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Deep Signal/Image Processing: Applications and New Algorithms

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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 (10 October 2021) | Viewed by 14132

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Special Issue Editors

Prof. Dr. Emilio Soria-Olivas

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Guest Editor

IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: deep learning applications
Special Issues, Collections and Topics in MDPI journals

Dr. Joan Vila-Francés

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Guest Editor

IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: NLP applications
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Dr. Juan Gómez-Sanchís

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Guest Editor

IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: AI applications in agriculture
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Dr. Fernando Mateo Jiménez

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Guest Editor

IDAL. Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: neural networks; data mining; signal processing; deep learning; time series forecast; variable selection

Prof. Dr. Marcelino Martínez Sober

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IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: artificial intelligence applications
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Dr. Antonio José Serrano López

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IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: medical AI applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to contribute to a Special Issue of the journal Applied Sciences, “Deep Signal/Image Processing: Applications and New Algorithms”, which aims to present recent developments in the field of signal processing using advanced techniques. This Special Issue aims to provide a varied and complementary collection of contributions that showcase new developments and applications using advanced machine/deep learning techniques in the field of signal processing. The ultimate goal is to promote research and advancement in this field by publishing high quality research articles and reviews in this rapidly growing interdisciplinary field.

Topics of interest include, but are not limited to, the following applications of deep learning methods:

Audio Signal Processing;
Speech Signal Processing;
Seismic Signal Processing;
Time-series;
Synthetic signal/image generation and augmentation;
Image classification and segmentation;
Human Action Recognition;
Video Semantic Segmentation;
Video and Image Forensics;
Video summarization;
Biomedical Signal/Image Applications;
Industrial Signal/Image Applications;
Hardware implementations.

Prof. Dr. Emilio Soria-Olivas
Dr. Joan Vila-Francés
Dr. Juan Gómez-Sanchís
Dr. Fernando Mateo Jiménez
Prof. Marcelino Martínez Sober
Dr. Antonio José Serrano López
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Machine Learning
Deep Learning
Signal Processing
Image Processing
Algorithms

Published Papers (4 papers)

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Research

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21 pages, 6790 KiB

Open AccessArticle

A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

by Munkhjargal Gochoo, Syeda Amna Rizwan, Yazeed Yasin Ghadi, Ahmad Jalal and Kibum Kim

Appl. Sci. 2021, 11(12), 5503; https://doi.org/10.3390/app11125503 - 14 Jun 2021

Cited by 23 | Viewed by 2501

Abstract

Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results. Full article

(This article belongs to the Special Issue Deep Signal/Image Processing: Applications and New Algorithms)

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11 pages, 4617 KiB

Open AccessArticle

Multi-Feature Guided Low-Light Image Enhancement

by Hong Liang, Ankang Yu, Mingwen Shao and Yuru Tian

Appl. Sci. 2021, 11(11), 5055; https://doi.org/10.3390/app11115055 - 29 May 2021

Cited by 5 | Viewed by 2325

Abstract

Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed. Full article

(This article belongs to the Special Issue Deep Signal/Image Processing: Applications and New Algorithms)

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12 pages, 1307 KiB

Open AccessArticle

Time Course of Sleep Inertia Dissipation in Memory Tasks

by Miranda Occhionero, Marco Fabbri, Lorenzo Tonetti, Monica Martoni and Vincenzo Natale

Appl. Sci. 2021, 11(8), 3354; https://doi.org/10.3390/app11083354 - 8 Apr 2021

Cited by 5 | Viewed by 1907

Abstract

Sleep inertia (SI) refers to a complex psychophysiological phenomenon, observed after awakening, that can be described as the gradual recovery of waking-like status. The time course of cognitive performance dissipation in an everyday life condition is still unclear, especially in terms of the sleep stage at awakening (REM or NREM-stage 2) and the relative effects on performance. The present study aimed to investigate the SI dissipation in different memory performances upon spontaneous morning awakening after uninterrupted nighttime sleep. Eighteen young adults (7 females; mean age 24.9 ± 3.14 years) spent seven non-consecutive nights (one baseline, three REM awakenings and three St2 awakenings) in the laboratory under standard polysomnographic (PSG) control. Participants were tested after three REM awakenings and three St2 awakenings, and three times at 11:00 a.m. as a control condition. In each testing session, participants filled in the Global Vigor and Affect Scale and carried out one memory task (episodic, semantic, or procedural task). For each condition, participants were tested every 10 min within a time window of 80 min. In accordance with previous studies, SI affected subjective alertness throughout the entire time window assessed. Moreover, SI significantly affected performance speed but not accuracy in the semantic task. With reference to this task, the SI effect dissipated within 30 min of awakening from REM, and within 20 min of awakening from St2. No significant SI effect was observed on episodic or procedural memory tasks. Full article

(This article belongs to the Special Issue Deep Signal/Image Processing: Applications and New Algorithms)

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Review

Jump to: Research

41 pages, 3314 KiB

Open AccessReview

MR Images, Brain Lesions, and Deep Learning

by Darwin Castillo, Vasudevan Lakshminarayanan and María José Rodríguez-Álvarez

Appl. Sci. 2021, 11(4), 1675; https://doi.org/10.3390/app11041675 - 13 Feb 2021

Cited by 15 | Viewed by 6057

Abstract

Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment. Full article

(This article belongs to the Special Issue Deep Signal/Image Processing: Applications and New Algorithms)

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