scholarly journals A MULTI DIVERSIFIED GUI FOR ANALYSIS OF IMAGE DENOISING USING VARIOUS PARAMETRS.

2020 ◽  
Vol 9 (12) ◽  
pp. 10-16
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Computer Vision ◽  
Image Denoising ◽  
Noise Suppression ◽  
Research Work ◽  
Visual Display ◽  
Communication Engineering ◽  
Computer Aided ◽  
Promising Area

Digital Image Processing is a promising area of research in the fields of electronics and communication engineering, consumer and entertainment electronics, control and instrumentation, biomedical instrumentation, remote sensing, robotics and computer vision and computer aided manufacturing (CAM). For a meaningful and useful processing such as image segmentation and object recognition, and to have very good visual display in applications like television, photo-phone, etc., the acquired image signal must be deblurred and made noise free. The deblurring and noise suppression (filtering) come under a common class of image processing tasks known as image restoration. This research work addresses several issues with image denoising taking into consideration several known parameters. For this purpose a GUI has been developed in Matlab which produced several research parameters.

A New Technique for Characterization of Residual Stresses in Glass Surfaces: Scratch Test Completed with Image Analysis

2008 ◽  
Vol 589 ◽  
pp. 275-280 ◽  
Author(s):  
Szilvia Szeghalmy ◽  
Péter Barkóczy ◽  
Maria Berkes Maros ◽  
Attila Fazekas ◽  
Csaba Póliska
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Residual Stresses ◽  
Research Work ◽  
Scratch Test ◽  
Glass Quality ◽  
Computer Aided ◽  
A New Technique ◽  
Glass Surfaces

Residual stresses significantly influence the strength and lifetime of the glass products, therefore their qualification and quantification during production is basically important for evaluating their probable reliability in application. The current paper aims at introducing a novel procedure of the suggested automatic glass quality test based on instrumented scratch test completed with computer aided image analysis. A special emphasis is put on the problem of limited reproducibility and reliability of the image processing, arisen in the first stage of the research work. The latest results consisting in the development of a new algorithm, providing a more reliable evaluation of the test data will be described.

scholarly journals Review And Analysis Of Computer Vision Algorithms

2021 ◽  
Vol 03 (05) ◽  
pp. 245-250
Author(s):  
Bakhtiyar Saidovich Rakhimov ◽  
◽  
Feroza Bakhtiyarovna Rakhimova ◽  
Sabokhat Kabulovna Sobirova ◽  
Furkat Odilbekovich Kuryazov ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Noise Suppression ◽  
Color Correction ◽  
Scientific Discipline ◽  
Specific Information ◽  
Two Dimensional ◽  
Image Transformation ◽  
Almost All

Computer vision as a scientific discipline refers to the theories and technologies for creating artificial systems that receive information from an image. Despite the fact that this discipline is quite young, its results have penetrated almost all areas of life. Computer vision is closely related to other practical fields like image processing, the input of which is two-dimensional images obtained from a camera or artificially created. This form of image transformation is aimed at noise suppression, filtering, color correction and image analysis, which allows you to directly obtain specific information from the processed image. This information may include searching for objects, keypoints, segments, and annexes;

Special Issue on Vision

1999 ◽  
Vol 11 (2) ◽  
pp. 87-87
Author(s):  
Shunichiro Oe ◽  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Visual Processing ◽  
Research Work ◽  
Image Understanding ◽  
Normal Vector ◽  
Automatic Welding ◽  
Special Issue ◽  
Image Sensing

The widely used term <B>Computer Vision</B> applies to when computers are substituted for human visual information processing. As Real-world objects, except for characters, symbols, figures and photographs created by people, are 3-dimensional (3-D), their two-dimensional (2-D) images obtained by camera are produced by compressing 3-D information to 2-D. Many methods of 2-D image processing and pattern recognition have been developed and widely applied to industrial and medical processing, etc. Research work enabling computers to recognize 3-D objects by 3-D information extracted from 2-D images has been carried out in artificial intelligent robotics. Many techniques have been developed and some applied practically in scene analysis or 3-D measurement. These practical applications are based on image sensing, image processing, pattern recognition, image measurement, extraction of 3-D information, and image understanding. New techniques are constantly appearing. The title of this special issue is <B>Vision</B>, and it features 8 papers from basic computer vision theory to industrial applications. These papers include the following: Kohji Kamejima proposes a method to detect self-similarity in random image fields - the basis of human visual processing. Akio Nagasaka et al. developed a way to identify a real scene in real time using run-length encoding of video feature sequences. This technique will become a basis for active video recording and new robotic machine vision. Toshifumi Honda presents a method for visual inspection of solder joint by 3-D image analysis - a very important issue in the inspection of printed circuit boards. Saburo Okada et al. contribute a new technique on simultaneous measurement of shape and normal vector for specular objects. These methods are all useful for obtaining 3-D information. Masato Nakajima presents a human face identification method for security monitoring using 3-D gray-level information. Kenji Terada et al. propose a method of automatic counting passing people using image sensing. These two technologies are very useful in access control. Yoji. Ogawa presents a new image processing method for automatic welding in turbid water under a non-preparatory environment. Liu Wei et al. develop a method for detection and management of cutting-tool wear using visual sensors. We are certain that all of these papers will contribute greatly to the development of vision systems in robotics and mechatronics.

scholarly journals Adversarial Gaussian Denoiser for Multiple-Level Image Denoising

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2998
Author(s):  
Aamir Khan ◽  
Weidong Jin ◽  
Amir Haider ◽  
MuhibUr Rahman ◽  
Desheng Wang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Computer Vision ◽  
Image Denoising ◽  
Theoretical Study ◽  
State Of The Art ◽  
Multiple Level ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Adversarial Network

Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

Study on Knowledge and Prevalence of Computer Vision Syndrome among Computer Operators in Nobel Medical College Teaching Hospital, Biratnagar, Nepal

2020 ◽  
Vol 9 (2) ◽  
pp. 45-49
Author(s):  
Pramod Sharma Gautam ◽  
Uday Chandra Prakash ◽  
Subreena Dangol
Keyword(s):  
Computer Vision ◽  
Teaching Hospital ◽  
Human Life ◽  
College Teaching ◽  
Extended Period ◽  
Visual Display ◽  
Work Place ◽  
Medical College ◽  
Computer Vision Syndrome ◽  
Department Of Ophthalmology

Background: The eye and vision related problems that results from continuous use of computers and other visual display terminals for extended period of time leads to computer vision syndrome. Due to rapid digitalization in human life, the risk of developing it has also increased in many folds. So, with an aim of determining the prevalence and level of awareness of computer vision syndrome among computer users along with their attitude and practices to prevent it, this study was conducted in the office employees who use computer for a considerable period of time. Materials and Methods: A hospital based observational descriptive study was conducted in the out-patient department of Ophthalmology in Nobel Medical College and Teaching Hospital, Biratnagar, where 105 employees working in different work stations of same institution were enrolled. A questionnaire and the clinical findings were used to collect data. Results: About 80% of the employees were using computer for about (8-11) hours per day. Prevalence of computer vision syndrome noted was (92.4%) with low level of knowledge (85.7%) about it. About 45% of them wore glasses for their refractive errors but attitude and practices in work place to prevent the bad effects of using visual display terminals were found to be lacking (53.3%). Burning sensation in the eye, headache, ocular irritation and itching and neck, shoulder or back pain were the common symptoms. Around (60-70)% of the eyes tested positive for dry eye. Conclusion: Lack of awareness of computer vision syndrome and lack of personal protective measures were associated with its high level of prevalence.  

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