Image Enhancement Techniques: An Exhaustive Review

Dubey, Shradha; Dixit, Manish

doi:10.1007/978-3-030-44758-8_34

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…The histogram of the final image will be as flat as practically possible when employing this method, while the general form of the histogram will be preserved when utilizing histogram stretching. Although the black pixels in a photograph cannot appear any darker than they currently are, the histogram can be spread or flattened to make the dark pixels appear darker and the light pixels appear lighter (the important word being "appear").Instead, if the somewhat brighter ones become substantially lighter, the black pixels will appear darker [41], [42].…”

Section: C) Histogram Equalization Methodsmentioning

confidence: 99%

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Utilizing Machine Learning and Computer Vision for the Detection of Abusive Behavior in IoT Systems

2023

IJIES

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Law enforcement and civilian protection are increasingly dependent on surveillance technologies. To prevent social, economic, and environmental harm, video surveillance situations like those in train stations, schools, and hospitals must automatically detect aggressive and suspicious behavior. Intelligent video surveillance systems now enable human interaction monitoring. Despite its security benefits, crowds and the camera's vision make it difficult to distinguish regular from abnormal activity. Therefore, there is a great deal of investigation into the many methods of identifying violent behavior. The detection approach presented in this research can be broken down into three distinct subfields. The classification methods employed led to the formation of these classes. Classifications include Gradient Descent (SGD), Adaptive boosting (ADA), Naive Base (NB), and Logistic Regression (LR) for detecting violent acts with machine learning. Methods for feature extraction and violence detection are derived using Principal Component Analysis (PCA). Dataset learning-based models for violence detection can also be evaluated using the AIRTLab dataset, a model developed to test the robustness of algorithms against false positives. In order to determine if the proposed model's resistance to false positives, accuracy (ACC) metrics were computed on the proposed model and used to set a benchmark for the AIRTLab dataset. According to the research literature, the proposed models are accurate of 98.31% with the logistic regression classifier having superior generalization skills.

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Section: C) Histogram Equalization Methodsmentioning

confidence: 99%

“…where, ℎ[𝑖]: the result value of the histogram equalization, M and N: grayscale image dimension [42]. Histogram-induced image distortions are depicted in Fig.…”

Section: C) Histogram Equalization Methodsmentioning

confidence: 99%

Utilizing Machine Learning and Computer Vision for the Detection of Abusive Behavior in IoT Systems

2023

IJIES

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“…This results in a slight increase in brightness for dark pixels and a significant increase for bright pixels. The procedure for histogram equalization in digital images involves four steps, as outlined in [26].…”

Section: Histogram Equalizationmentioning

confidence: 99%

Detection of arabic sign language by machine learning techniques with PCA and LDA

Hassan,

Sabri,

Ali

2024

ETJ

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An automatic system translated Arabic Sign Language to improve deaf-hearing communication. • KNN algorithm had 86.4% accuracy in recognizing 32 Arabic sign language letters. • The system bridges the communication gap between sign language users and non-users. People need a communication channel and a way to communicate with a person or group. Deaf people communicate with others through sign language. The remarkable and rapid development in image and video recognition systems has made researchers use this development to solve many problems, including sign language for the deaf, and reduce their suffering in communicating with ordinary people. This work aims to use and apply machine learning technology to build an automatic recognition system for Arabic Sign Language (ArSL). In this work, images of ArSL characters were recognized using four classification techniques (Naïve Bayes (NB), Decision Trees (DTs), and Adaptive Boosting), and K-Nearest Neighbor (KNN)) with the Python library and using two feature extraction algorithms (PCA & LDA). Data pre-processing steps, including grayscale conversion, Gaussian blur, histogram equalization, and resizing, are applied to enhance the data's suitability for training and testing. The work was tested with five experiments chosen with multiple ratios for training and test data. The first training is 90%, the second training is 80% of the data, the third is 75%, the fourth is 70%, and the last is 60%. The work also played a good role in interpreting ArSL, and the accuracy of the work considers the KNN algorithm more accurate in prediction.

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Image Enhancement Techniques: An Exhaustive Review

Cited by 2 publications

References 4 publications

Utilizing Machine Learning and Computer Vision for the Detection of Abusive Behavior in IoT Systems

Utilizing Machine Learning and Computer Vision for the Detection of Abusive Behavior in IoT Systems

Detection of arabic sign language by machine learning techniques with PCA and LDA

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