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نتیجه جستجو - تشخیص شی

تعداد مقالات یافته شده: 18
ردیف عنوان نوع
1 تشخیص و شناسایی ترافیک بر اساس شبکه‌های پیچشی هرمی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 28
با توسعه فناوری بدون‌‌راننده، ما به شدت نیاز به روشی برای درک صحنه‌های ترافیکی داریم. با این حال هنوز شناسایی علائم راهنمایی و رانندگی به دلیل مقیاس کوچک این نشانه‌ها در تصاویر جهان واقعی، وظیفه‌ای دشوار است. در سناریوهای پیچیده برخی علائم راهنمایی و رانندگی به دلیل شرایط آب و هوایی بسیار بد و شرایط نورپردازی می‌تواند بسیار اغفال‌‌کننده باشد. برای پیاده‌سازی یک سیستم تشخیص و شناسایی جامع‌تر ما یک شبکه‌ دو مرحله‌ای را توسعه می‌دهیم. در مرحله پیشنهاد ناحیه، ما یک معماری عرمی ویژگی عمیق را با اتصالات جانبی به کار می‌گیریم که سبب می‌شود ویژگی‌های معنایی شی کوچک حساس‌تر شوند. در مرحله طبقه‌بندی شبکه پیچیشی که به شکل متراکم متصل شده است به منظور تقویت انتقال و تسهیم ویژگی مورد استفاده واقع شده است که این شبکه منجر به طبقه‌بندی دقیق‌تر با تعداد پارامترهای کمتر خواهد شد. ما بر روی بنچمارک تشخیص GTSDB و همچنین بر روی بنچمارک چالش برانگیز k100 Tsinghua-Tencent نیز آزمایش کردیم که برای اکثر شبکه‌های سنتی بسیار مشکل است. آزمایشات نشان می‌دهند که روش پیشنهادی ما عملکردی بسیار عالی را کسب می‌کند و از سایر جدیدترین روش‌ها نیز بهتر است. پیاده‌سازی کد منبع در آدرس روبرو در دسترس است: https://github.com/derderking/Traffic-Sign.
کلیدواژه‌ها: نشانه ترافیک | تشخیص شی | هرم ویژگی.
مقاله ترجمه شده
2 Foveated ghost imaging based on deep learning
تصویربرداری از خیال مبتنی بر یادگیری عمیق-2019
Ghost imaging is an unconventional imaging mechanism that utilizes the high-order correlation to reconstruct object’s image. Limited by the maximum refresh rate of DMD or SLM, the sampling efficiency of ghost imaging has been a major obstacle for practical application. In this paper, foveated ghost imaging based on deep learning (DPFGI) is proposed to generate non-uniform resolution speckle patterns according to the object detection results as the fovea point. We combine foveated speckle pattern inspired by the human visual system with GAN-based ghost imaging object detection system to realize selecting the region of interest for foveated imaging intelligently. The simulation and experimental results show that DPFGI can detect objects in undersampled images with higher accuracy and achieve higher PSNR in the fovea region compared with uniform-resolution ghost imaging, which opens new perspectives for more intelligent ghost imaging.
Keywords: Ghost imaging | Foveated imaging | Deep learning | Object detection
مقاله انگلیسی
3 Automatic object detection using dynamic time warping on ground penetrating radar signals
ردیابی خودکار شی با استفاده از چرخش زمانی پویا در سیگنالهای رادار نافذ در زمین-2019
Ground Penetrating Radar (GPR) is a widely used non-destructive method in buried object detection. However, online, automatic, and accurate location and depth estimation methods using GPR are still un- der development. In this article, a cutting-edge expert system is proposed that compares signals from newly scanned locations to a target-free accumulated reference signal and computes a dissimilarity mea- sure using Dynamic Time Warping (DTW). By setting a threshold on DTW values and monitoring them online, a significant deviation of the DTW values from the reference signal is detected prior to reaching an object. A potential burial site is therefore automatically detected without having a complete GPR scan which is a huge advantage compared to existing methods. Following the scanning process and investi- gating the potential burial site, location and depth of multiple buried objects is estimated automatically and highly accurate. The fully-automated analytics eliminate the need of expert operators in estimating spatial burial locations and perform accurately even on noisy media. Statistical proofs are provided that support the validity of the developed expert system in theory. Moreover, the analytics run in real-time that is plausible for on-site applications
Keywords: Ground Penetrating Radar signals | Dynamic Time Warping | Sequential confidence intervals | Control process | Object detection
مقاله انگلیسی
4 تشخیص لبه برمبنای بهینه سازی کلونی مورچه ها در تصاویر SAR
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 13
پردازش تشخیص شیء/ هدف به دلیل وجود خالهایی در تصاویر SAR دشوار است که دقت شعاع سنجی و هندسی بالایی را مستقل از همه شرایط جوّی فراهم می کند. با استفاده از روش تشخیص لبه ای که اطلاعات مهمی را از تصویر استخراج می کند، دستیابی به دقت بالاتر و پردازش کمتر تصویر SAR برای تشخیص شیء ازطریق حذف این خالها امکانپذیر می باشد. الگوریتم کُلُنی مورچه ای که یکی از روشهای بهینه سازی اکتشافی می باشد یک الگوریتمی برمبنای مدلهای ریاضی رفتارهای واقعی کلنی مورچه ای می باشد. در ناحیه پردازش تصویر، بهینه سازی کلنی مورچه ای سهم موثری در برخی روشها مثل تشخیص شیء/ هدف در تصاویر خاص با استفاده از روش تشخیص لبه ای دارد. هدف ما استفاده از تشخیص لبه ای برمبنای بهینه سازی کلنی مورچه ای که یک روش موثر بهینه سازی می باشد برای حذف خالهایی است که تشخیص شیء را در تصاویر SAR دشوار می سازند.
کلیدواژه ها: تصویر SAR | تشخیص لبه ای | بهینه سازی کلونی مورچه ها | الگوریتم کلونی مورچه ها | بهینه سازی مکاشفه ای
مقاله ترجمه شده
5 Marine Wireless Big Data: Efficient Transmission, Related Applications, and Challenges
داده های دریایی بزرگ بی سیم: انتقال کارآمد،برنامه های مرتبط و چالش ها-2018
The vast volume of marine wireless sampling data and its continuously explosive growth herald the coming of the era of marine wireless big data. Two challenges imposed by these data are how to fast, reliably, and sustainably deliver them in extremely hostile marine environments and how to apply them after collection. In this article, we first propose an architecture of heterogeneous marine networks that flexibly exploits the existing underwater wireless techniques as a potential solution for fast data transmission. We then investigate the possibilities of and develop the schemes for energy-efficient and reliable undersea transmission without or with slight data rate reduction. After discussing the data transmission, we summarize the possible applications of the collected big data and particularly focus on the problems of applying these data in sea-surface object detection and marine object recognition. Open issues and challenges that need to be further explored regarding transmission and detection/recognition are also discussed in the article.
Keywords: Big Data,marine communication, marine engineering,object detection,object recognition,oceanographic techniques,wireless sensor networks
مقاله انگلیسی
6 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
7 Improved scene identification and object detection on egocentric vision of daily activities
شناسایی صحنه و تشخیص شی در دیدگاه خودمدار از فعالیت های روزانه-2017
Article history:Received 16 December 2015Revised 26 September 2016Accepted 19 October 2016Available online 21 October 2016Keywords:Scene classification Object detection Scene understandingFirst camera person visionThis work investigates the relationship between scene and associated objects on daily activities under egocentric vision constraints. Daily activities are performed in prototypical scenes that share a lot of vi- sual appearances independent of where or by whom the video was recorded. The intrinsic characteristics of egocentric vision suggest that the location where the activity is conducted remains consistent through- out frames. This paper shows that egocentric scene identification is improved by taking the temporal context into consideration. Moreover, since most of the objects are typically associated with particular types of scenes, we show that a generic object detection method can also be improved by re-scoring the results of the object detection method according to the scene content. We first show the case where the scene identity is explicitly predicted to improve object detection, and then we show a framework using Long Short-Term Memory (LSTM) where no labeling of the scene type is needed. We performed exper- iments in the Activities of Daily Living (ADL) public dataset (Pirsiavash and Ramanan,2012), which is a standard benchmark for egocentric vision.© 2016 Elsevier Inc. All rights reserved.
Keywords: Scene classification | Object detection | Scene understanding | First camera person vision
مقاله انگلیسی
8 Vision-based and marker-less surgical tool detection and tracking: a review of the literature
تشخیص و ردیابی ابزار مبتنی بر چشم انداز و بدون ابزار تشریحی : بررسی ادبیات-2017
Article history:Received 31 January 2016Revised 26 June 2016Accepted 5 September 2016Available online 13 September 2016Keywords:Tool detection Object detection Data-set ValidationEndoscopic/microscopic imagesIn recent years, tremendous progress has been made in surgical practice for example with Minimally In- vasive Surgery (MIS). To overcome challenges coming from deported eye-to-hand manipulation, robotic and computer-assisted systems have been developed. Having real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy is a key ingredient for such systems. In this paper, we present a review of the literature dealing with vision-based and marker-less surgical tool detection. This paper includes three primary contributions: (1) identification and analysis of data-sets used for developing and testing detection algorithms, (2) in-depth comparison of surgical tool detection methods from the feature extraction process to the model learning strategy and highlight existing short- comings, and (3) analysis of validation techniques employed to obtain detection performance results and establish comparison between surgical tool detectors. The papers included in the review were selected through PubMed and Google Scholar searches using the keywords: “surgical tool detection”, “surgical tool tracking”, “surgical instrument detection” and “surgical instrument tracking” limiting results to the year range 2000 2015. Our study shows that despite significant progress over the years, the lack of es- tablished surgical tool data-sets, and reference format for performance assessment and method ranking is preventing faster improvement.© 2016 Elsevier B.V. All rights reserved.
Keywords: Tool detection | Object detection | Data-set | Validation | Endoscopic/microscopic images
مقاله انگلیسی
9 Ship detection for visual maritime surveillance from non-stationary platforms
تشخیص کشتی برای نظارت دریایی بصری از چارچوب بدون ایستگاه -2017
This paper presents a new ship target detection algorithm to achieve efficient visual maritime surveillance from non-stationary surface platforms, e.g., buoys and ships, equipped with CCD cameras. In the proposed detector, the three main steps including horizon detection, background modeling and background subtraction, are all based on Discrete Cosine Transform (DCT). By exploiting the characteristics of DCT blocks, we simply extract the horizon line providing an important cue for sea-surface modeling. The DCT-based feature vectors are calculated as the sample input to a Gaussian mixture model which is effective in representing dynamic ocean textures, such as waves, wakes and foams. Having modeled sea regions, we perform the ship detection using background subtraction followed by foreground segmentation. Experimental results with various maritime images demonstrate that the proposed ship detection algorithm outperforms the traditional techniques in terms of both detection accuracy and real-time performance, especially for complex sea-surface background with large waves.
Keywords: Ship detection | Visual maritime surveillance | Object detection | Gaussian mixture model | Discrete cosine transform
مقاله انگلیسی
10 Causes for query answers from databases: Datalog abduction, view-updates, and integrity constraints
علیت برای پاسخ های پرس و جو از پایگاه های داده: Abduction Datalog، بروز رسانی دید، و محدودیت های یکپارچگی-2017
Causality has been recently introduced in databases, to model, characterize, and possibly compute causes for query answers. Connections between QA-causality and consistency based diagnosis and database repairs (wrt. integrity constraint violations) have already been established. In this work we establish precise connections between QA-causality and both abductive diagnosis and the view-update problem in databases, allowing us to obtain new algorithmic and complexity results for QA-causality. We also obtain new results on the complexity of view-conditioned causality, and investigate the notion of QA-causality in the presence of integrity constraints, obtaining complexity results from a connection with view-conditioned causality. The abduction connection under integrity constraints allows us to obtain algorithmic tools for QA-causality.
Keywords: Causality in databases | Abductive diagnosis | View updates | Delete propagation | Integrity constraints
مقاله انگلیسی
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