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Electronics
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Computer Vision Techniques: Theory and Applications
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Computer Vision Techniques: Theory and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 8234

Special Issue Editors

Prof. Dr. Miin-shen Yang

E-Mail Website
Guest Editor
Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li 32023, Taiwan
Interests: fuzzy clustering; fuzzy set applications; pattern recognition; machine learning
Prof. Dr. Cocianu Catalina-Lucia

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010374 Bucharest, Romania
Interests: digital image processing; machine learning; evolutionary computing

Special Issue Information

Dear Colleagues,

Computer vision (CV) is a fast-growing domain of informatics concerned with making computers “see”—as in “understand”—imagery. Image acquisition is performed by image sensors that currently have capabilities which were unthinkable two decades ago. These sensors surpass the human eye as they can perceive things invisible to us but are limited in other aspects. Computers can be programmed to “understand” quite a lot from captured images, often things that humans do not see or care about. Still, computers are nowhere near the capability of the human brain. The gap is becoming smaller and smaller as technology and science advance. The applications of computer vision are wide and cover all aspects of social and economic activities: pattern recognition, nondestructive assessment of quality for various types of products, 3D object detection, agriculture, weather, healthcare, identification, surveillance, public safety, driving aides, self-driving vehicles, etc. Computers are already able to estimate defects and properties of products/objects such as color, shape, size, surfaces, contamination, and they can identify persons and track them. The limits to where computer vision can go cannot be established, but current developments suggest the limit is only given by our imagination and soon computers might even see better than us.

This Special Issue aims to provide a platform to publish recent original research, review papers or surveys in the state of the art of theoretical approaches and applications of computer vision.

Topics of interest to this Special Issue include but are not limited to:

  • Image processing in CV;
  • Feature detection and matching;
  • Image segmentation;
  • Image registration;
  • Object recognition and tracking;
  • Image classification and recognition;
  • Motion analysis;
  • Machine learning for CV;
  • Biologically inspired CV;
  • Applications.

Technical Program Committee Member:

Dr. Uscatu Cristian Răzvan: Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest 010374, Romania

Prof. Dr. Miin-shen Yang
Prof. Dr. Cocianu Catalina-Lucia
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. Electronics 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

  • image analysis
  • image processing
  • image understanding
  • deep learning techniques for CV
  • evolutionary computing and bio-inspired algorithms for CV

Published Papers (4 papers)

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Research

16 pages, 10164 KiB  
Article
SV-FPN: Small Object Feature Enhancement and Variance-Guided RoI Fusion for Feature Pyramid Networks
by Qianhui Yang, Changlun Zhang, Hengyou Wang, Qiang He and Lianzhi Huo
Electronics 2022, 11(13), 2028; https://doi.org/10.3390/electronics11132028 - 28 Jun 2022
Cited by 4 | Viewed by 1440
Abstract
Small object detection is one of the research difficulties in object detection, and Feature Pyramid Networks (FPN) is a common feature extractor in deep learning; thus, improving the results of small object detection based on FPN is of great significance in this field. [...] Read more.
Small object detection is one of the research difficulties in object detection, and Feature Pyramid Networks (FPN) is a common feature extractor in deep learning; thus, improving the results of small object detection based on FPN is of great significance in this field. In this paper, SV-FPN is proposed for a small object detection task, which consists of Small Object Feature Enhancement (SOFE) and Variance-guided Region of Interest Fusion (VRoIF). When using FPN as a feature extractor, an SOFE module is designed to enhance the finer-resolution level feature maps from which the small object features are extracted. VRoIF takes the variance of RoI features as the data driver to learn the completeness of several RoI features from different feature layers, which avoids wasting information and introducing noise. Ablation experiments on three public datasets (KITTI, PASCAL VOC 07+12 and MS COCO 2017) demonstrate the effectiveness of SV-FPN, and the mean Average Precision (mAP) of SV-FPN in the three datasets achieves 41.5%, 53.9% and 38.3%, respectively. Full article
(This article belongs to the Special Issue Computer Vision Techniques: Theory and Applications)
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17 pages, 3468 KiB  
Article
Infrared and Visible Image Registration Based on Automatic Robust Algorithm
by Jingyu Ji, Yuhua Zhang, Zhilong Lin, Yongke Li, Changlong Wang, Yongjiang Hu and Jiangyi Yao
Electronics 2022, 11(11), 1674; https://doi.org/10.3390/electronics11111674 - 25 May 2022
Cited by 3 | Viewed by 2643
Abstract
Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an [...] Read more.
Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an automatic robust infrared and visible image registration algorithm, based on a deep convolutional network, was proposed. In order to precisely search and locate the feature points, a deep convolutional network is introduced, which solves the problem that a large number of feature points can still be extracted when the pixels of the infrared image are not clear. Then, in order to achieve accurate feature point matching, a rough-to-fine matching algorithm is designed. The rough matching is obtained by location orientation scale transform Euclidean distance, and then, the fine matching is performed based on the update global optimization, and finally, the image registration is realized. Experimental results show that the proposed algorithm has better robustness and accuracy than several advanced registration algorithms. Full article
(This article belongs to the Special Issue Computer Vision Techniques: Theory and Applications)
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20 pages, 6649 KiB  
Article
Feature Enhancement-Based Ship Target Detection Method in Optical Remote Sensing Images
by Liming Zhou, Yahui Li, Xiaohan Rao, Yadi Wang, Xianyu Zuo, Baojun Qiao and Yong Yang
Electronics 2022, 11(4), 634; https://doi.org/10.3390/electronics11040634 - 18 Feb 2022
Cited by 3 | Viewed by 1577
Abstract
Ship targets in ORSIs (Optical Remote Sensing Images) have the characteristics of various scales, and most of them are medium and small-scale targets. When the existing target detection algorithms are applied to ship target detection in ORSIs, the detection accuracy is low. There [...] Read more.
Ship targets in ORSIs (Optical Remote Sensing Images) have the characteristics of various scales, and most of them are medium and small-scale targets. When the existing target detection algorithms are applied to ship target detection in ORSIs, the detection accuracy is low. There are two main reasons for the above problems, one is the mismatch of the receptive fields, and the other is the lack of feature information. For resolving the problem that multi-scale ship targets are difficult to detect, this paper proposes a ship target detection algorithm based on feature enhancement. Firstly, EIRM (Elastic Inception Residual Module) is proposed for feature enhancement, which can capture feature information of different dimensions and provide receptive fields of different scales for mid- and low-level feature maps. Secondly, the SandGlass-L block is proposed by replacing the ReLu6 activation function of the SandGlass block with the Leaky ReLu activation function. Leaky ReLu solves the problem of 0 output when ReLu6 has negative input, so the SandGlass-L block can retain more feature information. Finally, based on SandGlass-L, SGLPANet (SandGlass-L Path Aggregation Network) is proposed to alleviate the problem of information loss caused by dimension transformation and retain more feature information. The backbone network of the algorithm in this paper is CSPDarknet53, and the SPP module and EIRM act after the backbone network. The neck network is SGLPANet. Experiments on the NWPU VHR-10 dataset show that the algorithm in this paper can well solve the problem of low detection accuracy caused by mismatched receptive fields and missing feature information. It not only improves the accuracy of ship target detection, but also achieves good results when extended to other categories. At the same time, the extended experiments on the LEVIR dataset show that the algorithm also has certain applicability on different datasets. Full article
(This article belongs to the Special Issue Computer Vision Techniques: Theory and Applications)
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18 pages, 3740 KiB  
Article
Multi-Scale Memetic Image Registration
by Cătălina Lucia Cocianu and Cristian Răzvan Uscatu
Electronics 2022, 11(2), 278; https://doi.org/10.3390/electronics11020278 - 16 Jan 2022
Cited by 5 | Viewed by 1573
Abstract
Many technological applications of our time rely on images captured by multiple cameras. Such applications include the detection and recognition of objects in captured images, the tracking of objects and analysis of their motion, and the detection of changes in appearance. The alignment [...] Read more.
Many technological applications of our time rely on images captured by multiple cameras. Such applications include the detection and recognition of objects in captured images, the tracking of objects and analysis of their motion, and the detection of changes in appearance. The alignment of images captured at different times and/or from different angles is a key processing step in these applications. One of the most challenging tasks is to develop fast algorithms to accurately align images perturbed by various types of transformations. The paper reports a new method used to register images in the case of geometric perturbations that include rotations, translations, and non-uniform scaling. The input images can be monochrome or colored, and they are preprocessed by a noise-insensitive edge detector to obtain binarized versions. Isotropic scaling transformations are used to compute multi-scale representations of the binarized inputs. The algorithm is of memetic type and exploits the fact that the computation carried out in reduced representations usually produces promising initial solutions very fast. The proposed method combines bio-inspired and evolutionary computation techniques with clustered search and implements a procedure specially tailored to address the premature convergence issue in various scaled representations. A long series of tests on perturbed images were performed, evidencing the efficiency of our memetic multi-scale approach. In addition, a comparative analysis has proved that the proposed algorithm outperforms some well-known registration procedures both in terms of accuracy and runtime. Full article
(This article belongs to the Special Issue Computer Vision Techniques: Theory and Applications)
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