*Editorial* **Computer Vision and Machine Learning for Intelligent Sensing Systems**

**Jing Tian**

> Institute of Systems Science, National University of Singapore, Singapore 119615, Singapore; tianjing@nus.edu.sg

> Intelligent sensing systems have been fueled to make sense of visual sensory data to handle complex and difficult real-world sense-making challenges due to the rapid growth of computer vision and machine learning technologies. We can now interpret visual sensory data more effectively thanks to recent developments in machine learning algorithms. This means that in related research, significant attention is now being paid to problems in this field, such as visual surveillance, smart cities, etc.

> The Special Issue offers a selection of high-quality research articles that tackle the major difficulties in computer vision and machine learning for intelligent sensing systems from both a theoretical and practical standpoint. It includes intelligent sensing techniques [1–5], twelve foundational investigations into sense-making methods [6–10], and particular uses of intelligent sensing systems in autonomous driving [11] and virtual reality [12].

**Intelligent sensing techniques**

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#### **Intelligent sense-making techniques**

	- Oh et al. [10] suggested estimating gaze by detecting eye region landmarks through a single eye image. It learns representations of images at various resolutions, and the self-attention module is used to obtain a refined feature map with better spatial information.

**Citation:** Tian, J. Computer Vision and Machine Learning for Intelligent Sensing Systems. *Sensors* **2023**, *23*, 4214. https://doi.org/10.3390/ s23094214

Received: 12 April 2023 Accepted: 17 April 2023 Published: 23 April2023

**Copyright:** © 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **Applications of intelligent sensing systems**


In conclusion, through the wide range of research presented in this Special Issue, we would like to boost fundamental and practical research on applying computer vision and machine learning for intelligent sensing systems.

**Conflicts of Interest:** The author declares no conflict of interest.
