کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
13436739 | 1843061 | 2019 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Research on de-motion blur image processing based on deep learning
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
SNRLSTMDNNPSNRGMGDBNLBPMSEANNRestricted Boltzmann MachinesELM - المLocal binary patterns - الگوهای باینری محلیMAE - بلهLTP - تقویت طولانی مدت یا LTP Long Short Term Memory - حافظه بلند مدت کوتاهExtreme learning machine - دستگاه یادگیری شدیدRBM - سربازیCNN - سی ان انDeep belief network - شبکه اعتقادی عمیقDeep neural network - شبکه عصبی عمیقArtificial Neural Network - شبکه عصبی مصنوعیConvolutional neural networks - شبکه عصبی همجوشیSVM - ماشین بردار پشتیبانیSupport vector machine - ماشین بردار پشتیبانیMean Absolute Error - میانگین خطا مطلقMean Square Error - میانگین مربع خطاsignal to noise ratio - نسبت سیگنال به نویزPeak signal to noise ratio - نسبت سیگنال به نویز بالاترینImage processing - پردازش تصویرDeep learning - یادگیری عمیق
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Research on de-motion blur image processing based on deep learning Research on de-motion blur image processing based on deep learning](/preview/png/13436739.png)
چکیده انگلیسی
In recent years, with the rapid development of computer technology and network technology, computer vision has been widely used in various scientific fields. Human motion recognition, as an important branch of computer vision, is essentially to classify human motion information in motion images correctly. It has great significance in intelligent monitoring and security, human-computer interaction, motion analysis and other fields. At present, there are still some problems in human motion recognition methods. Firstly, how to extract and characterize the motion information in images has been one of the difficulties in this field; secondly, with the appearance of kinect and other depth cameras, researchers have provided the depth information of human motion images, and how to effectively use these depth information to achieve human motion recognition and classification is also an important research issue; finally, when the amount of sample data is small, how to use the deep learning network model to achieve a higher human motion recognition rate? Based on UTD-MHAD database, this paper studies the human motion recognition of RGB image and depth image captured simultaneously by kinect, and carries out relevant discussion and analysis on the above problems, using micro-inertial sensors (MTi-G-700 developed by Xsens and Android mobile phones, tablets and other personal mobile devices come with MEMS gyroscopes and accelerometers) to correct the image to motion blur, build a new mathematical model, use the inertial data obtained by MIMU in a short time to estimate the position, attitude and speed of camera motion, correct the image pixel position, perform image de-motion blur processing, and then perform image processing such as denoising to solve the image motion blur problem. A new algorithm is developed and its science is verified by MATLAB simulation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 60, April 2019, Pages 371-379
Journal: Journal of Visual Communication and Image Representation - Volume 60, April 2019, Pages 371-379
نویسندگان
Yanfen Chang,