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نتیجه جستجو - دیدگاه کامپیوتر

تعداد مقالات یافته شده: 18
ردیف عنوان نوع
1 Combining computer vision with semantic reasoning for on-site safety management in construction
ترکیب بینایی ماشین با استدلال معنایی برای مدیریت ایمنی در هر دو سو در ساخت -2021
Computer vision has been utilized to extract safety-related information from images with the advancement of video monitoring systems and deep learning algorithms. However, construction safety management is a knowledge-intensive task; for instance, safety managers rely on safety regulations and their prior knowledge during a jobsite safety inspection. This paper presents a conceptual framework that combines computer vision and ontology techniques to facilitate the management of safety by semantically reasoning hazards and corre- sponding mitigations. Specifically, computer vision is used to detect visual information from on-site photos while the safety regulatory knowledge is formally represented by ontology and semantic web rule language (SWRL) rules. Hazards and corresponding mitigations can be inferred by comparing extracted visual information from construction images with pre-defined SWRL rules. Finally, the example of falls from height is selected to validate the theoretical and technical feasibility of the developed conceptual framework. Results show that the proposed framework operates similar to the thinking model of safety managers and can facilitate on-site hazard identi- fication and prevention by semantically reasoning hazards from images and listing corresponding mitigations. 1. Introduction
keywords: بینایی ماشین | هستی شناسی | استدلال معنایی | شناسایی ریسک | مدیریت ایمنی ساخت | Computer vision | Ontology | Semantic reasoning | Hazard identification | Construction safety management
مقاله انگلیسی
2 Protecting the privacy of humans in video sequences using a computer vision-based de-identification pipeline
حفاظت از حریم خصوصی افراد در توالی های ویدئویی با استفاده از شناسایی مبتنی بر دید کامپیوتری لوله ای-2017
Article history:Received 12 October 2016Revised 5 May 2017Accepted 27 May 2017Keywords:Privacy protection De-identification Computer vision Video processingWe propose a computer vision-based de-identification pipeline that enables automated protection of pri- vacy of humans in video sequences through obfuscating their appearance, while preserving the natu- ralness and utility of the de-identified data. Our pipeline specifically addresses de-identifying soft and non-biometric features, such as clothing, hair, skin color etc., which often remain recognizable when sim- pler techniques such as blurring are applied. Assuming a surveillance scenario, we combine background subtraction based on Gaussian mixtures with an improved version of the GrabCut algorithm to find and segment pedestrians. De-identification is performed by altering the appearance of the segmented pedes- trians through the neural art algorithm that uses the responses of a deep neural network to render the pedestrian images in a different style. Experimental evaluation is performed both by automated classifi- cation and through a user study. Results suggest that the proposed pipeline successfully de-identifies a range of hard and soft biometric and non-biometric identifiers, including face, clothing and hair.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Privacy protection | De-identification | Computer vision | Video processing
مقاله انگلیسی
3 Traffic light recognition exploiting map and localization at every stage
شناخت ترافیک با استفاده از نقشه و محلی سازی در هر مرحله-2017
Article history:Received 5 February 2017Revised 30 June 2017Accepted 5 July 2017Available online 12 July 2017Keywords:Traffic light recognition Localization and digital map Intelligent vehiclesIntelligent transportation systems Computer visionObject detectionTraffic light recognition is being intensively researched for the purpose of reducing traffic accidents at in- tersections and realizing autonomous driving. However, conventional vision-based approaches have sev- eral limitations due to full image scanning, always-on operation, various different types of traffic lights, and complex driving environments. In particular, it might be impossible to recognize a relevant traffic light among multiple traffic lights at multiple intersections. To overcome such limitations, we propose an effective architecture that integrates a vision system with an accurate positioning system and an extended digital map. The recognition process is divided into four stages and we suggest an extended methodology for each stage. These stages are: ROI generation, detection, classification, and tracking. The 3D positions of traffic lights and slope information obtained from an extended digital map enable ROIs to be generated accurately, even on slanted roads, while independent design and implementation of individual recogni- tion modules for detection and classification allow for selection according to the type of traffic light face. Such a modular architecture gives the system simplicity, flexibility, and maintainable algorithms. In ad- dition, adaptive tracking that exploits the distance to traffic lights allows for seamless state estimation through smooth data association when measurements change from long to short ranges. Evaluation of the proposed system occurred at six test sites and utilized two different types of traffic lights, seven states, sloped roads, and various environmental complexities. The experimental results show that the proposed system can recognize traffic lights with 98.68% precision, 92.73% recall, and 95.52% accuracy in the 10.02–81.21 m range.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Traffic light recognition | Localization and digital map | Intelligent vehicles | Intelligent transportation systems | Computer vision | Object detection
مقاله انگلیسی
4 Computer vision for assistive technologies
چشم انداز کامپیوتر برای فن آوری های کمک-2017
Article history:Received 17 April 2015Revised 1 September 2016Accepted 2 September 2016Available online 6 September 2016Keywords:Computer vision Assistive technologiesIn the last decades there has been a tremendous increase in demand for Assistive Technologies (AT) useful to overcome functional limitations of individuals and to improve their quality of life. As a consequence, different research papers addressing the development of assistive technologies have appeared into the literature pushing the need to organize and categorize them taking into account the application assistive aims. Several surveys address the categorization problem for works concerning a specific need, hence giving the overview on the state of the art technologies supporting the related function for the individual. Unfortunately, this “user-need oriented” way of categorization considers each technology as a whole and then a deep and critical explanation of the technical knowledge used to build the operative tasks as well as a discussion on their cross-contextual applicability is completely missing making thus existing surveys unlikely to be technically inspiring for functional improvements and to explore new technological frontiers. To overcome this critical drawback, in this paper an original “task oriented” way to categorize the state of the art of the AT works has been introduced: it relies on the split of the final assistive goals into tasks that are then used as pointers to the works in literature in which each of them has been used as a component. In particular this paper concentrates on a set of cross-application Computer Vision tasks that are set as the pivots to establish a categorization of the AT already used to assist some of the user’s needs. For each task the paper analyzes the Computer Vision algorithms recently involved in the development of AT and, finally, it tries to catch a glimpse of the possible paths in the short and medium term that could allow a real improvement of the assistive outcomes. The potential impact on the assessment of AT considering users, medical, economical and social perspective is also addressed.© 2016 Elsevier Inc. All rights reserved.
Keywords: Computer vision | Assistive technologies
مقاله انگلیسی
5 Robust normal estimation and region growing segmentation of infrastructure 3D point cloud models
تخمین قوی نرمال و تقسیم بندی ناحیه رشد و زیر بنای مدل ابری نقطه ای-2017
Modern remote sensing technologies such as three-dimensional (3D) laser scanners and image-based 3D scene reconstruction are in increasing demand for applications in civil infrastructure design, mainte- nance, operation, and as-built construction verification. The complex nature of the 3D point clouds these technologies generate, as well as the often massive scale of the 3D data, make it inefficient and time con- suming to manually analyze and manipulate point clouds, and highlights the need for automated analysis techniques. This paper presents one such technique, a new region growing algorithm for the automated segmentation of both planar and non-planar surfaces in point clouds. A core component of the algorithm is a new point normal estimation method, an essential task for many point cloud processing algorithms. The newly developed estimation method utilizes robust multivariate statistical outlier analysis for reli- able normal estimation in complex 3D models, considering that these models often contain regions of varying surface roughness, a mixture of high curvature and low curvature regions, and sharp features. An adaptation of Mahalanobis distance, in which the mean vector and covariance matrix are derived from a high-breakdown multivariate location and scale estimator called Deterministic MM-estimator (DetMM) is used to find and discard outlier points prior to estimating the best local tangent plane around any point in a cloud. This approach is capable of more accurately estimating point normals located in highly curved regions or near sharp features. Thereafter, the estimated point normals serve a region growing segmen- tation algorithm that only requires a single input parameter, an improvement over existing methods which typically require two control parameters. The reliability and robustness of the normal estimation subroutine was compared against well-known normal estimation methods including the Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) estimators, along with Maximum Likelihood Sample Consensus (MLESAC). The overall region growing segmentation algorithm was then experimentally validated on several challenging 3D point clouds of real-world infrastructure systems. The results indicate that the developed approach performs more accurately and robustly in comparison with conventional region growing methods, particularly in the presence of sharp features, outliers and noise.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Segmentation | 3D point cloud models | Robust estimation | Outliers | 3D reconstruction | Computer vision | Normal estimation | 3D data processing
مقاله انگلیسی
6 Learning and Surface Boundary Feedbacks for Colour Natural Scene Perception
یادگیری و بازخورد مرزی سطحی برای درک صحنه طبیعی رنگ-2017
HIGHLIGHTSBio-inspired natural scene boundary learning for detection.Feedback interactions among V1, V2, V4 and IT areas.Results quantified using F-measure from the Berkeley segmentation benchmark.Neural architecture performance and results compatible with real applications.AbstractBoundary detection and segmentation are essential stages in object recognition and scene understanding. In this paper, we present a bio-inspired neural model of the ventral pathway for colour contour and surface perception, called LPREEN (Learning and Perceptual boundaRy rEcurrent dEtection Neural architecture). LPREEN models colouropponent processes and feedback interactions between cortical areas V1, V2, V4, and IT, which produce top-down and bottom-up information fusion. We suggest three feedback interactions that enhance and complete boundaries. Our proposed neural model contains a contour learning feedback that enhances the most probable contour positions in V1 according to a previous experience, and generates a surface perception in V4 through diffusion processes. We compared the proposed model with another bio-inspired model and two well-known contour extraction methods, using the Berkeley Segmentation Benchmark. LPREEN showed better performance than two methods and slightly worse performance than another one.
Keywords : Computer vision | contour learning | boundary detection | neural networks | colour image processing | bio-inspired models
مقاله انگلیسی
7 Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes
Midgar: تشخیص افراد از طریق بینایی ماشین در اینترنت اشیا برای بهبود امنیت در شهرهای هوشمند، شهر ستان های هوشمند و خانه های هوشمند-2017
Could we use Computer Vision in the Internet of Things for using pictures as sensors? This is the principal hypothesis that we want to resolve. Currently, in order to create safety areas, cities, or homes, people use IP cameras. Nevertheless, this system needs people who watch the camera images, watch the recording after something occurred, or watch when the camera notifies them of any movement. These are the disadvantages. Furthermore, there are many Smart Cities and Smart Homes around the world. This is why we thought of using the idea of the Internet of Things to add a way of automating the use of IP cameras. In our case, we propose the analysis of pictures through Computer Vision to detect people in the analysed pictures. With this analysis, we are able to obtain if these pictures contain people and handle the pictures as if they were sensors with two possible states. Notwithstanding, Computer Vision is a very complicated field. This is why we needed a second hypothesis: Could we work with Computer Vision in the Internet of Things with a good accuracy to automate or semi-automate this kind of events? The demonstration of these hypotheses required a testing over our Computer Vision module to check the possibilities that we have to use this module in a possible real environment with a good accuracy. Our proposal, as a possible solution, is the analysis of entire sequence instead of isolated pictures for using pictures as sensors in the Internet of Things.
Keywords: Smart Cities | Smart Towns | Smart Homes | Internet of Things | Smart Objects | Computer Vision | Surveillance | Security
مقاله انگلیسی
8 Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes
Midgar: تشخیص مردم از طریق چشم انداز کامپیوتر درسناریو اینترنت اشیاء برای بهبود امنیت در شهرهای هوشمند، شهرهای کوچک هوشمند و خانه های هوشمند-2017
Could we use Computer Vision in the Internet of Things for using pictures as sensors? This is the principal hypothesis that we want to resolve. Currently, in order to create safety areas, cities, or homes, people use IP cameras. Nevertheless, this system needs people who watch the camera images, watch the recording after something occurred, or watch when the camera notifies them of any movement. These are the disadvantages. Furthermore, there are many Smart Cities and Smart Homes around the world. This is why we thought of using the idea of the Internet of Things to add a way of automating the use of IP cameras. In our case, we propose the analysis of pictures through Computer Vision to detect people in the analysed pictures. With this analysis, we are able to obtain if these pictures contain people and handle the pictures as if they were sensors with two possible states. Notwithstanding, Computer Vision is a very complicated field. This is why we needed a second hypothesis: Could we work with Computer Vision in the Internet of Things with a good accuracy to automate or semi-automate this kind of events? The demonstration of these hypotheses required a testing over our Computer Vision module to check the possibilities that we have to use this module in a possible real environment with a good accuracy. Our proposal, as a possible solution, is the analysis of entire sequence instead of isolated pictures for using pictures as sensors in the Internet of Things.
Keywords: Smart Cities | Smart Towns | Smart Homes | Internet of Things | Smart Objects | Computer Vision | Surveillance | Security
مقاله انگلیسی
9 Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes
Midgar: تشخیص مردم از طریق چشم انداز کامپیوتری در اینترنت از شرایط برای بهبود امنیت در شهرهای هوشمند، شهر های کوچک هوشمند و خانه های هوشمند-2017
Article history:Received 28 October 2015 Received in revised form 23 December 2016Accepted 29 December 2016Available online 5 January 2017Keywords: Smart Cities Smart Towns Smart HomesInternet of Things Smart Objects Computer Vision Surveillance SecurityCould we use Computer Vision in the Internet of Things for using pictures as sensors? This is the principal hypothesis that we want to resolve. Currently, in order to create safety areas, cities, or homes, people use IP cameras. Nevertheless, this system needs people who watch the camera images, watch the recording after something occurred, or watch when the camera notifies them of any movement. These are the disadvantages. Furthermore, there are many Smart Cities and Smart Homes around the world. This is why we thought of using the idea of the Internet of Things to add a way of automating the use of IP cameras. In our case, we propose the analysis of pictures through Computer Vision to detect people in the analysed pictures. With this analysis, we are able to obtain if these pictures contain people and handle the pictures as if they were sensors with two possible states. Notwithstanding, Computer Vision is a very complicated field. This is why we needed a second hypothesis: Could we work with Computer Vision in the Internet of Things with a good accuracy to automate or semi-automate this kind of events? The demonstration of these hypotheses required a testing over our Computer Vision module to check the possibilities that we have to use this module in a possible real environment with a good accuracy. Our proposal, as a possible solution, is the analysis of entire sequence instead of isolated pictures for using pictures as sensors in the Internet of Things.© 2016 Elsevier B.V. All rights reserved.
Keywords:Smart Cities | Smart Towns | Smart Homes | Internet of Things | Smart Objects | Computer Vision | Surveillance | Security
مقاله انگلیسی
10 An effective and efficient approximate two-dimensional dynamic programming algorithm for supporting advanced computer vision applications
یک الگوریتم برنامه ریزی پویا دو بعدی مؤثر و کارآمد برای پشتیبانی از برنامه های کاربردی پیشرفته کامپیوتری کامپیوتری-2017
Article history:Received 7 January 2017Accepted 11 July 2017Available online 2 August 2017Keywords:Two-dimensional dynamic programming CUDA platformComputer vision Intelligent systemsDynamic programming is a popular optimization technique, developed in the 60’s and still widely used today in several fields for its ability to find global optimum. Dynamic Programming Algorithms (DPAs) can be developed in many dimension. However, it is known that if the DPA dimension is greater or equal to two, the algorithm is an NP complete problem. In this paper we present an approximation of the fully two-dimensional DPA (2D-DPA) with polynomial complexity. Then, we describe an implementation of the algorithm on a recent parallel device based on CUDA architecture. We show that our parallel implemen- tation presents a speed-up of about 25 with respect to a sequential implementation on an Intel I7 CPU. In particular, our system allows a speed of about ten 2D-DPA executions per second for 85 × 85 pixels images. Experiments and case studies support our thesis.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Two-dimensional dynamic programming | CUDA platform | Computer vision | Intelligent systems
مقاله انگلیسی
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