Visual Computing Unified Application Using Deep Learning and Computer Vision Techniques

被引：0

作者：

Sowmya, B.J. ^{[1
]}

Meeradevi ^{[2
]}

Seema, S. ^{[3
]}

Dayananda, P. ^{[4
]}

Supreeth, S. ^{[5
]}

Shruthi, G. ^{[5
]}

Rohith, S. ^{[6
]}

机构：

[1] Department of Artificial, Intelligence and Data Science, Ramaiah Institute of Technology, Karnataka, Bengaluru, India

[2] Department of Artificial, Intelligence and Machine Learning, Ramaiah Institute of Technology, Karnataka, Bengaluru, India

[3] Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Karnataka, Bengaluru, India

[4] Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Karnataka, Manipal, India

[5] School of Computer Science and Engineering, REVA University, Karnataka, Bengaluru, India

[6] Department of Electronics & Communication Engineering, Nagarjuna College of Engineering & Technology, Karnataka, Bengaluru, India

来源：

International Journal of Interactive Mobile Technologies | 2024年 / 18卷 / 01期

关键词：

Computer vision - Convolution - Convolutional neural networks - Deep learning - Generative adversarial networks - Learning algorithms - Learning systems - Video signal processing;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. Computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

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页码：59 / 74