Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
3.2
Journals
J. Imaging
Special Issues
Robust Image Processing
share announcement

Robust Image Processing

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 18627

Special Issue Editor

Prof. Dr. Antoine Vacavant

E-Mail Website
Guest Editor
Institut Pascal, Université Clermont Auvergne, CNRS, SIGMA Clermont, F-63000 Clermont-Ferrand, France
Interests: medical and biomedical image analysis; robustness for image processing; computer vision; machine learning; discrete mathematical models (geometry, topology, morphology); benchmarking and evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robustness is an important concern in machine learning, pattern recognition, and image processing, and it has attracted a lot of attention from technical and scientific viewpoints in the literature, in particular, robustness models the capacity of a computerized approach to resist perturbing phenomena and data uncertainties, such as inherent image noise, which generates a common artefact while designing algorithms. Further, the evaluation of this robustness is a crucial matter in machine learning and computer vision, more increasingly so with the emergence of deep learning algorithms and big data. This matter has been dealt with for a long time, since the introduction of robust statistical models and tools.

This Special Issue on “Robust Image Processing” aims at presenting novel robust approaches devoted to image processing, pattern recognition or machine learning. This is also the opportunity to address the question of the definition and evaluation of robustness, according to noise and other uncertainties models related to these topics.

Prof. Antoine Vacavant
Guest Editor

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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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

  • Novel robust approaches in image processing, machine learning, pattern recognition, and computer vision
  • Definition and evaluation of robustness
  • Benchmarking, large scale image data analysis
  • Image data noise and other uncertainties modeling
  • Robust statistics for imaging

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

11 pages, 10409 KiB  
Article
Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images
by Ali S. Awad
J. Imaging 2020, 6(10), 103; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6100103 - 04 Oct 2020
Cited by 1 | Viewed by 1604
Abstract
In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other [...] Read more.
In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other is external, based on the index or pixel location in the image. The proposed method focuses on leveraging these two types of prior information to obtain tangible results. To this end, very similar patches are collected from the noisy image. This is done by sorting the image pixels in ascending order and then placing them in consecutive rows in a new two-dimensional image. Henceforth, a principal component analysis is applied on the patch matrix to help remove the small noisy components. Since the restored pixels are similar or close in values to those in the clean image, it is preferable to arrange them using indices similar to those of the clean pixels. Simulation experiments show that outstanding results are achieved, compared to other known methods, either in terms of image visual quality or peak signal to noise ratio. Specifically, once the proper indices are used, the proposed method achieves PSNR value better than the other well-known methods by >1.5 dB in all the simulation experiments. Full article
(This article belongs to the Special Issue Robust Image Processing)
►▼ Show Figures

Figure 1

16 pages, 4118 KiB  
Article
Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery
by Md Abul Ehsan Bhuiyan, Chandi Witharana, Anna K. Liljedahl, Benjamin M. Jones, Ronald Daanen, Howard E. Epstein, Kelcy Kent, Claire G. Griffin and Amber Agnew
J. Imaging 2020, 6(9), 97; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6090097 - 17 Sep 2020
Cited by 21 | Viewed by 5282
Abstract
Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing [...] Read more.
Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo object detection and classification from multispectral (MS) imagery. MS imagery includes RGB and narrow spectral channels from near- and/or middle-infrared regions of reflectance spectra. The central objective of this exploratory study is to understand to what degree MS band statistics govern DLCNN model predictions. We scaffold our analysis on a case study that uses Arctic tundra permafrost landform features called ice-wedge polygons (IWPs) as candidate geo objects. We choose Mask RCNN as the DLCNN architecture to detect IWPs from eight-band Worldview-02 VHSR satellite imagery. A systematic experiment was designed to understand the impact on choosing the optimal three-band combination in model prediction. We tasked five cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.95, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17–0.19 and 0.20–0.21, respectively, for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels. Full article
(This article belongs to the Special Issue Robust Image Processing)
►▼ Show Figures

Figure 1

12 pages, 5699 KiB  
Article
Marching Cubes and Histogram Pyramids for 3D Medical Visualization
by Porawat Visutsak
J. Imaging 2020, 6(9), 88; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6090088 - 03 Sep 2020
Cited by 3 | Viewed by 4109
Abstract
This paper aims to implement histogram pyramids with marching cubes method for 3D medical volumetric rendering. The histogram pyramids are used for feature extraction by segmenting the image into the hierarchical order like the pyramid shape. The histogram pyramids can decrease the number [...] Read more.
This paper aims to implement histogram pyramids with marching cubes method for 3D medical volumetric rendering. The histogram pyramids are used for feature extraction by segmenting the image into the hierarchical order like the pyramid shape. The histogram pyramids can decrease the number of sparse matrixes that will occur during voxel manipulation. The important feature of the histogram pyramids is the direction of segments in the image. Then this feature will be used for connecting pixels (2D) to form up voxel (3D) during marching cubes implementation. The proposed method is fast and easy to implement and it also produces a smooth result (compared to the traditional marching cubes technique). The experimental results show the time consuming for generating 3D model can be reduced by 15.59% in average. The paper also shows the comparison between the surface rendering using the traditional marching cubes and the marching cubes with histogram pyramids. Therefore, for the volumetric rendering such as 3D medical models and terrains where a large number of lookups in 3D grids are performed, this method is a particularly good choice for generating the smooth surface of 3D object. Full article
(This article belongs to the Special Issue Robust Image Processing)
►▼ Show Figures

Figure 1

18 pages, 6956 KiB  
Article
μXRF Mapping as a Powerful Technique for Investigating Metal Objects from the Archaeological Site of Ferento (Central Italy)
by Giuseppe Capobianco, Adriana Sferragatta, Luca Lanteri, Giorgia Agresti, Giuseppe Bonifazi, Silvia Serranti and Claudia Pelosi
J. Imaging 2020, 6(7), 59; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6070059 - 30 Jun 2020
Cited by 6 | Viewed by 3196
Abstract
This research concerns the application of micro X-ray fluorescence (µXRF) mapping to the investigation of a group of selected metal objects from the archaeological site of Ferento, a Roman and then medieval town in Central Italy. Specifically, attention was focused on two test [...] Read more.
This research concerns the application of micro X-ray fluorescence (µXRF) mapping to the investigation of a group of selected metal objects from the archaeological site of Ferento, a Roman and then medieval town in Central Italy. Specifically, attention was focused on two test pits, named IV and V, in which metal objects were found, mainly pertaining to the medieval period and never investigated before the present work from a compositional point of view. The potentiality of µXRF mapping was tested through a Bruker Tornado M4 equipped with an Rh tube, operating at 50 kV, 500 μA, and spot 25 μm obtained with polycapillary optics. Principal component analysis (PCA) and multivariate curve resolution (MCR) were used for processing the X-ray fluorescence spectra. The results showed that the investigated items are characterized by different compositions in terms of chemical elements. Three little wheels are made of lead, while the fibulae are made of copper-based alloys with varying amounts of tin, zinc, and lead. Only one ring is iron-based, and the other objects, namely a spatula and an applique, are also made of copper-based alloys, but with different relative amounts of the main elements. In two objects, traces of gold were found, suggesting the precious character of these pieces. MCR analysis was demonstrated to be particularly useful to confirm the presence of trace elements, such as gold, as it could differentiate the signals related to minor elements from those due to major chemical elements. Full article
(This article belongs to the Special Issue Robust Image Processing)
►▼ Show Figures

Figure 1

23 pages, 8173 KiB  
Article
Image Processing Technique and Hidden Markov Model for an Elderly Care Monitoring System
by Swe Nwe Nwe Htun, Thi Thi Zin and Pyke Tin
J. Imaging 2020, 6(6), 49; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6060049 - 13 Jun 2020
Cited by 15 | Viewed by 3836
Abstract
Advances in image processing technologies have provided more precise views in medical and health care management systems. Among many other topics, this paper focuses on several aspects of video-based monitoring systems for elderly people living independently. Major concerns are patients with chronic diseases [...] Read more.
Advances in image processing technologies have provided more precise views in medical and health care management systems. Among many other topics, this paper focuses on several aspects of video-based monitoring systems for elderly people living independently. Major concerns are patients with chronic diseases and adults with a decline in physical fitness, as well as falling among elderly people, which is a source of life-threatening injuries and a leading cause of death. Therefore, in this paper, we propose a video-vision-based monitoring system using image processing technology and a Hidden Markov Model for differentiating falls from normal states for people. Specifically, the proposed system is composed of four modules: (1) object detection; (2) feature extraction; (3) analysis for differentiating normal states from falls; and (4) a decision-making process using a Hidden Markov Model for sequential states of abnormal and normal. In the object detection module, background and foreground segmentation is performed by applying the Mixture of Gaussians model, and graph cut is applied for foreground refinement. In the feature extraction module, the postures and positions of detected objects are estimated by applying the hybrid features of the virtual grounding point, inclusive of its related area and the aspect ratio of the object. In the analysis module, for differentiating normal, abnormal, or falling states, statistical computations called the moving average and modified difference are conducted, both of which are employed to estimate the points and periods of falls. Then, the local maximum or local minimum and the half width value are determined in the observed modified difference to more precisely estimate the period of a falling state. Finally, the decision-making process is conducted by developing a Hidden Markov Model. The experimental results used the Le2i fall detection dataset, and showed that our proposed system is robust and reliable and has a high detection rate. Full article
(This article belongs to the Special Issue Robust Image Processing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop