Greetings, fellow Nawala! Hope you are always in good health.
This is the IAES Newsletter of the Institute of Advanced Engineering and Science. Today we want to share how image processing plays a role in our daily life. Meanwhile, image processing is a branch of science that extracts from an image to find the information it contains. As was done by Marzouk and Azeem in 2022, they are conducting research on image processing to detect congestion. The data is based on images of passing vehicles taken via CCTV installed at several street points. More details can be seen in the following article:
Marwa A. Marzouk, Amr Abd El Azeem
Recent studies have proven that vehicle tracking and detection play an important role in traffic density monitoring. Traffic overcrowding can be effectively controlled if the number of vehicles expected to pass through a congested intersection can be predicted ahead of time. To overcome such impact of traffic congestion the proposed system presents a framework, using motion detection algorithms and “ThingSpeak” internet of things (IoT) platform which is used in to calculate traffic density, the proposed system capturing video with wireless internet protocol (IP) cameras and broadcasting it to the server where motion detection algorithms as background subtraction are used to obtain a quick overview of traffic density, To save cost and improve the solution, the suggested system utilizes image processing techniques as well as the IoT analytic platform “ThingSpeak” to monitor traffic density. Finally, the suggested method is used to manage traffic flow and avoid traffic crowded. The results of the studies show that the integration of IoT-based technologies with a modified background subtraction technique is effective. This method might be enhanced further to detect vehicles that break traffic laws. We may also improve this system by detecting the presence of emergency vehicles (including an ambulance or fire truck) and granting priority to those cars.
In addition to detecting pictures of a vehicle, image processing can also detect other moving objects. Gheisari et al. conducted research on image processing to detect human tracking or human movement. The results of this research are very useful for the development of robotics and autonomous driving. More details can be seen in the following article:
A novel enhanced algorithm for efficient human tracking
Mehdi Gheisari, Zohreh Safari, Mohammad Almasi, Amir Hossein Pourishaban Najafabadi, Abel Sridharan, Ragesh G K, Yang Liu, Aaqif Afzaal Abbasi
Tracking moving objects has been an issue in recent years of computer vision and image processing and human tracking makes it a more significant challenge. This category has various aspects and wide applications, such as autonomous deriving, human-robot interactions, and human movement analysis. One of the issues that have always made tracking algorithms difficult is their interaction with goal recognition methods, the mutable appearance of variable aims, and simultaneous tracking of multiple goals. In this paper, a method with high efficiency and higher accuracy was compared to the previous methods for tracking just objects using imaging with the fixed camera is introduced. The proposed algorithm operates in four steps in such a way as to identify a fixed background and remove noise from that. This background is used to subtract from movable objects. After that, while the image is being filtered, the shadows and noises of the filmed image are removed, and finally, using the bubble routing method, the mobile object will be separated and tracked. Experimental results indicated that the proposed model for detecting and tracking mobile objects works well and can improve the motion and trajectory estimation of objects in terms of speed and accuracy to a desirable level up to in terms of accuracy compared with previous methods.
In health sciences, the role of image processing is very helpful in making a diagnosis. Nuseir et al. conducted research on the role of image processing to diagnose nasal symptoms. A more detailed explanation can be seen in the following article:
Computed tomography scans image processing for nasal symptoms severity prediction
Amjad Nuseir, Hasan Albalas, Aya Nuseir, Maulla Alali, Firas Zoubi, Mahmoud Al-Ayyoub, Mohammed Mahdi, Ahmad Al Omari
This paper aims to use a new technique of computed tomography (CT) scan image processing to correlate the image analysis with sinonasal symptoms. A retrospective cross-sectional study is conducted by analyzing the digital records of 50 patients who attended the ear, nose and throat (ENT) clinics at King Abdullah University Hospital, Jordan. The coronal plane CT scans are analyzed using our developed software. The purposes of this software are to calculate the surface area of the nasal passage at three different levels visible on coronal plane CT scans: i) the head of the inferior turbinate, ii) the head of the middle turbinate, and iii) the tail of the inferior turbinate. We employ image processing techniques to correlate the narrowing of nasal surface area with sinonasal symptoms. As a consequence, obstruction in the first level is correlated significantly with the symptoms of nasal obstruction while the narrowing in the second level is related to frontal headache. No other significant correlations are found with nasal symptoms at the third level. In our study, we find that image processing techniques can be very useful to predict the severity of common nasal symptoms and they can be used to suggest treatment and to follow up on the case progression.
In fisheries, image processing can also play a role in facilitating the classification of fish species. Fish is one of the animals that live in water, so it is hard to classify it manually. Al Smadi et al. classify fish based on images with the support of the convolutional neural networks (CNN) classification method. Details regarding the classification can be seen in the following article:
Deep convolutional neural network-based system for fish classification
Ahmad Al Smadi, Atif Mehmood, Ahed Abugabah, Eiad Almekhlafi, Ahmad Mohammad Al-smadi
In computer vision, image classification is one of the potential image processing tasks. Nowadays, fish classification is a wide considered issue within the areas of machine learning and image segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective fish classification method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous fish species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 different state-of-the-art gradient descent-based optimizers, namely adaptive delta (AdaDelta), stochastic gradient descent (SGD), adaptive momentum (Adam), adaptive max pooling (Adamax), Root mean square propagation (Rmsprop), for CNN. Overall, the obtained experimental results show that Rmsprop, Adam, Adamax performed well compared to the other optimization techniques used, while AdaDelta and SGD performed the worst. Furthermore, the experimental results demonstrated that Adam optimizer attained the best results in performance measures for 70-30% and 80-20% splitting experiments, while the Rmsprop optimizer attained the best results in terms of performance measures of 70-25% splitting experiments. Finally, the proposed model is then compared with state-of-the-art deep CNNs models. Therefore, the proposed model attained the best accuracy of 98.46% in enhancing the CNN ability in classification, among others.
Some of the articles above are just a few of the many roles image processing plays in everyday life. To get more information you can access it for FREE at: https://ijai.iaescore.com/, https://ijece.iaescore.com/, and https://ijict.iaescore.com/
by: Busthomi