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Applied Sciences
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New Trends in Image Processing
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New Trends in Image Processing

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 July 2020) | Viewed by 26863

Special Issue Editors

Prof. Dr. Hyeonjoon Moon

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: big data; computer vision; pattern recognition; biometrics; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Irfan Mehmood

E-Mail Website
Guest Editor
Faculty of Engineering & Informatics, School of Media, Design and Technology, University of Bradford, Bradford, UK
Interests: visual surveillance; image and video processing; computer vision; artificial intelligence; deep learning; medical image processing; visual attention modeling; information summarization and retrieval
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few years, the methodologies and technologies related to video processing, imaging processing, computer graphics, 3D modelling, and multimedia have significantly been implemented in the field of computer vision. The continuous development of these technologies has led to an introduction of new methodologies and applications in this field. Moreover, recent image-processing, machine learning algorithms, and especially deep learning, provide an opportunity to efficiently process massive datasets, in order to extract information and develop new analysis procedures.

The aim of this Special Issue is two-fold, as follows: firstly, this Issue shows novel applications of modern devices for data acquisition and data visualization (e.g., CCTV videos, 3D scanners, VR glasses, and robots), and, secondly, it proposes new methodologies for huge dataset processing using modern pattern recognition and machine learning approaches (e.g., deep learning and hypergraph learning).

The proposed Special Issue, named “New Trends in Image Processing”, includes (but it is not limited) the following topics:

  • 3D models processing
  • Augmented and virtual reality applications
  • Robotic applications
  • RGBD analysis
  • Advanced image enhancement
  • De-noising and low-light enhancement
  • Advanced image classification and retrieval
  • Semantic segmentation
  • Image processing

Prof. Hyeonjoon Moon
Dr. Irfan Mehmood
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

  • Biometrics
  • Enhancement
  • Learning
  • Classification
  • Image processing
  • Computer vision

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Published Papers (5 papers)

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Research

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14 pages, 12658 KiB  
Article
Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network
by Hyeon-Woo Kim, Hyung-Joon Kim, Seungmin Rho and Eenjun Hwang
Appl. Sci. 2020, 10(7), 2253; https://doi.org/10.3390/app10072253 - 26 Mar 2020
Cited by 24 | Viewed by 6832
Abstract
Facial landmarks represent prominent feature points on the face that can be used as anchor points in many face-related tasks. So far, a lot of research has been done with the aim of achieving efficient extraction of landmarks from facial images. Employing a [...] Read more.
Facial landmarks represent prominent feature points on the face that can be used as anchor points in many face-related tasks. So far, a lot of research has been done with the aim of achieving efficient extraction of landmarks from facial images. Employing a large number of feature points for landmark detection and tracking usually requires excessive processing time. On the contrary, relying on too few feature points cannot accurately represent diverse landmark properties, such as shape. To extract the 68 most popular facial landmark points efficiently, in our previous study, we proposed a model called EMTCNN that extended the multi-task cascaded convolutional neural network for real-time face landmark detection. To improve the detection accuracy, in this study, we augment the EMTCNN model by using two convolution techniques—dilated convolution and CoordConv. The former makes it possible to increase the filter size without a significant increase in computation time. The latter enables the spatial coordinate information of landmarks to be reflected in the model. We demonstrate that our model can improve the detection accuracy while maintaining the processing speed. Full article
(This article belongs to the Special Issue New Trends in Image Processing)
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17 pages, 3654 KiB  
Article
Tampered and Computer-Generated Face Images Identification Based on Deep Learning
by L. Minh Dang, Kyungbok Min, Sujin Lee, Dongil Han and Hyeonjoon Moon
Appl. Sci. 2020, 10(2), 505; https://doi.org/10.3390/app10020505 - 10 Jan 2020
Cited by 13 | Viewed by 4454
Abstract
Image forgery is an active topic in digital image tampering that is performed by moving a region from one image into another image, combining two images to form one image, or retouching an image. Moreover, recent developments of generative adversarial networks (GANs) that [...] Read more.
Image forgery is an active topic in digital image tampering that is performed by moving a region from one image into another image, combining two images to form one image, or retouching an image. Moreover, recent developments of generative adversarial networks (GANs) that are used to generate human facial images have made it more challenging for even humans to detect the tampered one. The spread of those images on the internet can cause severe ethical, moral, and legal issues if the manipulated images are misused. As a result, much research has been conducted to detect facial image manipulation based on applying machine learning algorithms on tampered face datasets in the last few years. This paper introduces a deep learning-based framework that can identify manipulated facial images and GAN-generated images. It is comprised of multiple convolutional layers, which can efficiently extract features using multi-level abstraction from tampered regions. In addition, a data-based approach, cost-sensitive learning-based approach (class weight), and ensemble-based approach (eXtreme Gradient Boosting) is applied to the proposed model to deal with the imbalanced data problem (IDP). The superiority of the proposed model that deals with an IDP is verified using a tampered face dataset and a GAN-generated face dataset under various scenarios. Experimental results proved that the proposed framework outperformed existing expert systems, which has been used for identifying manipulated facial images and GAN-generated images in terms of computational complexity, area under the curve (AUC), and robustness. As a result, the proposed framework inspires the development of research on image forgery identification and enables the potential to integrate these models into practical applications, which require tampered facial image detection. Full article
(This article belongs to the Special Issue New Trends in Image Processing)
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17 pages, 3620 KiB  
Article
Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images
by Josep Arnal and Luis Súcar
Appl. Sci. 2020, 10(1), 243; https://doi.org/10.3390/app10010243 - 28 Dec 2019
Cited by 21 | Viewed by 3792
Abstract
To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of [...] Read more.
To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse and Gaussian noise, as much as mixed noise. The proposed filtering method was compared to the state-of-the-art methodologies: adaptive nearest neighbor filter, alternating projections filter, color block-matching 3D filter, fuzzy peer group averaging filter, partition-based trimmed vector median filter, trilateral filter, fuzzy wavelet shrinkage denoising filter, graph regularization filter, iterative peer group switching vector filter, peer group method, and the fuzzy vector median method. The experiments demonstrated that the introduced noise reduction technique outperforms those state-of-the-art filters with respect to the metrics peak signal to noise ratio (PSNR), the mean absolute error (MAE), and the normalized color difference (NCD). Full article
(This article belongs to the Special Issue New Trends in Image Processing)
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14 pages, 2407 KiB  
Article
Detection and Removal of Moving Object Shadows Using Geometry and Color Information for Indoor Video Streams
by Akmalbek Abdusalomov and Taeg Keun Whangbo
Appl. Sci. 2019, 9(23), 5165; https://doi.org/10.3390/app9235165 - 28 Nov 2019
Cited by 18 | Viewed by 7468
Abstract
The detection and removal of moving object shadows is a challenging issue. In this article, we propose a new approach for accurately removing shadows on modern buildings in the presence of a moving object in the scene. Our approach is capable of achieving [...] Read more.
The detection and removal of moving object shadows is a challenging issue. In this article, we propose a new approach for accurately removing shadows on modern buildings in the presence of a moving object in the scene. Our approach is capable of achieving good performance when addressing multiple shadow problems, by reducing background surface similarity and ghost artifacts. First, a combined contrast enhancement technique is applied to the input frame sequences to produce high-quality output images for indoor surroundings with an artificial light source. After obtaining suitable enhanced images, segmentation and noise removal filtering are applied to create a foreground mask of the possible candidate moving object shadow regions. Subsequently, geometry and color information are utilized to remove detected shadow pixels that incorrectly include the foreground mask. Here, experiments show that our method correctly detects and removes shadowed pixels in object tracking tasks, such as in universities, department stores, or several indoor sports games. Full article
(This article belongs to the Special Issue New Trends in Image Processing)
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Other

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10 pages, 3212 KiB  
Letter
Empirical Remarks on the Translational Equivariance of Convolutional Layers
by Kyung Joo Cheoi, Hyeonyeong Choi and Jaepil Ko
Appl. Sci. 2020, 10(9), 3161; https://doi.org/10.3390/app10093161 - 1 May 2020
Cited by 3 | Viewed by 3096
Abstract
In general, convolutional neural networks (CNNs) maintain some level of translational invariance. However, the convolutional layer itself is translational-equivariant. The pooling layers provide some level of invariance. In object recognition, invariance is more important than equivariance. In this paper, we investigate how vulnerable [...] Read more.
In general, convolutional neural networks (CNNs) maintain some level of translational invariance. However, the convolutional layer itself is translational-equivariant. The pooling layers provide some level of invariance. In object recognition, invariance is more important than equivariance. In this paper, we investigate how vulnerable CNNs without pooling or augmentation are to translation in object recognition. For CNNs that are specialized in learning local textures but vulnerable to learning global geometric information, we propose a method to explicitly transform an image into a global feature image and then provide it as an input to neural networks. In our experiments on a modified MNIST dataset, we demonstrate that the recognition accuracy of a conventional baseline network significantly decreases from 98% to less than 60% even in the case of 2-pixel translation. We also demonstrate that the proposed method is far superior to the baseline network in terms of performance improvement. Full article
(This article belongs to the Special Issue New Trends in Image Processing)
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