Application of Deep Learning Methods for Multimedia

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 1683

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Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), 10223 Vilnius, Lithuania
Interests: machine learning; real-time signal processing; image analysis; object detection and tracking
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J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: intelligent systems; neural networks; cyber-security; visualization and simulation
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Special Issue Information

Dear Colleagues,

As an important branch of presentation learning, deep learning has wide application value. It is usually based on multi-layer neural networks, which can extract effective features from large-scale data to represent the data, thus improving the performance of machine learning algorithms.

In recent years, deep learning has made great achievements in image processing, speech recognition, and natural language processing, and as a result, it has been widely used in multimedia. In this Special Issue, we invite researchers to present their original research articles or reviews of the recent literature related to deep learning applications in image recognition, image processing, natural language comprehension, power saving, the military field, medical field, intelligent manufacturing field, and so on.

Prof. Dr. Artūras Serackis
Prof. Dr. Adel S. Elmaghraby
Prof. Dr. Begoña Garcia-Zapirain
Guest Editors

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Published Papers (1 paper)

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Research

18 pages, 2459 KiB  
Article
Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network
by Mantas Tamulionis, Tomyslav Sledevič, Vytautas Abromavičius, Dovilė Kurpytė-Lipnickė, Dalius Navakauskas, Artūras Serackis and Dalius Matuzevičius
Appl. Sci. 2023, 13(3), 1264; https://doi.org/10.3390/app13031264 - 17 Jan 2023
Cited by 4 | Viewed by 1198
Abstract
Taking smartphone-made videos for photogrammetry is a convenient approach because of the easy image collection process for the object being reconstructed. However, the video may contain a lot of relatively similar frames. Additionally, frames may be of different quality. The primary source of [...] Read more.
Taking smartphone-made videos for photogrammetry is a convenient approach because of the easy image collection process for the object being reconstructed. However, the video may contain a lot of relatively similar frames. Additionally, frames may be of different quality. The primary source of quality variation in the same video is varying motion blur. Splitting the sequence of the frames into chunks and choosing the least motion-blurred frame in every chunk would reduce data redundancy and improve image data quality. Such reduction will lead to faster and more accurate reconstruction of the 3D objects. In this research, we investigated image quality evaluation in the case of human 3D head modeling. Suppose a head modeling workflow already uses a convolutional neural network for the head detection task in order to remove non-static background. In that case, features from the neural network may be reused for the quality evaluation of the same image. We proposed a motion blur evaluation method based on the LightGBM ranker model. The method was evaluated and compared with other blind image quality evaluation methods using videos of a mannequin head and real faces. Evaluation results show that the developed method in both cases outperformed sharpness-based, BRISQUE, NIQUE, and PIQUE methods in finding the least motion-blurred image. Full article
(This article belongs to the Special Issue Application of Deep Learning Methods for Multimedia)
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