Contrast Enhancement in Mammograms Using Convolution Neural Networks for Edge Computing Systems

Hashmi, Adeel; Juneja, Abhinav; Kumar, Naresh; Gupta, Deepali; Turabieh, Hamza; Dhingra, Grima; Jha, Ravi Shankar; Bitsue, Zelalem Kiros

doi:10.1155/2022/1882464

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…Cubic SVM provides the highest efficiency of 0.99. Several machine learning approaches as described in [29][30][31][32][33][34][35][36][37] can be utilized in a similar way. Authors in [38] proposed a classroom activity detection approach using video surveillance.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Gupta

Agarwal

2023

Eng. Technol. Appl. Sci. Res.

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Over the past few years, there has been a noticeable growth in the use of video surveillance systems, frequently functioning as integrated systems that remotely monitor key locations. In order to prevent terrorism, theft, accidents, illegal parking, vandalism, fighting, chain snatching, and crime, human activities can be observed through visual surveillance in sensitive and public places like buses, trains, airports, banks, shopping centers, schools, and colleges. In this paper, a review of the state-of-the-art is provided, showing the overall development of identifying suspicious behavior from surveillance recordings over the past few years. We give a quick overview of the issues and difficulties associated with recognizing suspicious human activity. The purpose of this publication is to give this field's scholars a literature evaluation of several suspicious activity recognition systems along with their general structure.

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Section: Literature Reviewmentioning

confidence: 99%

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Gupta

Agarwal

2023

Eng. Technol. Appl. Sci. Res.

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“…MobileNetV3-Large is more accurate than ImageNet with less latency than MobileNetV2 [10]. In [16][17][18][19][20][21][22][23][24][25][26][27] one can review some machine learning models that show its importance and improved results.…”

Section: A Modelsmentioning

confidence: 99%

Suspicious Activity Classification in Classrooms using Deep Learning

Gupta,

Agarwal

2023

Eng. Technol. Appl. Sci. Res.

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Video processing is attracting the attention of both research and industry. The existence of intelligent surveillance cameras with high processing power has paved the way for designing intelligent visual surveillance systems. Along with analyzing video for information recovery, it is nowadays used to analyze live surveillance video to detect activities. These systems are implemented in real time. The proposed work's goal is to create a method that can examine and discover suspicious behaviors in the lecture room environment. Video analytics offers the most efficient answer because it enables pointing an occasion and retrieves applicable statistics from the video recorded. The method aims to identify suspicious activities like fighting, sleeping, looking elsewhere, eating, etc. that the students might be doing. The proposed method involves breaking a video input into frames and converting it into image data because the model has been trained on images collected from the internet. Several models were tested and experimented with, including efficientnet_b2, spnasnet_100, efficientnet_b3, and mobilenetv3_large_100. Parameters such as the Learning Rate were optimized to find out the best method and create a system with the best results.

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“…Numerous primary picture-enhancing techniques and their comparison analyses of the implied methodologies are briefed [10][11][12][13][14][15][16][17][18][19][20]. The other studies [21][22][23][24] presents a transfer learning method based on convolution neural networks to improve mammographic picture contrast. The findings reveal that the suggested technique beats the other strategies for enhancing low-contrast photos on various real time applications.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Gabor Filtering and Adaptive Histogram Equalization Method for Improving Images

Gowda A B,

Kumar S

et al. 2023

IJRITCC

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The correct information may only sometimes be effectively conveyed by images due to various factors, such as excessively bright or dark lighting and low or high contrast. As a result, picture improvement has become an essential part of digital image processing. This proposed method aims to develop an algorithm for improving photos captured in dark environments. This letter presents a new picture-enhancing approach that combines median and Gabor filtering using the wavelet domain with histogram equalization working over a spatial domain. The proposed method in this paper combines spatial and transformed domains for image enhancement and has been simulated using MATLAB. The simulation results of two different photos show that the suggested approach extends the histogram over a wide range of grayscale, offering a superior improvement to the original image. The novel proposed algorithm aims to improve image quality and visibility, making identifying essential details within the image easier. Further, the proposed technique's success is manifested by examining the produced photos' contrast and brightness. The findings reveal that the suggested technique beats the other strategies for improving low-contrast photos.

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Contrast Enhancement in Mammograms Using Convolution Neural Networks for Edge Computing Systems

Cited by 6 publications

References 35 publications

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Suspicious Activity Classification in Classrooms using Deep Learning

A Novel Gabor Filtering and Adaptive Histogram Equalization Method for Improving Images

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