Hengfu Yang^1,2,*, Mingfang Jiang^1,2, Zhichen Gao³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5173-5189, 2023, DOI:10.32604/cmc.2023.034819

Abstract Few previous Reversible Visible Watermarking (RVW) schemes have both good transparency and watermark visibility. An adaptive RVW scheme that integrates Total Variation and visual perception in Block Truncation Coding (BTC) compressed domain, called TVB-RVW is proposed in this paper. A new mean image estimation method for BTC-compressed images is first developed with the help of Total Variation. Then, a visual perception factor computation model is devised by fusing texture and luminance characteristics. An adaptive watermark embedding strategy is used to embed the visible watermark with the effect of the visual perception factor in the BTC domain. Moreover, a lossless embedding… More >

Fayadh Alenezi^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3425-3443, 2022, DOI:10.32604/cmc.2022.023339

Abstract Image dehazing is still an open research topic that has been undergoing a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified hypotheses in order to attain… More >

Debajit Datta¹, Pramod Kumar Maurya¹, Kathiravan Srinivasan², Chuan-Yu Chang^3,*, Rishav Agarwal¹, Ishita Tuteja¹, V. Bhavyashri Vedula¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2545-2561, 2021, DOI:10.32604/cmc.2021.015478

Abstract The pandemic situation in 2020 brought about a ‘digitized new normal’ and created various issues within the current education systems. One of the issues is the monitoring of students during online examination situations. A system to determine the student’s eye gazes during an examination can help to eradicate malpractices. In this work, we track the users’ eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier. We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network (CNN) models, namely the AlexNet model and the… More >