Machine and Computer Vision Methods for Natural Images in Electronics and Interdisciplinary Applications

Dear Colleagues, 

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
Guest Editor

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