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J. Imaging
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Image Processing in Soft Condensed Matter
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Image Processing in Soft Condensed Matter

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

Deadline for manuscript submissions: closed (15 January 2019) | Viewed by 30501

Special Issue Editor

Dr. Mierk Schwabe

E-Mail Website
Guest Editor
Institute of Materials Physics in Space, German Aerospace Center (DLR), Weßling, Germany
Interests: complex plasmas; dusty plasmas; collective phenomena; computer simulations turbulence

Special Issue Information

Dear Colleagues,

Soft condensed matter consists of mesoscopic particles that are significantly larger than atoms, but much smaller than the overall system. Due to their spatial and temporal scales, these individual constituents are often visible using suitable experimental techniques such as confocal microscopes, and their locations and dynamics can be studied. Examples of soft matter systems are granular systems, colloids, complex plasmas, and foams. All these diverse systems are united by common techniques and challenges in image processing.

The intent of the Special Issue on “Image Processing in Soft Condensed Matter” is to bring together specialists from various soft matter fields to present imaging processing techniques covering topics such as particle imaging velocimetry (PIV), particle tracking velocimetry (PTV), stereoscopic imaging, 3D visualization, and machine learning techniques for image processing. We are looking for review articles presenting the state-of-the-art of commonly-used techniques, as well as novel contributions.

Dr. Mierk Schwabe
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

  • Soft matter
  • Condensed matter
  • Colloids
  • Complex plasmas
  • Granular material
  • Particle Image Velocimetry
  • Particle Tracking Velocimetry
  • Confocal Microscopy
  • Stereoscopic Imaging

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

Jump to: Review

17 pages, 2885 KiB  
Article
Tracking and Linking of Microparticle Trajectories During Mode-Coupling Induced Melting in a Two-Dimensional Complex Plasma Crystal
by Lénaïc Couëdel and Vladimir Nosenko
J. Imaging 2019, 5(3), 41; https://doi.org/10.3390/jimaging5030041 - 16 Mar 2019
Cited by 4 | Viewed by 5326
Abstract
In this article, a strategy to track microparticles and link their trajectories adapted to the study of the melting of a quasi two-dimensional complex plasma crystal induced by the mode-coupling instability is presented. Because of the three-dimensional nature of the microparticle motions and [...] Read more.
In this article, a strategy to track microparticles and link their trajectories adapted to the study of the melting of a quasi two-dimensional complex plasma crystal induced by the mode-coupling instability is presented. Because of the three-dimensional nature of the microparticle motions and the inhomogeneities of the illuminating laser light sheet, the scattered light intensity can change significantly between two frames, making the detection of the microparticles and the linking of their trajectories quite challenging. Thanks to a two-pass noise removal process based on Gaussian blurring of the original frames using two different kernel widths, the signal-to-noise ratio was increased to a level that allowed a better intensity thresholding of different regions of the images and, therefore, the tracking of the poorly illuminated microparticles. Then, by predicting the positions of the microparticles based on their previous positions, long particle trajectories could be reconstructed, allowing accurate measurement of the evolution of the microparticle energies and the evolution of the monolayer properties. Full article
(This article belongs to the Special Issue Image Processing in Soft Condensed Matter)
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14 pages, 2258 KiB  
Article
Image Registration with Particles, Examplified with the Complex Plasma Laboratory PK-4 on Board the International Space Station
by Mierk Schwabe, Milenko Rubin-Zuzic, Christoph Räth and Mikhail Pustylnik
J. Imaging 2019, 5(3), 39; https://doi.org/10.3390/jimaging5030039 - 14 Mar 2019
Cited by 4 | Viewed by 5869
Abstract
Often, in complex plasmas and beyond, images of particles are recorded with a side-by-side camera setup. These images ideally need to be joined to create a large combined image. This is, for instance, the case in the PK-4 Laboratory on board the International [...] Read more.
Often, in complex plasmas and beyond, images of particles are recorded with a side-by-side camera setup. These images ideally need to be joined to create a large combined image. This is, for instance, the case in the PK-4 Laboratory on board the International Space Station (the next generation of complex plasma laboratories in space). It enables observations of microparticles embedded in an elongated low temperature DC plasma tube. The microparticles acquire charges from the surrounding plasma and interact strongly with each other. A sheet of laser light illuminates the microparticles, and two cameras record the motion of the microparticles inside this laser sheet. The fields of view of these cameras slightly overlap. In this article, we present two methods to combine the associated image pairs into one image, namely the SimpleElastix toolkit based on comparing the mutual information and a method based on detecting the particle positions. We found that the method based on particle positions performs slightly better than that based on the mutual information, and conclude with recommendations for other researchers wanting to solve a related problem. Full article
(This article belongs to the Special Issue Image Processing in Soft Condensed Matter)
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7 pages, 1991 KiB  
Article
Identification of the Interface in a Binary Complex Plasma Using Machine Learning
by He Huang, Mierk Schwabe and Cheng-Ran Du
J. Imaging 2019, 5(3), 36; https://doi.org/10.3390/jimaging5030036 - 12 Mar 2019
Cited by 12 | Viewed by 5550
Abstract
A binary complex plasma consists of two different types of dust particles in an ionized gas. Due to the spinodal decomposition and force imbalance, particles of different masses and diameters are typically phase separated, resulting in an interface. Both external excitation and internal [...] Read more.
A binary complex plasma consists of two different types of dust particles in an ionized gas. Due to the spinodal decomposition and force imbalance, particles of different masses and diameters are typically phase separated, resulting in an interface. Both external excitation and internal instability may cause the interface to move with time. Support vector machine (SVM) is a supervised machine learning method that can be very effective for multi-class classification. We applied an SVM classification method based on image brightness to locate the interface in a binary complex plasma. Taking the scaled mean and variance as features, three areas, namely small particles, big particles and plasma without dust particles, were distinguished, leading to the identification of the interface between small and big particles. Full article
(This article belongs to the Special Issue Image Processing in Soft Condensed Matter)
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15 pages, 1706 KiB  
Article
Three-Dimensional Reconstruction of Individual Particles in Dense Dust Clouds: Benchmarking Camera Orientations and Reconstruction Algorithms
by Michael Himpel and André Melzer
J. Imaging 2019, 5(2), 28; https://doi.org/10.3390/jimaging5020028 - 13 Feb 2019
Cited by 8 | Viewed by 5931
Abstract
In dusty plasmas, determining the three-dimensional particle positions and trajectories of individual particles is often required. This paper benchmarks two approaches capable of reconstructing the trajectories of particles in three dimensions. The influences of the particle number, the particle number density, and the [...] Read more.
In dusty plasmas, determining the three-dimensional particle positions and trajectories of individual particles is often required. This paper benchmarks two approaches capable of reconstructing the trajectories of particles in three dimensions. The influences of the particle number, the particle number density, and the orientation of the individual cameras are studied. Additionally, the demands on the desired image quality, required for these algorithms, are discussed. The reader is given practical information for the appropriate reconstruction approach and camera positioning that should/could be used in a specific application. Full article
(This article belongs to the Special Issue Image Processing in Soft Condensed Matter)
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Review

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22 pages, 1195 KiB  
Review
Algorithms for Particle Detection in Complex Plasmas
by Daniel P. Mohr, Christina A. Knapek, Peter Huber and Erich Zaehringer
J. Imaging 2019, 5(2), 30; https://doi.org/10.3390/jimaging5020030 - 21 Feb 2019
Cited by 8 | Viewed by 7361
Abstract
In complex plasmas, the behavior of freely floating micrometer sized particles is studied. The particles can be directly visualized and recorded by digital video cameras. To analyze the dynamics of single particles, reliable algorithms are required to accurately determine their positions to sub-pixel [...] Read more.
In complex plasmas, the behavior of freely floating micrometer sized particles is studied. The particles can be directly visualized and recorded by digital video cameras. To analyze the dynamics of single particles, reliable algorithms are required to accurately determine their positions to sub-pixel accuracy from the recorded images. Typically, a straightforward algorithm such as the moment method is used for this task. Here, we combine different variations of the moment method with common techniques for image pre- and post-processing (e.g., noise reduction and fitting), and we investigate the impact of the choice of threshold parameters, including an automatic threshold detection, on synthetic data with known attributes. The results quantitatively show that each algorithm and method has its own advantage, often depending on the problem at hand. This knowledge is applicable not only to complex plasmas, but useful for any kind of comparable image-based particle tracking, e.g., in the field of colloids or granular matter. Full article
(This article belongs to the Special Issue Image Processing in Soft Condensed Matter)
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