دانلود و نمایش مقالات مرتبط با Computer vision application::صفحه 1
بلافاصله پس از پرداخت دانلود کنید

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - Computer vision application

تعداد مقالات یافته شده: 11
ردیف عنوان نوع
1 Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application
سیستم سخت‌افزار-نرم‌افزار روی تراشه مبتنی بر شبکه‌های عصبی عمیق برای کاربرد بینایی ماشین-2022
Embedded vision systems are the best solutions for high-performance and lightning-fast inspection tasks. As everyday life evolves, it becomes almost imperative to harness artificial intelligence (AI) in vision applications that make these systems intelligent and able to make decisions close to or similar to humans. In this context, the AI’s integration on embedded systems poses many challenges, given that its performance depends on data volume and quality they assimilate to learn and improve. This returns to the energy consumption and cost constraints of the FPGA-SoC that have limited processing, memory, and communication capacity. Despite this, the AI algorithm implementation on embedded systems can drastically reduce energy consumption and processing times, while reducing the costs and risks associated with data transmission. Therefore, its efficiency and reliability always depend on the designed prototypes. Within this range, this work proposes two different designs for the Traffic Sign Recognition (TSR) application based on the convolutional neural network (CNN) model, followed by three implantations on PYNQ-Z1. Firstly, we propose to implement the CNN-based TSR application on the PYNQ-Z1 processor. Considering its runtime result of around 3.55 s, there is room for improvement using programmable logic (PL) and processing system (PS) in a hybrid architecture. Therefore, we propose a streaming architecture, in which the CNN layers will be accelerated to provide a hardware accelerator for each layer where direct memory access (DMA) interface is used. Thus, we noticed efficient power consumption, decreased hardware cost, and execution time optimization of 2.13 s, but, there was still room for design optimizations. Finally, we propose a second co-design, in which the CNN will be accelerated to be a single computation engine where BRAM interface is used. The implementation results prove that our proposed embedded TSR design achieves the best performances compared to the first proposed architectures, in terms of execution time of about 0.03 s, computation roof of about 36.6 GFLOPS, and bandwidth roof of about 3.2 GByte/s.
keywords: CNN | FPGA | Acceleration | Co-design | PYNQ-Z1
مقاله انگلیسی
2 Barriers to computer vision applications in pig production facilities
موانع برنامه های بینایی کامپیوتری در تاسیسات تولید خوک-2022
Surveillance and analysis of behavior can be used to detect and characterize health disruption and welfare status in animals. The accurate identification of changes in behavior is a time-consuming task for caretakers in large, commercial pig production systems and requires strong observational skills and a working knowledge of animal husbandry and livestock systems operations. In recent years, many studies have explored the use of various technologies and sensors to assist animal caretakers in monitoring animal activity and behavior. Of these technologies, computer vision offers the most consistent promise as an effective aid in animal care, and yet, a systematic review of the state of application of this technology indicates that there are many significant barriers to its widespread adoption and successful utilization in commercial production system settings. One of the most important of these barriers is the recognition of the sources of errors from objective behavior labeling that are not measurable by current algorithm performance evaluations. Additionally, there is a significant disconnect between the remarkable advances in computer vision research interests and the integration of advances and practical needs being instituted by scientific experts working in commercial animal production partnerships. This lack of synergy between experts in the computer vision and animal health and production sectors means that existing and emerging datasets tend to have a very particular focus that cannot be easily pivoted or extended for use in other contexts, resulting in a generality versus particularity conundrum. This goal of this paper is to help catalogue and consider the major obstacles and impediments to the effective use of computer vision associated technologies in the swine industry by offering a systematic analysis of computer vision applications specific to commercial pig management by reviewing and summarizing the following: (i) the purpose and associated challenges of computer vision applications in pig behavior analysis; (ii) the use of computer vision algorithms and datasets for pig husbandry and management tasks; (iii) the process of dataset construction for computer vision algorithm development. In this appraisal, we outline common difficulties and challenges associated with each of these themes and suggest possible solutions. Finally, we highlight the opportunities for future research in computer vision applications that can build upon existing knowledge of pig management by extending our capability to interpret pig behaviors and thereby overcome the current barriers to applying computer vision technologies to pig production systems. In conclusion, we believe productive collaboration between animal-based scientists and computer-based scientists may accelerate animal behavior studies and lead the computer vision technologies to commercial applications in pig production facilities.
keywords: بینایی کامپیوتر | دامپروری دقیق | رفتار - اخلاق | یادگیری عمیق | مجموعه داده | گراز | Computer vision | Precision livestock farming | Behavior | Deep learning | Dataset | Swine
مقاله انگلیسی
3 Field-programmable gate arrays in a low power vision system
آرایه های دروازه ای قابل برنامه ریزی در یک سیستم دید کم قدرت-2021
In recent years, field-programmable gate arrays have played a major role in developing low power electronic systems. End users usually prefer systems with high performance, reduced size, and low power consumption. These requirements create a challenging task for designers. Re-configuring technology allows the use of field-programmable gate arrays to be at the maximum level during runtime. This paper proposes the implementation of the Dynamic Partial Reconfiguration technique to switch during runtime between two edge detection algorithms (FASTX and Sobel) in a computer vision algorithm. Xilinx Ultrascale+ZCU106 has been used as the implementation target since it consumes approximately 4% less power during runtime. It was discovered that the dynamic switching between algorithms reduces the on-chip area utilization. Finally, through experimental results our proposed work has demonstrated the applicability of computer vision with low power consumption.
Keywords: Ultrascale | Low power | Computer vision application | Dynamic partial reconfiguration
مقاله انگلیسی
4 Computer vision technologies for safety science and management in construction: A critical review and future research directions
فناوری های بینایی رایانه ای برای علم ایمنی و مدیریت در ساخت و ساز: مروری انتقادی و جهت تحقیقات آینده-2021
Recent years have seen growing interests in developing and applying computer vision technologies to solve safety problems in the construction industry. Despite the technological advancements, there is no research that exams the theoretical links between computer vision technology and safety science and management. Thus, the ob- jectives of this paper are to: (1) investigate the current status of applying computer vision technology to con- struction safety, (2) examine the links between computer vision applications and key research themes of construction safety, (3) discuss the theoretical challenges of applying computer vision to construction safety, and(4) recommend future research directions. A five-step review approach was adopted to search and analyze peer- reviewed academic journal articles. A three-level computer vision development framework was proposed to categorized computer vision applications in the construction industry. The links between computer vision and three main safety research traditions: safety management system, behavior-based safety program, and safety culture, were discussed. The results suggest that the majority of past efforts were focused on object recognition, object tracking, and action recognition, with limited research focused on recognizing unsafe behavior. There are even fewer studies aimed at developing vision-based safety assessment and prediction systems. Based on the review findings, four future research directions are suggested: (1) develop and test a behavioral-cues-based safety climate measure, (2) develop safety behavior datasets, (3) develop a formal hazard identification and assessment model, and (4) develop criteria to evaluate the real impacts of vision-based technologies on safety performance.
Keywords: Computer vision | Construction health and safety | Safety science | Safety culture | Safety Climate, Hazard | Safety management system | Digital technologies | Automation
مقاله انگلیسی
5 Color Image Enhancement based on Gamma Encoding and Histogram Equalization
بهبود تصویر رنگی بر اساس رمزگذاری گاما و یکسان سازی هیستوگرام-2021
Image Enhancement is used as a preprocessing step in many computer vision applications. It provides enhanced input for other computerized image processing methods. Many preprocessing techniques can be applied to images depending on the application domain. In this paper we are proposing an image enhancement technique for color images that can be used as preprocessing step in many computer vision applications. It can also be used as a data augmentation technique in object detection. Luminance component of images is sometimes not captured by cameras and displayed by monitors properly. To remove this drawback of devices we have used gamma encoding. Four different values of gamma are evaluated depending on the quality of images. Image is then converted into YUV Color space. Y component represents the luminance. U and V components represent color. After that Contrast Limited Adaptive Histogram Equalization is applied to the Y component to improve the contrast of the image. The results are compared with the state-of-the-art methods on the basis of Peak Signal to noise Ratio (PSNR) and Mean Square Error (MSE). Quantitative results show that proposed algorithm results in improved value of PSNR and decreased value of MSE as compared to existing methods. Qualitative comparison is also done and results show improvement over the existing techniques.© 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials, Manufacturing and Mechanical Engineering for Sustainable Developments-2020.
Keywords: Histogram | Intensity | Luminance | Contrast stretching
مقاله انگلیسی
6 Accelerated Computer Vision Inference with AI on the Edge
استنتاج چشم انداز رایانه ای سریع با هوش مصنوعی در لبه-2020
Computer vision is not just about breaking down images or videos into constituent pixels, but also about making sense of those pixels and comprehending what they represent. Researchers have developed some brilliant neural networks and algorithms for modern computer vision. Tremendous developments have been observed in deep learning as computational power is getting cheaper. But data-driven deep learning and cloud computing based systems face some serious limitations at edge devices in real-world scenarios. Since we cannot bring edge devices to the data-centers, so we bring AI to the edge devices with AI on the Edge. OpenVINO toolkit is a powerful tool that facilitates deployment of high-performance computer vision applications to the edge devices. It converts existing applications into hardwarefriendly and inference-optimized deployable runtime packages that operate seamlessly at the edge. The goals of this paper are to describe an in-depth survey of problems faced in existing computer vision applications and to present AI on the Edge along with OpenVINO toolkit as the solution to those problems. We redefine the workflow for deploying computer vision systems and provide an efficient approach for development and deployment of edge applications. Furthermore, we summarize the possible works and applications of AI on the Edge in future in regard to security and privacy.
Index Terms: Artificial Intelligence | Deep Learning | Neural Networks | Computer Vision | AI on the Edge | OpenVINO
مقاله انگلیسی
7 Learning in the machine: To share or not to share?
یادگیری در دستگاه: برای به اشتراک گذاشتن یا عدم اشتراک گذاری؟-2020
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization approach, it is not a necessity in computer vision applications. Furthermore, Free Convolutional Networks match the performance observed in standard architectures when trained using properly translated data (akin to video). Under the assumption of translationally augmented data, Free Convolutional Networks learn translationally invariant representations that yield an approximate form of weight-sharing.
Keywords: Deep learning | Convolutional neural networks | Weight-sharing | Biologically plausible architectures
مقاله انگلیسی
8 Automated pig counting using deep learning
شمارش خودکار خوک با استفاده از یادگیری عمیق-2019
Pig counting is one of the most critical topics in farming management and asset estimation. Due to its complexity, traditional agriculture method relies on manual counting, which is obviously inefficient and a waste of manpower. The challenging aspects like partial occlusion, overlapping and different perspectives even limit the usage of traditional computer vision techniques. In recent years, deep learning has become more and more popular for computer vision applications, because of its superior performance comparing to traditional methods. In this paper, we propose a deep learning solution to address the pig counting problem. We present a modified Counting Convolutional Neural Network (Counting CNN) model according to the structure of ResNeXt, and tune a series of experimental parameters. Our CNN model learns the mapping from the image feature to the density map, and obtains the total number of pigs in the entire image by integrating the density map. In order to validate the efficacy of our proposed method, we conduct experiments on a real-world dataset collected from actual piggery farming with 15 pigs in an image averagely. We achieve 1.67 Mean Absolute Error (MAE) per image and outperforms the competing algorithms, which strongly demonstrates that our proposed method can accurately estimate the number of pigs even if they are partially occluded in different perspectives. The detection speed, 42 ms per image, meets the requirements of agricultural application. We share our code and the first pig dataset we collected for pig counting at https://github.com/xixiareone/counting-pigs for livestock husbandry and science research community.
Keywords: Deep learning | Pigs counting | Automatic
مقاله انگلیسی
9 پردازش تصویر میدان نوری: یک مرور کلی
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 29 - تعداد صفحات فایل doc فارسی: 79
عکس برداری میدان نوری به عنوان یک فناوری پدید آمده است که اجازه به دست آوردن اطلاعات غنی تری از جهان ما را می دهد. برخلاف عکس برداری سنتی که یک تصویر دوبُعدی نوری از صحنه می گیرد و محدوده زاویه ای را نیز وارد می کند، میدان های نوری، تابندگی را از اشعه ها در همه جهت ها جمع آوری می کنند که این کار اطلاعات زاویه ای از دست رفته در فناوری سنتی را تقسیم می کند. از یک سو، این نمایش بُعدی بالاتر داده های بصری، قابلیت های قدرتمندی را برای درک صحنه ارائه می کند و متعاقبا" عملکرد مسائل سنتی دیدِ رایانه ای مثل حس عمق، متمرکز کردن مجدد پس از عکس گرفتن، بخش بندی، پایدارسازی ویدیو، دسته بندی ماده و غیره را بهبود می بخشد. از سوی دیگر، بُعدی بودن بالای میدان های نوری همچنین چالشهای جدیدی را ازنظر به دست آوردن داده، فشرده سازی داده، ویرایش محتوا و نمایش آنها به بار می آورد. تحقیقات در حوزه پردازش تصویر میدان نوری با کنارهم قرار دادن این دو مولفه به صورت روزافزونی در دیدِ رایانه ای، تصاویر رایانه ای و حوزه های پردازش سیگنال به محبوبیت و عمومیت دست یافته است. در این مقاله، ما یک مرور کلی و جامع انجام می دهیم و روی تحقیقات حوزه میدان نوری درطی 20 سال گذشته بحث می کنیم. ما روی همه جنبه های پردازش تصویر میدان نوری شامل نمایش پایه ای میدان نوری و تئوری آن، اکتساب داده ها، دقت بسیار بالا، تخمین عمق، فشرده سازی، ویرایش، الگوریتمهای پردازش برای نمایش میدان نوری و کاربردهای داده های میدان نوری در دیدِ رایانه ای تمرکز می کنیم.
کلیدواژه ها: تصویربرداری میدان نوری
مقاله ترجمه شده
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
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 5659 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 5659 :::::::: افراد آنلاین: 69