Special Issue "Machine and Computer Vision Methods for Natural Images in Electronics and Interdisciplinary Applications"
Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 9122
Interests: applied computer science, particularly image processing and analysis; computer vision and machine vision in automation and robotics; image quality assessment; video and signal processing applications in intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
Machine vision and computer vision methods may be considered very rapidly developing areas of research that integrate the interdisciplinary knowledge, making it possible to establish new scientific teams oriented toward new applications in many fields of science and technology. Starting from continuously growing Industry 4.0 solutions, through video surveillance, analysis of images from drones to novel applications in agriculture or even aquaculture, video analysis has become more and more popular. Due to the growing availability of affordable cameras, “smart” wearable electronics, and IoT solutions, often integrated with cameras and visual sensors, the methods of natural image processing are becoming even more important. The verification of some general-purpose image analysis methods for natural images, e.g., in mobile robotics, may also lead to worse results than those achieved for artificial images, leading to a so-called “reality gap”.
Since images acquired by cameras may contains various distortions that are not always the same as those in synthetic images, their presence and amount should also be considered in terms of image quality, potentially influencing the results of their further analysis. In some embedded systems, as well as in many industrial applications, a crucial role is also related to the “explainability” of algorithms, excluding the potential applications of some deep learning solutions.
The aim of this Special Issue on “Machine and Computer Vision Methods for Natural Images in Electronics and Interdisciplinary Applications” is to bring together the research communities interested in computer and machine vision from various departments and universities that focus on electronics, automation, and robotics, as well as computer science.
Topics of interest for this Special Issue include but are not limited to:
- Novel applications of computer vision in autonomous vehicles, video surveillance, and intelligent transportation systems;
- Quality assessment of natural images;
- Feature extraction and image registration based on novel handcrafted features;
- Machine vision for video simultaneous localization and mapping (VSLAM) solutions;
- Image-based navigation of unmanned aerial vehicles (UAVs) and other mobile robots;
- Binarization and segmentation algorithms of natural images;
- Fast image analysis methods for embedded solutions, e.g., using the Monte Carlo method;
- Natural image analysis for Industry 4.0;
- Exploration of data acquired using various sensors for non-destructive evaluation and diagnostics purposes (e.g., thermovision) using image analysis methods;
- Applications of natural image analysis in industry and agriculture.
Dr. Krzysztof Okarma
Manuscript Submission Information
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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 2200 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.
- natural images
- image analysis
- machine vision
- video analysis
- industrial cameras
- image quality
- visual inspection and diagnostics
- industrial and robotic vision systems