Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning

Pillai, Manu S.; Chaudhary, Gopal; Khari, Manju; Crespo, Rubén González

doi:10.1007/s00500-021-05576-w

Cited by 52 publications

(8 citation statements)

References 28 publications

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“…Through voting in the Hough dimensional space and afterward choosing local maxima in an aggregate matrix, the circular contenders are created. 16 This paper will present TSHT, an algorithm based on multi-resolution segmentation and combining image preprocessing with the OHT algorithm. After image preprocessing and two-step optimization HT (TSHT), we will find the center of the PCB circular identifier.…”

Section: Introductionmentioning

confidence: 99%

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Center detection algorithm for printed circuit board circular marks based on image space and parameter space

Wang

Chen

et al. 2022

J. Electron. Imag.

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A highly efficient circle positioning algorithm, called the two-step optimization Hough transform (TSHT), based on multi-resolution segmentation is proposed to solve the problems of the offset Hough transform, namely, its large memory overhead, long time consumption, and low recognition accuracy. First, using the image feature of the printed circuit board (PCB) circular identifier, the target circle is obtained using adaptive image preprocessing, and then, images of an acceptable quality are separated by shape quality inspection to improve their robustness. Second, using effective interval sampling strategies and gradually controlling the accumulative interval of parameters, the TSHT algorithm reduces the memory overhead and quickly locates the center at the pixel level. Finally, the center at the sub-pixel level is found by the least-squares method for circle fitting. The experiments prove that TSHT, as a result of its high robustness, strong anti-noise capability, fast recognition speed, and accuracy, can be successfully applied to a vision positioning system of a solder paste printing machine.

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Section: Introductionmentioning

confidence: 99%

“…The CHT is a fundamental extracting features approach for detecting circle in defective pictures in image processing. Through voting in the Hough dimensional space and afterward choosing local maxima in an aggregate matrix, the circular contenders are created 16 …”

Section: Introductionmentioning

confidence: 99%

Center detection algorithm for printed circuit board circular marks based on image space and parameter space

Wang

Chen

et al. 2022

J. Electron. Imag.

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“…In addition, other studies 13 , 14 , 15 , 16 used different machine learning and deep learning techniques to propose various prediction models. The study 13 introduced a neutrosophic soft set decision making for stock trending analysis.…”

Section: Introductionmentioning

confidence: 99%

“…A review analysis of intrusion detection was stated by using machine learning techniques as in the work. 15 Moreover, a deep learning approach was suggested by the study 16 in purpose to improve image detection of automobile accident.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven analysis and predictive modeling on COVID‐19

Sharma

Alsmadi

Alkhawaldeh

et al. 2022

Concurrency and Computation

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The coronavirus started in China in 2019, has spread rapidly in every single country and has spread in millions of cases worldwide. This paper presents a proposed approach that involves identifying the relative impact of COVID-19 on a specific gender, the mortality rate in specific age, investigating different safety measures adopted by each country and their impact on the virus growth rate. Our study proposes data-driven analysis and prediction modeling by investigating three aspects of the pandemic (gender of patients, global growth rate, and social distancing). Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on three large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore three significant aspects of COVID-19 pandemic as gender, global growth rate, and social distancing. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. The results show a superior prediction performance comparing with the related approaches. K E Y W O R D S COVID-19, gender of patients, global growth rate, predictive modeling, social distancing 1 INTRODUCTION Coronaviruses 1 are a large family of viruses that are known to cause illness ranging from the common cold to more severe diseases such as middle east respiratory syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). A novel coronavirus (CoV) includes new species of coronavirus that was discovered in 2019 and has not been previously identified in humans. The 2019 novel coronavirus (COVID-19) pandemic appeared in Wuhan, China in December 2019 and has become a serious public health problem worldwide. This virus is linked to a large seafood and animal market and spreads from animal to human. It causes an outbreak of respiratory illness and symptoms that would be seen at least 15 days later. It is still spreading widely, and also new variants or strains are discovered occasionally. It can easily spread person to person. At this time, it's unclear how easily or sustainably this virus is spreading between people as various researches are periodically observing different behaviors of the virus. Previous investigations have shown that the SARS-CoV virus is passed on from musk cats to humans, and the MERS-CoV virus is relayed from dromedary camels to humans. COVID-19 virus is being presumed to be transmitted from bats to humans.Rapid respiratory transmission of the disease is one of the major reasons for the spread of this pandemic. Signs of infection include respiratory symptoms, fever, cough, and dyspnea. In more serious cases, the infection can cause pneumonia, severe acute respiratory syndrome, septic shock, multi-organ failure, and death. It has been also determined that men are more infected than women and it is severely affecting the adult age group with known chronic diseases. People have been advised to take preventive measures li...

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“…Compared with the traditional active contour model, the proposed model could produce effective results. Reference [30] used various gradient-based edge detectors on images. The Canny operator produced good results, but the parameters needed to be adjusted in some cases.…”

Section: Introductionmentioning

confidence: 99%

A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement

Shen¹,

Jiang²,

Teng³

2018

ICST Transactions on Scalable Information Systems

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Traditional image enhancement methods have the problems of low contrast and fuzzy details. Therefore, we propose a novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. Firstly, multi-scale convolution is used to preprocess the image. Then, we improve the traditional Laplace edge detection operator and combine it with Gauss filter. The Gaussian filter is used to smooth the image and suppress the noise, and the edge detection is processed based on the Laplace gradient edge detector. The detail image extracted by Gauss-Laplace operator and the image with brightness enhancement are linearly weighted fused to reconstruct the image with clear detail edge and strong contrast. Experiments are carried out with detailed images in different scenes. It is compared with traditional methods to verify the effectiveness of the proposed method.

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Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning

Cited by 52 publications

References 28 publications

Center detection algorithm for printed circuit board circular marks based on image space and parameter space

Center detection algorithm for printed circuit board circular marks based on image space and parameter space

Data‐driven analysis and predictive modeling on COVID‐19

A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement

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