Post-COVID-19: Deep Image Processing AI to Analyze Social Distancing in a Human Community

2021 ◽  
pp. 59-68
Author(s):  
Francis Class-Peters ◽  
Wilfried Yves Hamilton Adoni ◽  
Tarik Nahhal ◽  
Abdeltif EL Byed ◽  
Moez Krichen ◽  
...  
Keyword(s):  
Image Processing ◽  
Social Distancing ◽  
Human Community ◽  
Deep Image

scholarly journals Social Distancing Detector using YOLO v3 Image Processing Algorithm

2021 ◽  
pp. 783-787
Author(s):  
Ms. Kavita S. Kumavat ◽  
Aman Kumar Sao ◽  
Harish Khedekar ◽  
Chirag Panpaliya ◽  
Shantanu Korde
Keyword(s):  
Image Processing ◽  
Public Awareness ◽  
Image Processing Algorithm ◽  
Social Distancing ◽  
Detection Model ◽  
Model Object ◽  
Learning Framework ◽  
The Social ◽  
Feasible Option ◽  
Bounding Boxes

The lack of public awareness and negligence, the pandemic due to coronavirus(covid19) has brought a global crisis with its deadly spread to more than 180 countries, and about 147 million confirmed cases along with 3.11 million deaths globally as of 26th April 2021. Due to the absence of the vaccine against the covid19 the world has suffered a lot. Though scientists have developed several vaccines then also the pandemic is still out of control so therefore the only feasible option available to us is social distancing. And this notion motivated us to bring up the idea of a social distancing detector using image processing which includes a deep learning framework for automation task monitoring. The framework utilizes the YOLO v3 model object detection model to separate moving people from the background and to detect people by using bounding boxes. The basic idea of this article is to analyze the social distancing violation index rate that how many people violate the rule of social distancing in a particular interval of time.

scholarly journals Regularization by Architecture: A Deep Prior Approach for Inverse Problems

2019 ◽  
Vol 62 (3) ◽  
pp. 456-470 ◽  
Author(s):  
Sören Dittmer ◽  
Tobias Kluth ◽  
Peter Maass ◽  
Daniel Otero Baguer
Keyword(s):  
Image Processing ◽  
Inverse Problems ◽  
Linear Operators ◽  
Experimental Results ◽  
Image Prior ◽  
Ill Posed ◽  
Deep Image ◽  
Theoretical Results

Abstract The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems. DIP networks have been recently introduced for applications in image processing; also first experimental results for applying DIP to inverse problems have been reported. This paper aims at discussing different interpretations of DIP and to obtain analytic results for specific network designs and linear operators. The main contribution is to introduce the idea of viewing these approaches as the optimization of Tikhonov functionals rather than optimizing networks. Besides theoretical results, we present numerical verifications.

scholarly journals Social Distancing Detector using Image Processing

2021 ◽  
pp. 542-545
Author(s):  
Aman Kumar Sao ◽  
Harish Khedekar ◽  
Chirag Panpaliya ◽  
Shantanu Korde ◽  
Ms. Kavita S. Kumavat
Keyword(s):  
Image Processing ◽  
Public Awareness ◽  
Social Distancing ◽  
Detection Model ◽  
Model Object ◽  
Learning Framework ◽  
The Social ◽  
The World ◽  
Feasible Option ◽  
Bounding Boxes

The lack of public awareness and negligence, the pandemic due to coronavirus(covid19) has brought a global crisis with its deadly spread to more than 180 countries, and about 147 million confirmed cases along with 3.11 million deaths globally as of 26th April 2021. Due to the absence of the vaccine against the covid19 the world has suffered a lot. Though scientists have developed several vaccines then also the pandemic is still out of control so therefore the only feasible option available to us is social distancing. And this notion motivated us to bring up the idea of a social distancing detector using image processing which includes a deep learning framework for automation task monitoring. The framework utilizes the YOLO v3 model object detection model to separate moving people from the background and to detect people by using bounding boxes. The basic idea of this article is to analyze the social distancing violation index rate that how many people violate the rule of social distancing in a particular interval of time.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

The 'well-known' theory of electron image formation

1983 ◽  
Vol 41 ◽  
pp. 288-289
Author(s):  
M.A. O'Keefe ◽  
W.O. Saxton
Keyword(s):  
Image Processing ◽  
Image Formation ◽  
Source Profiles ◽  
Electron Image

A recent paper by Kirkland on nonlinear electron image processing, referring to a relatively new textbook, highlights the persistence in the literature of calculations based on incomplete and/or incorrect models of electron imageing, notwithstanding the various papers which have recently pointed out the correct forms of the appropriate equations. Since at least part of the problem can be traced to underlying assumptions about the illumination coherence conditions, we attempt to clarify both the assumptions and the corresponding equations in this paper, illustrating the effects of an incorrect theory by means of images calculated in different ways.The first point to be made clear concerning the illumination coherence conditions is that (except for very thin specimens) it is insufficient simply to know the source profiles present, i.e. the ranges of different directions and energies (focus levels) present in the source; we must also know in general whether the various illumination components are coherent or incoherent with respect to one another.

Recent Applications of High Resolution Electron Microscopy to Crystalline Arrays of Virus Particles

1978 ◽  
Vol 36 (3) ◽  
pp. 470-482 ◽  
Author(s):  
R.W. Horne
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Analytical Techniques ◽  
Staining Technique ◽  
High Resolution Electron Microscopy ◽  
Optical Diffraction ◽  
Full Potential ◽  
Virus Particles ◽  
Resolution Electron ◽  
Wide Range

The technique of surrounding virus particles with a neutralised electron dense stain was described at the Fourth International Congress on Electron Microscopy, Berlin 1958 (see Home & Brenner, 1960, p. 625). For many years the negative staining technique in one form or another, has been applied to a wide range of biological materials. However, the full potential of the method has only recently been explored following the development and applications of optical diffraction and computer image analytical techniques to electron micrographs (cf. De Hosier & Klug, 1968; Markham 1968; Crowther et al., 1970; Home & Markham, 1973; Klug & Berger, 1974; Crowther & Klug, 1975). These image processing procedures have allowed a more precise and quantitative approach to be made concerning the interpretation, measurement and reconstruction of repeating features in certain biological systems.

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