Special Issue "Bio-Inspired Algorithms for Image Processing"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Sandor Szenasi
Website
Guest Editor
Software Engineering Institute of John von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, Hungary
Interests: GPGPU; CUDA; bio-inspired algorithms; image processing; parallel algorithms
Dr. Gábor Kertész
Website
Guest Editor
Software Engineering Institute of John von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, Hungary
Interests: machine learning; deep neural networks; parallel programming

Special Issue Information

Dear Colleagues,

In the field of image processing, there are several hard problems without exact solutions due to incomplete or imperfect information and limited computation capacity. Some methods of the different subfields (classification, feature extraction, pattern recognition, segmentation, etc.) are based on the process of natural selection, the behavior of living creatures, or especially on the mechanisms of the brain. It is also worth mentioning that in the case of traditional procedural image processing methods, where the algorithms are well-defined, the parametrization can be challenging, if not impossible. This step can be formalized as an optimization problem, where the application of heuristics is necessary for addressing such highly complex problems to provide feasible solutions in acceptable runtime. Many of these optimization techniques are also inspired by nature.

In this Special Issue of "Bio-inspired Algorithms for Image Processing", we seek original research or results of practical applications from the area of bio-inspired algorithms in the field of image processing. We are waiting for manuscripts discussing evolutional (genetic algorithms, NSGA, etc.), swarm intelligence-based (particle swarm optimization, ant colony optimization, fireworks, etc.) and brain-inspired computing (neural networks, deep learning, etc.) methods applied in any kind of image processing research projects (classification, segmentation, medical image processing, etc.).

Dr. Sandor Szenasi
Dr. Gábor Kertész
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 papers will be 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. Algorithms 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 1000 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

  • Design and analysis of bio-inspired methods
  • Application of bio-inspired methods
  • Limitations of bio-inspired methods
  • Ant colony optimization
  • Particle swarm optimization
  • Firefly algorithm
  • Fireworks algorithm
  • Bees algorithm
  • Evolutionary algorithms
  • Neural networks
  • Deep learning
  • Soft computing methods
  • Nature-inspired heuristics

Published Papers (2 papers)

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Research

Open AccessArticle
PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation
Algorithms 2020, 13(5), 126; https://doi.org/10.3390/a13050126 - 19 May 2020
Abstract
Salient segmentation is a critical step in biomedical image analysis, aiming to cut out regions that are most interesting to humans. Recently, supervised methods have achieved promising results in biomedical areas, but they depend on annotated training data sets, which requires labor and [...] Read more.
Salient segmentation is a critical step in biomedical image analysis, aiming to cut out regions that are most interesting to humans. Recently, supervised methods have achieved promising results in biomedical areas, but they depend on annotated training data sets, which requires labor and proficiency in related background knowledge. In contrast, unsupervised learning makes data-driven decisions by obtaining insights directly from the data themselves. In this paper, we propose a completely unsupervised self-aware network based on pre-training and attentional backpropagation for biomedical salient segmentation, named as PUB-SalNet. Firstly, we aggregate a new biomedical data set from several simulated Cellular Electron Cryo-Tomography (CECT) data sets featuring rich salient objects, different SNR settings, and various resolutions, which is called SalSeg-CECT. Based on the SalSeg-CECT data set, we then pre-train a model specially designed for biomedical tasks as a backbone module to initialize network parameters. Next, we present a U-SalNet network to learn to selectively attend to salient objects. It includes two types of attention modules to facilitate learning saliency through global contrast and local similarity. Lastly, we jointly refine the salient regions together with feature representations from U-SalNet, with the parameters updated by self-aware attentional backpropagation. We apply PUB-SalNet for analysis of 2D simulated and real images and achieve state-of-the-art performance on simulated biomedical data sets. Furthermore, our proposed PUB-SalNet can be easily extended to 3D images. The experimental results on the 2d and 3d data sets also demonstrate the generalization ability and robustness of our method. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms for Image Processing)
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Open AccessArticle
Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
Algorithms 2020, 13(3), 69; https://doi.org/10.3390/a13030069 - 21 Mar 2020
Abstract
In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in [...] Read more.
In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms for Image Processing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Metric Embedding Learning on Multi-Directional Projections

Abstract: Image based instance recognition is a difficult problem, in some cases even for the human eye. While latest developments in computer vision -- mostly driven by deep learning -- have shown that high performance models for classification or categorization can be engineered, the problem of discriminating similar objects with a low number of samples remain challenging. Advances from multi-class classification are applied for object matching problems, as the feature extraction techniques are the same: nature-inspired multi-layered convolutional nets learn the representations from images, and the output of such a model maps them to a multidimensional encoding space. A metric based loss is defined, bringing embeddings from same instances close to each other. While these solutions achieve high classification performance, low efficiency is caused by memory cost of parameter number, which is in a relationship with input image size; on shrinking the input, the model require less trainable parameters, while performance decreases. This drawback is tackled by using compressed representations of images, e.g. image projections. In this paper, a multi-directional image projection transformation resulting in fixed vector lengths (MDIPFL) is applied for image one-shot recognition tasks, trained on Siamese and Triplet architectures.

Keywords: deep metric learning, one-shot learning, multi-directional image projections, object matching, object re-identification

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