Image Processing and Pattern Recognition Based on Deep Learning for Sensing Applications—3rd Edition

Dear Colleagues,

The pattern recognition used in analyzing and interpreting images in sensing applications is today closely tied to artificial intelligence and neural networks based on deep learning. The current trends in the use of neural networks include the following: (a) improvements within established families to enhance statistical performance and efficiency; (b) transfer learning; (c) the use of multiple networks in more complex systems; (d) the merging of decisions by individual networks; (e) the combination of efficient features with neural networks to improve detection and classification performance; and (f) the application of a multimodal approach based on data collection from various sensors. Additionally, combining neural networks with other artificial intelligence classifiers can also improve performance. New deep learning models have also been proven to improve detection, classification, and segmentation performances (for example, Visual Language Models (VLMs), Long Short-Term Memory (LSTM), Vision Transformer (ViT), and Large Language Models (LLMs)). Furthermore, sensors integrated with deep learning can improve sensorial applications in various fields, such as healthcare diagnostics, anomaly detection, traffic prediction, precision agriculture, and smart home systems, among others. Of particular importance in achieving high performance is accurate image collection by sensors. Special attention will be paid to data collection in various fields, including agriculture, medicine, environment, and restricted areas.

This Special Issue aims to publish original research contributions concerning new deep neural network-based approaches in image processing and pattern recognition for sensorial applications in various domains: remote sensing, crop monitoring, restricted zone monitoring, system support in medical diagnosis, emotion detection, and others.

The scope of the Special Issue includes (but is not limited to) the following research areas concerning image processing and pattern recognition, with the aid of new artificial intelligence techniques for sensorial applications:                                            

• Image processing;
• Sensors for various image generation: RGB, multispectral, thermal;
• Collecting data and data fusion from different sensors;
• Multimodal approaches;
• Pattern recognition;
• Image segmentation;
• Object classification;
• Neural networks;
• Deep learning;
• Decision fusion;
• Systems based on multiple neural networks;
• The detection of regions of interest from remote images;
• Industry applications;
• Sensorial domain applications;
• Precision agriculture application;
• Medical application;
• The monitoring of protected areas;
• Disaster monitoring and assessment.

Prof. Dr. Dan Popescu
Prof. Dr. Loretta Ichim
Guest Editors

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