# Digital Image Processing: Advanced Technologies and Applications
## 3 Conclusions  
The contributions listed in this Special Issue can be combined into three major groups with the following key attributes. **Group 1: Object detection:** In this category, contributions 7 and 10 inspect various object detection methods. In particular, contribution 7 addresses multi-pedestrian detection and tracking. Whereas contribution 10 addresses license plate detection in real-life images in an open environment. **Group 2: Object recognition:** In this category, several state-of-the-art contributions were accepted, which include contributions 1, 2, 3, 6, 8, 13, and 14. The afore-listed contributions either use AI methods or use deep learning methods to inspect various objects. This group is most prominent in this Special Issue and gathers significant scientific findings in the object recognition domain. **Group 3: Image Manipulations:** This group gathers contributions 4, 5, 9, 11, 12, and 15. Specifically, contribution 4 evaluates the quality of aerial images. Whereas 5, 9, 11, 12, and 15 perform image manipulations through various methods listed therein. In these collections, contribution 12 is particularly related to image segmentation, which is currently a challenging task in various real-life scenarios. After thoroughly analyzing the gathered contributions, the following important points are highlighted:  
* With the rapid advancements in AI and machine learning, the use of deep learning in various applications has become obvious in many industries due to its ability to process complex patterns and make reliable predictions. Therefore, deep learning algorithms have found their place in crucial fields, including object detection and recognition, natural language processing, and medical imaging. For instance, CNNs are extensively employed for tasks such as image classification, object detection, segmentation, and medical imaging. Similarly, recurrent neural networks (RNNs) and transformers have advanced the capabilities of many applications. For instance, real-time translation, sentiment analysis, and conversational agents. The development of GPUs and large-scale datasets has further driven deep learning's adoption for solving complex problems with exceptional accuracy and efficiency. * With the evolution of RGB images, several state-of-the-art algorithms have also appeared in the literature. Most of the images related papers collected in this Special Issue address RGB images using an intelligent combination of machine learning-based methods to achieve desired outcomes. **Final Remarks:**_Digital Image Processing: Advanced Technologies and Applications_ will serve as a fundamental resource for researchers and practitioners. It will also assist students who aim to orient their career in machine learning and deep learning. It not only imparts basic knowledge but also stimulates advanced thinking and exploration in recent technological advancements. As the digital imaging domain continues to grow, the insights and methodologies collected in this Special Issue will provide resources and applications for newcomers. The following are a few major takeaways from the collections presented in this Special Issue:  
_Technological Integration:_ The collection presents a combination of digital image processing with advanced technologies, such as machine learning and deep learning, and demonstrates their potential for solving complex, real-world problems. _Algorithmic Development:_ This collection emphasizes the development and optimization of recent algorithms, which process images, extract features, and report efficient processed images results. _Innovative Applications:_ A variety of applications in several domains are collected, which include traffic images, medical imaging, and aerial images. Each manuscript gathered here underscores their practical relevance to modern-day technology.