# Digital Image Processing: Advanced Technologies and Applications
## 2 An Overview of Published Articles  
During the past three decades, a large number of diverse methods have appeared in computer vision and machine learning. Many of them utilize conventional machine learning. However, the recent trend in deep learning has yielded encouraging results. This section gives a brief overview of the works collected in this Special Issue. In contribution one, researchers proposed an AI-enabled setup to analyze animal behavior with the objective of providing better flexibility and scalability to make the proposed setup more feasible. One of the interesting aspects of this work is that users can compliantly extend different behavior recognition algorithms to recognize animal behaviors and enjoy convenient human-computer interaction through natural language descriptions. A case study is discussed that evaluates behavioral variations between sick and healthy animals in a medical laboratory. License plate recognition (LPR) is a key part of current intelligent systems that locate and identify varying license plates. LPR is a challenging task due to the various designs of LPs, a lack of standard LP templates, unconventional outlines, and angle dissimilarities/occlusion. These factors influence the appearance of the LP and degrade the detection and recognition abilities of algorithms. However, recent rising trends in the development of machine learning algorithms have prompted authors to solve this problem, which is the second contribution in this Special Issue. Particularly, this contribution presents a novel LPR algorithm to solve the aforedescribed challenges. This method is composed of three interconnected steps: Initially, a vehicle is detected using the Faster-RCNN algorithm. Next, the LP is spotted by applying the morphological operations of imaging. Lastly, LPR is accomplished using a deep learning network. Experiments conducted on several datasets indicate a mean LPR accuracy of over 96% on three different datasets. The third contribution in this manuscript is about Urdu numeral classification and recognition. Urdu is one of the most complex languages, as it is a combination of several languages. Therefore, its character recognition is a difficult task. It is a bidirectional language that induces complexities during the recognition procedure. This contribution uses CNN and its variants to extract features, which are used by the Softmax activation function and SVM classifier. The obtained results are compared with GoogLeNet and the residual network (ResNet). This contribution reports 98.41% accuracy with the Softmax classifier and 99.0% with the SVM classifier. For GoogLeNet, the obtained accuracies are 95.61% and 96.4%, respectively, on ResNet. Unmanned aerial vehicle (UAV) image capture is a promising means for acquiring geospatial data. Securing even and consistent quality in UAV images is hard due to the use of low-cost steering devices and non-surveying cameras. In addition, no specific procedures exist to perform quantitative tests on UAV images. Hence, in the fourth contribution, the authors conducted a modulation transfer function (MTF) investigation using a slanted-edge target and a ground sample distance (GSD) analysis to verify the basics of MTF analysis. This was used to verify the basics of MTF analysis in assessing UAV image values. The accurate extraction of individual features in multi-view and multi-modal datasets is a difficult topic. In the fifth contribution, researchers present PhotoMatch, an open-source tool for multi-view and multi-modal feature-based image matching. The software contains several recently developed methods to process, extract, and match features. It also offers tools for a thorough assessment and judgement of the numerous methods and allows the user to select the top combination of methods for every modality in the dataset. A set of thirteen case studies, which included six multi-view and six multi-modal image datasets, were processed by following different methodologies.