Biometric Standards Survey

This document presents a quick survey on the most important standards regarding biometric technologies, concentrating mainly in those concerning the smartcard environment

COSPO/CENDI Industry Day Conference

The conference's objective was to provide a forum where government information managers and industry information technology experts could have an open exchange and discuss their respective needs and compare them to the available, or soon to be available, solutions. Technical summaries and points of contact are provided for the following sessions: secure products, protocols, and encryption; information providers; electronic document management and publishing; information indexing, discovery, and retrieval (IIDR); automated language translators; IIDR - natural language capabilities; IIDR - advanced technologies; IIDR - distributed heterogeneous and large database support; and communications - speed, bandwidth, and wireless

Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.Comment: 28 pages, 6 figures, Book Chapter from "Encyclopedia of E-Health and Telemedicine

AXMEDIS 2008

The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage

스캔 도면을 활용한 이동약자용 실내 그래프 데이터베이스 구축

학위논문(박사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2021.8. 박슬아.사람들의 실내 활동이 다양해지면서 건물의 규모가 커지고 구조가 복잡해지고 있다. 이러한 실내 환경의 변화는 교통약자의 이동성 보장에 대한 사회적 관심을 증가시켰으며, 교통약자 맞춤형 실내 라우팅 서비스에 대한 수요 또한 증가시켰다. 특히 많은 이동 제약을 가지는 이동약자 대상 서비스의 경우에는, 최적 경로를 계획하는 과정에서 개인의 선호나 경험이 반영된 개인화된 서비스로 범위가 확장되고 있다. 이러한 배경에서, 스키마가 유연하고 데이터의 가공 및 처리가 효율적인 데이터베이스의 구축이 필요하다. 본 연구에서는 스캔한 도면 이미지를 활용한 이동약자용 실내 그래프데이터베이스 구축 기법을 제안하였다. 먼저, 국내외 실내 공간 관련 표준 및 설계 기준들의 검토를 통해 이동약자의 통행과 관련된 실내 공간 및 객체, 영향 요인들을 도출하여 개념적 데이터 모델을 설계하였다. 또한, 실내의 각 공간과 시설물의 기하정보와 위상정보를 기반으로 이동약자의 접근성 및 통행 가능성을 정량화하기 위한 접근성 지수를 설계하였다. 다음으로, 스캔 도면을 입력하여 이동약자용 실내 그래프 데이터베이스 구축을 위한 프로세스를 제안하였다. 제안한 프로세스는 전이학습 기반 접근 방식을 통해 스캔 도면에서 공간의 구조 정보를 추출하고, 토폴로지 추출 및 접근성 평가를 통해 이동약자용 네트워크 모델을 생성하며, 생성한 네트워크 모델을 그래프 데이터베이스로 자동 변환하는 과정을 포함한다. 구체적으로, 제안 프로세스는 수정된 ResNet 기반의 모델을 새롭게 라벨링한 도면으로 미세 조정하여 사용함으로써 실내 구조맵을 생성한다. 이후 추출된 객체들의 공간 관계를 기반으로 각 공간을 노드와 링크로 표현한 실내 네트워크 모델을 구축한다. 각 공간의 접근성 정보는 제안된 접근성 지수와 임계값을 사용하여 생성된 후 데이터베이스에 저장되어, 이동약자를 위한 접근 가능한 그래프 추출에 활용될 수 있다. 본 연구에서는 제안한 기법을 서울대학교 도면 데이터 셋에 적용하여 이동약자용 실내 그래프 데이터베이스를 구축하고 평가하였다. 구축한 실내 그래프 데이터베이스를 활용하여 다층 경로 계획과 실내외 연계 경로 계획의 2가지 시나리오에 따라 최적 경로를 도출하였다. 그 결과, 일반 보행자의 최적 경로와 비교하여 이동약자용 최적 경로는 가까운 계단이 아닌 엘리베이터를 통한 수직 이동을 포함하였을 뿐만 아니라 접근 불가능한 공간을 회피하도록 도출되었다. 즉, 제안한 기법을 통해 이동약자 측면에서 통행 장애 정보를 포함하여 실내 환경을 적절하게 묘사하는 데이터베이스의 구축이 가능함을 확인할 수 있었다. 또한, 출입로로 명명된 관계 생성만으로 스케일이나 좌표 변환 없이 실내외 연계 경로 계획이 가능하였는데, 이는 독립적인 데이터 간 연계 사용에 적합한 그래프 데이터베이스의 특성을 반영한 결과로 판단할 수 있다. 본 연구의 주요 기여는 스캔한 도면을 사용하여 이동약자용 실내 그래프 데이터베이스를 구축하기 위한 프로세스를 개발한 것이다. 구체적으로, 이동약자의 이동에 초점을 두고 설계한 데이터 모델을 기반으로 한 데이터베이스 구축이 가능하므로 이동약자용 실내 길안내 서비스에 활용될 수 있다. 또한, 토폴로지 구축 및 그래프 데이터베이스로의 변환을 위한 하위 프로시져를 개발하였으며, 제안 프로세스는 해당 프로시져들로 구성되어 도면 입력을 통해 이동약자용 실내 그래프 데이터베이스 구축을 가능하게 한다. 해당 하위 프로시져들은 자동으로 수행될 수 있어 데이터베이스 구축 시 소요되는 시간과 비용을 절감할 수 있다. 또한, 다양한 정형 및 비정형 데이터의 연계에 적합한 그래프 데이터베이스의 특징에 의해, 제안한 프로세스를 통해 구축한 실내 데이터베이스는 기존 공간 모델의 기능을 포함하면서 다양한 유형의 길안내 서비스에 활용될 수 있을 것으로 기대된다.Changes to the indoor environment have increased social interest in ensuring the mobility of people with disabilities. Therefore, the demand for customized indoor routing services for people with mobility disabilities (PWMD), who have many travel restrictions, is increasing. These services have progressed from spatial routing to personalized routing, which reflects personal preferences and experiences in planning an optimal path. In this regard, it is necessary to generate a database for PWMD with a flexible schema suitable for the efficient manipulation and processing of data. This study aims to propose a technique of generating an indoor graph database for PWMD using scanned floor plans. First, a conceptual data model was developed by deriving relevant indoor features and influential factors, considering various international regulations on indoor environments. Also, the accessibility index was designed based on the data model to quantify the difficulties in accessing spaces based on each indoor spaces geometric characteristics. Next, a three-stage process was proposed: retrieving the structure of spaces from scanned floor plans through a transfer learning-based approach, retrieving topology and assessing accessibility for creating an indoor network model for PWMD, and converting the network model into a graph database. Specifically, an indoor structure map is created by fine-tuning the modified Resnet-based model with newly annotated floor plans for extracting structure information. Also, based on the spatial relationship of the extracted features, the indoor network model was created by abstracting indoor spaces with nodes and links. The accessibility of each space is determined by the proposed indices and thresholds; thereby, a feasible network for PWMD could be derived. Then, a process was developed for automatically converting an indoor network model, including accessibility property, into a graph database. The proposed technique was applied to the Seoul National University dataset to generate an indoor graph database for PWMD. Two scenario-based routing tests were conducted using the generated database to verify the utility of results: multi-floor routing and integrated indoor-outdoor routing. As a result, compared with the path for general pedestrians, the optimal path for PWMD was derived by avoiding inaccessible spaces, including vertical movement using elevators rather than the nearest stairs. In other words, applying the proposed technique, a database that adequately described an indoor environment in terms of PWMD with sufficient mobile constraint information could be constructed. Moreover, an integrated indoor-outdoor routing could be conducted by only creating an entrance-labeled relationship, without scale and coordinate transformation. This result reflects the usability of the generated graph database and its suitability regarding the incorporation of multiple individual data sources. The main contribution lies in the development of the process for generating an indoor graph database for PWMD using scanned floor plans. In particular, the database for PWMD routing can be generated based on the proposed data model with PWMD-related features and factors. Also, sub-procedures for topology retrieval and graph database conversion are developed to generate the indoor graph database by the end-to-end process. The developed sub-procedures are performed automatically, thereby reducing the required times and costs. It is expected that the target database of the proposed process can be generated considering utilization for various types of routing since the graph database is easily integrated with multiple types of information while covering the existing spatial models function.1. Introduction 1 1.1 Objectives and contributions 1 1.2 Related works 7 1.2.1 Indoor environment conceptualization 7 1.2.2 Indoor data construction 11 1.2.3 Accessibility assessment 19 1.3 Research scope and flow 22 2. Conceptual modeling 26 2.1 Relevant features and factors 28 2.2 Proposed data model 30 2.3 Space accessibility for PWMD 36 2.3.1 Influential factors within indoor environments 37 2.3.2 Accessibility index 41 3. Indoor graph database for PWMD from scanned floor plans 43 3.1 Retrieving structure of indoor spaces 43 3.1.1 Pre-trained model for detecting indoor geometry 45 3.1.2 Dataset with new annotation 47 3.1.3 Transfer learning-based approach 52 3.2 Generating the indoor network model for PWMD 56 3.2.1 Definition of nodes and links in the network model 60 3.2.2 The classification rule of space polygons 63 3.2.3 Connection between general spaces and doors 68 3.2.4 Node-link generation for horizontal transition spaces 71 3.2.5 Vertical link generation 75 3.2.6 Connectivity and accessibility information generation 79 3.3 Indoor graph database for PWMD 80 3.3.1 Graph representation of indoor environments 80 3.3.2 Conversion of network model into graph database 83 3.4 Entire process 87 4. Experiment and results 89 4.1 Experimental setup and test data 89 4.2 Evaluation for retrieved information 92 4.2.1 Results of structure retrieval 92 4.2.2 Results of topology retrieval 99 4.3 Generated indoor graph database for PWMD 128 4.3.1 Results of the indoor graph database for PWMD 128 4.3.2 Query-based routing 136 5. Conclusion 147 References 150 Appendix 166 국문초록 178박

Computer integrated documentation

The main technical issues of the Computer Integrated Documentation (CID) project are presented. The problem of automation of documents management and maintenance is analyzed both from an artificial intelligence viewpoint and from a human factors viewpoint. Possible technologies for CID are reviewed: conventional approaches to indexing and information retrieval; hypertext; and knowledge based systems. A particular effort was made to provide an appropriate representation for contextual knowledge. This representation is used to generate context on hypertext links. Thus, indexing in CID is context sensitive. The implementation of the current version of CID is described. It includes a hypertext data base, a knowledge based management and maintenance system, and a user interface. A series is also presented of theoretical considerations as navigation in hyperspace, acquisition of indexing knowledge, generation and maintenance of a large documentation, and relation to other work

Automated Bridge Inspection for Concrete Surface Defect Detection Using Deep Neural Network Based on LiDAR Scanning

Structural inspection and maintenance of bridges are essential to improve the safety and sustainability of the infrastructure systems. Visual inspection using non-equipped eyes is the principal method of detecting surface defects of bridges, which is time-consuming, unsafe, and encounters inspectors falling risks. Therefore, there is a need for automated bridge inspection. Recently, Light Detection and Ranging (LiDAR) scanners are used for detecting surface defects. LiDAR scanners can collect high-quality 3D point cloud datasets. In order to automate the process of structural inspection, it is important to collect proper datasets and use an efficient approach to analyze them and find the defects. Deep Neural Networks (DNNs) have been recently used for detecting 3D objects within 3D point clouds. PointNet and PointNet++ are deep neural networks for classification, part segmentation, and semantic segmentation of point clouds that are modified and adapted in this work to detect surface concrete defects. The research contributions are: (1) Designing a LiDAR-equipped UAV platform for structural inspection using an affordable 2D scanner for data collection, and (2) Proposing a method for detecting concrete surface defects using deep neural networks based on LiDAR generated point clouds. Training and testing datasets are collected from four concrete bridges in Montréal and annotated manually. The point cloud dataset prepared in five areas, which contain more than 51 million points and 2,572 annotated defects in four classes of crack, light spalling, medium spalling, and severe spalling. The accuracies of 75% (adapted PointNet) and 79% (adapted PointNet++) in detecting defects are achieved in binary semantic segmentation

Extraction of textual information from image for information retrieval

Ph.DDOCTOR OF PHILOSOPH

Investigation of advanced navigation and guidance system concepts for all-weather rotorcraft operations

Results are presented of a survey conducted of active helicopter operators to determine the extent to which they wish to operate in IMC conditions, the visibility limits under which they would operate, the revenue benefits to be gained, and the percent of aircraft cost they would pay for such increased capability. Candidate systems were examined for capability to meet the requirements of a mission model constructed to represent the modes of flight normally encountered in low visibility conditions. Recommendations are made for development of high resolution radar, simulation of the control display system for steep approaches, and for development of an obstacle sensing system for detecting wires. A cost feasibility analysis is included