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1 – 10 of over 34000Ying Tao Chai and Ting-Kwei Wang
Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection…
Abstract
Purpose
Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.
Design/methodology/approach
Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.
Findings
Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.
Originality/value
This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.
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Elena Villaespesa and Seth Crider
Based on the highlights of The Metropolitan Museum of Art's collection, the purpose of this paper is to examine the similarities and differences between the subject keywords tags…
Abstract
Purpose
Based on the highlights of The Metropolitan Museum of Art's collection, the purpose of this paper is to examine the similarities and differences between the subject keywords tags assigned by the museum and those produced by three computer vision systems.
Design/methodology/approach
This paper uses computer vision tools to generate the data and the Getty Research Institute's Art and Architecture Thesaurus (AAT) to compare the subject keyword tags.
Findings
This paper finds that there are clear opportunities to use computer vision technologies to automatically generate tags that expand the terms used by the museum. This brings a new perspective to the collection that is different from the traditional art historical one. However, the study also surfaces challenges about the accuracy and lack of context within the computer vision results.
Practical implications
This finding has important implications on how these machine-generated tags complement the current taxonomies and vocabularies inputted in the collection database. In consequence, the museum needs to consider the selection process for choosing which computer vision system to apply to their collection. Furthermore, they also need to think critically about the kind of tags they wish to use, such as colors, materials or objects.
Originality/value
The study results add to the rapidly evolving field of computer vision within the art information context and provide recommendations of aspects to consider before selecting and implementing these technologies.
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Haroon Idrees, Mubarak Shah and Ray Surette
The growth of police operated surveillance cameras has out-paced the ability of humans to monitor them effectively. Computer vision is a possible solution. An ongoing research…
Abstract
Purpose
The growth of police operated surveillance cameras has out-paced the ability of humans to monitor them effectively. Computer vision is a possible solution. An ongoing research project on the application of computer vision within a municipal police department is described. The paper aims to discuss these issues.
Design/methodology/approach
Following the demystification of computer vision technology, its potential for police agencies is developed within a focus on computer vision as a solution for two common surveillance camera tasks (live monitoring of multiple surveillance cameras and summarizing archived video files). Three unaddressed research questions (can specialized computer vision applications for law enforcement be developed at this time, how will computer vision be utilized within existing public safety camera monitoring rooms, and what are the system-wide impacts of a computer vision capability on local criminal justice systems) are considered.
Findings
Despite computer vision becoming accessible to law enforcement agencies the impact of computer vision has not been discussed or adequately researched. There is little knowledge of computer vision or its potential in the field.
Originality/value
This paper introduces and discusses computer vision from a law enforcement perspective and will be valuable to police personnel tasked with monitoring large camera networks and considering computer vision as a system upgrade.
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Bambang Rilanto Trilaksono, Ryan Triadhitama, Widyawardana Adiprawita, Artiko Wibowo and Anavatti Sreenatha
The purpose of this paper is to present the development of hardware‐in‐the‐loop simulation (HILS) for visual target tracking of an octorotor unmanned aerial vehicle (UAV) with…
Abstract
Purpose
The purpose of this paper is to present the development of hardware‐in‐the‐loop simulation (HILS) for visual target tracking of an octorotor unmanned aerial vehicle (UAV) with onboard computer vision.
Design/methodology/approach
HILS for visual target tracking of an octorotor UAV is developed by integrating real embedded computer vision hardware and camera to software simulation of the UAV dynamics, flight control and navigation systems run on Simulink. Visualization of the visual target tracking is developed using FlightGear. The computer vision system is used to recognize and track a moving target using feature correlation between captured scene images and object images stored in the database. Features of the captured images are extracted using speed‐up robust feature (SURF) algorithm, and subsequently matched with features extracted from object image using fast library for approximate nearest neighbor (FLANN) algorithm. Kalman filter is applied to predict the position of the moving target on image plane. The integrated HILS environment is developed to allow real‐time testing and evaluation of onboard embedded computer vision for UAV's visual target tracking.
Findings
Utilization of HILS is found to be useful in evaluating functionality and performance of the real machine vision software and hardware prior to its operation in a flight test. Integrating computer vision with UAV enables the construction of an unmanned system with the capability of tracking a moving object.
Practical implications
HILS for visual target tracking of UAV described in this paper could be applied in practice to minimize trial and error in various parameters tuning of the machine vision algorithm as well as of the autopilot and navigation system. It also could reduce development costs, in addition to reducing the risk of crashing the UAV in a flight test.
Originality/value
A HILS integrated environment for octorotor UAV's visual target tracking for real‐time testing and evaluation of onboard computer vision is proposed. Another contribution involves implementation of SURF, FLANN, and Kalman filter algorithms on an onboard embedded PC and its integration with navigation and flight control systems which enables the UAV to track a moving object.
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We have long been obsessed with the dream of creating intelligent machines. This vision can be traced back to Greek civilization, and the notion that mortals somehow can create…
Abstract
We have long been obsessed with the dream of creating intelligent machines. This vision can be traced back to Greek civilization, and the notion that mortals somehow can create machines that think has persisted throughout history. Until this decade these illusions have borne no substance. The birth of the computer in the 1940s did cause a resurgence of the cybernaut idea, but the computer's role was primarily one of number‐crunching and realists soon came to respect the enormous difficulties in crafting machines that could accomplish even the simplest of human tasks.
Mahesh Babu Purushothaman and Kasun Moolika Gedara
This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and…
Abstract
Purpose
This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.
Design/methodology/approach
Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).
Findings
Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.
Research limitations/implications
Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.
Practical implications
The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.
Social implications
By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.
Originality/value
Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.
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Kun Zhang, Hanqin Qiu, Jingyue Wang, Chunlin Li, Jinyi Zhang and Dora Dongzhi Chen
This paper aims to answer the following four research questions: Where do tourists gaze at the destination? What do tourists gaze at the destination? How do tourists gaze…
Abstract
Purpose
This paper aims to answer the following four research questions: Where do tourists gaze at the destination? What do tourists gaze at the destination? How do tourists gaze differently? Why do tourists gaze differently referring to relevant theory?
Design/methodology/approach
With a computer vision approach, this study illustrated a series of maps that reflect where and what do tourists gaze at and compared the differences in the visual perceptions among Asian, European and North American tourists in Hong Kong.
Findings
The findings confirm that the “tourist gaze” is influenced by geographical and cultural conditions. The conclusions provided three types of implementations for destination management strategies and advocated a high engagement with computer vision technology.
Originality/value
In theory, this study proves that the “tourist gaze” is influenced by geographical and cultural conditions. The study’s methodological contribution lies in applying advanced technology of visual content analysis for big data relevant to the issue of the tourist gaze. Practically, the finding that has not been achieved via previous questionnaire surveys will serve as a reference for tourism recommendations and precision marketing. In addition, its practical contribution is that it offers a means by which to explore tourists’ perceptions of destinations and understand the attractiveness of destinations to tourists.
研究设计/方法/技术
研究一方面使用计算机视觉深入学习模型对游客照片内容进行识别, 比较了亚洲、欧洲和北美游客在香港不同空间场景的视觉感知差异。另一方面, 研究借助ArcGIS软件对游客凝视地点和内容差异进行了具体可视化分析。
研究目的
这项研究有四个研究子问题:
(1) 游客在哪里凝视?
(2) 游客凝视了什么?
(3) 游客凝视内容有什么不同?
(4) 为什么游客凝视不同?
(1) 游客在哪里凝视?
(2) 游客凝视了什么?
(3) 游客凝视内容有什么不同?
(4) 为什么游客凝视不同?
研究发现
不同游客在旅游目的地的“凝视”存在差异, 差异表征具体体现在地点选择和内容偏好等维度。同时, 研究结果显示计算机视觉技术在旅游研究领域呈现较好的应用潜力。
原创/价值
理论上, 本研究佐证了”游客凝视”受地理和文化条件影响的理论。技术上, 本研究探索了视觉分析技术在游客凝视议题上应用, 为旅游目的地感知评估提供了新的视角。应用层面, 研究结论为旅游目的地精准营销提供了参考。
Resumen
Diseño/metodología/enfoque
Con un enfoque de visión artificial, este estudio ilustra una serie de mapas que reflejan dónde y qué miran los turistas, y compara las diferencias en las percepciones visuales entre los turistas asiáticos, europeos y norteamericanos en Hong Kong.
Objetivo
El estudio tiene cuatro preguntas de investigación:
(1) ¿Dónde miran los turistas en el destino?
(2) ¿Qué miran los turistas en el destino?
(3) ¿Cómo miran los turistas de forma diferente?
(4) ¿Por qué los turistas miran de forma diferente en referencia a la teoría pertinente?
(1) ¿Dónde miran los turistas en el destino?
(2) ¿Qué miran los turistas en el destino?
(3) ¿Cómo miran los turistas de forma diferente?
(4) ¿Por qué los turistas miran de forma diferente en referencia a la teoría pertinente?
Conclusiones
Las conclusiones confirman que la “mirada del turista” está influida por las condiciones geográficas y culturales. Las conclusiones aportan tres tipos de aplicaciones para las estrategias de gestión de destinos y abogan por un alto compromiso con la tecnología de visión artificial.
Originalidad/valor
En teoría, este estudio demuestra que la “mirada del turista” está influenciada por las condiciones geográficas y culturales. La contribución metodológica del estudio radica en la aplicación de tecnología avanzada de análisis de contenido visual para big data relevante para el tema de la mirada del turista. En la práctica, los hallazgos que no se han logrado a través de encuestas anteriores servirán de referencia para las recomendaciones turísticas y el marketing de precisión. Además, su contribución práctica es que ofrece un medio para explorar las percepciones de los turistas sobre los destinos, y comprender el atractivo de los mismos para los turistas.
Details
Keywords
- Visual content analysis
- Computer vision technology
- Spatial distribution
- Geo-tagged photos
- Deep learning model
- Cultural convention
- Visual perception
- Análisis de contenido visual
- Tecnología de vision artificial
- Distribución espacial
- Fotos geoetiquetadas
- Modelo de deep learning
- Convención cultural
- 视觉内容分析
- 计算机视觉技术
- 空间分布
- 带有地理标签的照片
- 深入学习模型
- 文化传统
Dianchen Zhu, Huiying Wen and Yichuan Deng
To improve insufficient management by artificial management, especially for traffic accidents that occur at crossroads, the purpose of this paper is to develop a pro-active…
Abstract
Purpose
To improve insufficient management by artificial management, especially for traffic accidents that occur at crossroads, the purpose of this paper is to develop a pro-active warning system for crossroads at construction sites. Although prior studies have made efforts to develop warning systems for construction sites, most of them paid attention to the construction process, while the accidents that occur at crossroads were probably overlooked.
Design/methodology/approach
By summarizing the main reasons resulting for those accidents occurring at crossroads, a pro-active warning system that could provide six functions for countermeasures was designed. Several approaches relating to computer vision and a prediction algorithm were applied and proposed to realize the setting functions.
Findings
One 12-hour video that films a crossroad at a construction site was selected as the original data. The test results show that all designed functions could operate normally, several predicted dangerous situations could be detected and corresponding proper warnings could be given. To validate the applicability of this system, another 36-hour video data were chosen for a performance test, and the findings indicate that all applied algorithms show a significant fitness of the data.
Originality/value
Computer vision algorithms have been widely used in previous studies to address video data or monitoring information; however, few of them have demonstrated the high applicability of identification and classification of the different participants at construction sites. In addition, none of these studies attempted to use a dynamic prediction algorithm to predict risky events, which could provide significant information for relevant active warnings.
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Drago Torkar, Rudi Murn and Dušan Peček
Despite a relatively long tradition and outstanding progress in CCD technology during the last few years, computer vision applications have not become as significant as was…
Abstract
Despite a relatively long tradition and outstanding progress in CCD technology during the last few years, computer vision applications have not become as significant as was expected. Because of lack of understanding and the complexity of applications themselves, the introduction of computer vision technology in production lines is still rare. Rarity raises prices and so we go round in circles. At present computer vision needs effective, simple and low‐cost applications, which will make it accessible to potential customers and show them all its possibilities and benefits. Industrial users need solutions instead of theory.
John Oyekan, Axel Fischer, Windo Hutabarat, Christopher Turner and Ashutosh Tiwari
The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line…
Abstract
Purpose
The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques.
Design/methodology/approach
This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined.
Findings
This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable.
Originality/value
Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production.
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