Low Light Image Enhancement for Dark Images
Akshay Patil,
Tejas Chaudhari,
Ketan Deo,
Kalpesh Sonawane,
Rupali Bora
Issue:
Volume 6, Issue 4, August 2020
Pages:
99-104
Received:
10 May 2020
Accepted:
25 May 2020
Published:
7 September 2020
Abstract: Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness. Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. To improve image quality, these low-light images are needed to be enhanced. For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed. Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.
Abstract: Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of ...
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A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election
Bon-A Koo,
Jana Choe,
Yeseo Kim
Issue:
Volume 6, Issue 4, August 2020
Pages:
105-112
Received:
19 August 2020
Accepted:
3 September 2020
Published:
10 September 2020
Abstract: The recent death of George Floyd once again reminded the Americans of the chronic racial bias when it comes to police using force during an encounter with an alleged criminal or, in some cases, innocent civilians, and promulgated Black Lives Matter (BLM) movements in the United States. In order to verify such police use of excessive force against a particular racial group, we examined datasets regarding cases of police killings, which were collected from 50 states (and Washington, D. C. separately) across the country. To find out the possible factors that might cause frequent police killings against a particular racial group, we analyzed relevant datasets, observing each state’s demographics, political ideology, education level, and the frequency of police deaths in respect to each state’s frequency of police killings. Although we found numerous factors that might lead such trends in police violence, we discovered a correlation between a state’s political ideology and the frequency of police killings of a particular racial group in the corresponding state. In response to such trends, we evaluated the correlation between each state’s prevalence of police killings and its presidential election outcome in 2016. Using two machine learning methods, random forest and logistic regression, we further predicted each state’s prospective preference toward a particular candidate (Republican or Democrat) and the election outcome of the 2020 presidential election.
Abstract: The recent death of George Floyd once again reminded the Americans of the chronic racial bias when it comes to police using force during an encounter with an alleged criminal or, in some cases, innocent civilians, and promulgated Black Lives Matter (BLM) movements in the United States. In order to verify such police use of excessive force against a...
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Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach
Jeremiah Kiingati,
Samuel Mwalili,
Anthony Waititu
Issue:
Volume 6, Issue 4, August 2020
Pages:
113-119
Received:
17 August 2020
Accepted:
5 September 2020
Published:
19 September 2020
Abstract: In this paper we apply sequential Bayesian approach to compare the outcome of the presidential polls in Kenya. We use the previous polls to form the prior for the current polls. Even though several authors have used non-Bayesian models for countrywide polling data to forecast the outcome of the presidential race we propose a Bayesian approach in this case. As such the question of how to treat the previous and current pre-election polls data is inevitable. Some researchers consider only the most recent poll others Combine all previous polls up the present time and treat it as a single sample, weighting only by sample size, while others Combine all previous polls but adjust the sample size according to a weight function depending on the day the poll is taken. In this paper we apply a sequential Bayesian model (as an advancement of the latter which is time sensitive) where the previous measure is used as the prior of the current measure. Our concern is to model the proportion of votes between two candidates, incumbent and challenger. A Bayesian model of our binomial variable of interest will be applied sequentially to the Kenya opinion poll data sets in order to arrive at a posterior probability statement. The simulation results show that the eventual winner must lead consistently and constantly in at least 60% of the opinions polls. In addition, a candidate demonstrating high variability is more likely to lose the polls.
Abstract: In this paper we apply sequential Bayesian approach to compare the outcome of the presidential polls in Kenya. We use the previous polls to form the prior for the current polls. Even though several authors have used non-Bayesian models for countrywide polling data to forecast the outcome of the presidential race we propose a Bayesian approach in th...
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