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Algorithms
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Bio-Inspired Algorithms for Image Processing
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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: closed (31 October 2020) | Viewed by 29881

Special Issue Editors

Dr. Sándor Szénási

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Guest Editor
Software Engineering Institute, John von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, Hungary
Interests: machine learning, neural networks, simulation, GPU programming
Special Issues, Collections and Topics in MDPI journals
Dr. Gábor Kertész

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Guest Editor
Software Engineering Institute, John von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, Hungary
Interests: machine learning; deep neural networks; parallel programming
Special Issues, Collections and Topics in MDPI journals

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 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. 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 1600 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

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Published Papers (7 papers)

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Editorial

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2 pages, 631 KiB  
Editorial
Special Issue on Bio-Inspired Algorithms for Image Processing
by Sándor Szénási and Gábor Kertész
Algorithms 2020, 13(12), 320; https://doi.org/10.3390/a13120320 - 3 Dec 2020
Viewed by 1909
Abstract
In the field of image processing, there are several difficult issues that do not have exact solutions due to incomplete or imperfect information and limited computation capacity [...] Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms for Image Processing)

Research

Jump to: Editorial

27 pages, 14281 KiB  
Article
Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks
by Roland Lõuk, Andri Riid, René Pihlak and Aleksei Tepljakov
Algorithms 2020, 13(8), 198; https://doi.org/10.3390/a13080198 - 14 Aug 2020
Cited by 11 | Viewed by 4037
Abstract
In the manuscript, the issue of detecting and segmenting out pavement defects on highway roads is addressed. Specifically, computer vision (CV) methods are developed and applied to the problem based on deep learning of convolutional neural networks (ConvNets). A novel neural network structure [...] Read more.
In the manuscript, the issue of detecting and segmenting out pavement defects on highway roads is addressed. Specifically, computer vision (CV) methods are developed and applied to the problem based on deep learning of convolutional neural networks (ConvNets). A novel neural network structure is considered, based on a pipeline of three ConvNets and endowed with the capacity for context awareness, which improves grid-based search for defects on orthoframes by considering the surrounding image content—an approach, which essentially draws inspiration from how humans tend to solve the task of image segmentation. Also, methods for assessing the quality of segmentation are discussed. The contribution also describes the complete procedure of working with pavement defects in an industrial setting, involving the workcycle of defect annotation, ConvNet training and validation. The results of ConvNet evaluation provided in the paper hint at a successful implementation of the proposed technique. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms for Image Processing)
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22 pages, 5363 KiB  
Article
Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems
by Dan Malowany and Hugo Guterman
Algorithms 2020, 13(7), 167; https://doi.org/10.3390/a13070167 - 11 Jul 2020
Cited by 5 | Viewed by 4639
Abstract
Computer vision is currently one of the most exciting and rapidly evolving fields of science, which affects numerous industries. Research and development breakthroughs, mainly in the field of convolutional neural networks (CNNs), opened the way to unprecedented sensitivity and precision in object detection [...] Read more.
Computer vision is currently one of the most exciting and rapidly evolving fields of science, which affects numerous industries. Research and development breakthroughs, mainly in the field of convolutional neural networks (CNNs), opened the way to unprecedented sensitivity and precision in object detection and recognition tasks. Nevertheless, the findings in recent years on the sensitivity of neural networks to additive noise, light conditions, and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In an attempt to bring computer vision algorithms closer to the capabilities of a human operator, the mechanisms of the human visual system was analyzed in this work. Recent studies show that the mechanisms behind the recognition process in the human brain include continuous generation of predictions based on prior knowledge of the world. These predictions enable rapid generation of contextual hypotheses that bias the outcome of the recognition process. This mechanism is especially advantageous in situations of uncertainty, when visual input is ambiguous. In addition, the human visual system continuously updates its knowledge about the world based on the gaps between its prediction and the visual feedback. CNNs are feed forward in nature and lack such top-down contextual attenuation mechanisms. As a result, although they process massive amounts of visual information during their operation, the information is not transformed into knowledge that can be used to generate contextual predictions and improve their performance. In this work, an architecture was designed that aims to integrate the concepts behind the top-down prediction and learning processes of the human visual system with the state-of-the-art bottom-up object recognition models, e.g., deep CNNs. The work focuses on two mechanisms of the human visual system: anticipation-driven perception and reinforcement-driven learning. Imitating these top-down mechanisms, together with the state-of-the-art bottom-up feed-forward algorithms, resulted in an accurate, robust, and continuously improving target recognition model. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms for Image Processing)
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14 pages, 6386 KiB  
Article
Image Edge Detector with Gabor Type Filters Using a Spiking Neural Network of Biologically Inspired Neurons
by Krishnamurthy V. Vemuru
Algorithms 2020, 13(7), 165; https://doi.org/10.3390/a13070165 - 9 Jul 2020
Cited by 10 | Viewed by 4457
Abstract
We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine [...] Read more.
We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for 1 < t < T /4, where t is the number of milliseconds in a simulated exposure frame and T is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms for Image Processing)
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18 pages, 767 KiB  
Article
Metric Embedding Learning on Multi-Directional Projections
by Gábor Kertész
Algorithms 2020, 13(6), 133; https://doi.org/10.3390/a13060133 - 29 May 2020
Cited by 4 | Viewed by 3371
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 [...] Read more.
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, and the output of such a model maps them to a multidimensional encoding space. A metric based loss brings same instance embeddings close to each other. While these solutions achieve high classification performance, low efficiency is caused by memory cost of high parameter number, which is in a relationship with input image size. Upon shrinking the input, the model requires less trainable parameters, while performance decreases. This drawback is tackled by using compressed feature extraction, e.g., projections. In this paper, a multi-directional image projection transformation with fixed vector lengths (MDIPFL) is applied for one-shot recognition tasks, trained on Siamese and Triplet architectures. Results show, that MDIPFL based approach achieves decent performance, despite of the significantly lower number of parameters. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms for Image Processing)
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14 pages, 4959 KiB  
Article
PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation
by Feiyang Chen, Ying Jiang, Xiangrui Zeng, Jing Zhang, Xin Gao and Min Xu
Algorithms 2020, 13(5), 126; https://doi.org/10.3390/a13050126 - 19 May 2020
Cited by 3 | Viewed by 5098
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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16 pages, 11276 KiB  
Article
Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
by Jin Xu, Haixia Wang, Can Cui, Baigang Zhao and Bo Li
Algorithms 2020, 13(3), 69; https://doi.org/10.3390/a13030069 - 21 Mar 2020
Cited by 30 | Viewed by 4471
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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