measure the displacements of a vast number of points in the case of laboratory testing on construction materials. Starting from the needs of structural engineers, three ad hoc tools for crack measurement in fibre-reinforced specimens and 2D or 3D deformation analysis through digital images were implemented and tested. These tools make use of advanced image processing algorithms and can integrate or even substitute some traditional sensors employed today in most laboratories. In addition, the automation provided by the implemented software, the limited cost of the instruments and the possibility to operate with an indefinite number of points offer new and more extensive analysis in the field of material testing. Several comparisons with other traditional sensors widely adopted inside most laboratories were carried out in order to demonstrate the accuracy of the implemented software. Implementation details, simulations and real applications are reported and discussed in this paper

Truck Trailer Classification Using Side-Fire Light Detection And Ranging (LiDAR) Data

Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources. Nevertheless, using current emerging technologies such as Light Detection and Ranging (LiDAR) data, it may be possible to predict commodity type from truck body types or trailers. For example, refrigerated trailers are commonly used to transport perishable produce and meat products, tank trailers are for fuel and other liquid products, and specialized trailers carry livestock. The main goal of this research is to develop methods using side-fired LiDAR data to distinguish between specific types of truck trailers beyond what is generally possible with traditional vehicle classification sensors (e.g., piezoelectric sensors and inductive loop detectors). A multi-array LiDAR sensor enables the construction of 3D-profiles of vehicles since it measures the distance to the object reflecting its emitted light. In this research 16-beam LiDAR sensor data are processed to estimate vehicle speed and extract useful information and features to classify semi-trailer trucks hauling ten different types of trailers: a reefer and non-reefer dry van, 20 ft and 40 ft intermodal containers, a 40 ft reefer intermodal container, platforms, tanks, car transporters, open-top van/dump and aggregated other types (i.e., livestock, logging, etc.). In addition to truck-trailer classification, methods are developed to detect empty and loaded platform semi-trailers. K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost), and Support Vector Machines (SVM) supervised machine learning algorithms are implemented on the field data collected on a freeway segment that includes over seven-thousand trucks. The results show that different trailer body types and empty and loaded platform semi-trailers can be classified with a very high level of accuracy ranging from 85% to 98% and 99%, respectively. To enhance the accuracy by which multiple LiDAR frames belonging to the same truck are merged, a new algorithm is developed to estimate the speed while the truck is within the field of view of the sensor. This algorithm is based on tracking tires and utilizes line detection concepts from image processing. The proposed algorithm improves the results and allows creating more accurate 2D and 3D truck profiles as documented in this thesis