Advancements in Edge Detection Techniques for Image Enhancement: A Comprehensive Review

المجلة: International Journal of Artificial Intelligence & Robotics (IJAIR),

سنة النشر: 2024

تاريخ النشر: 2024-06-10

Edge detection is a fundamental algorithm in image processing and computer vision, widely applied in various domains such as medical imaging and autonomous driving. This comprehensive literature review critically evaluates the latest edge detection methods, encompassing classical approaches (Sobel, Canny, and Prewitt) and advanced techniques based on deep learning, fuzzy logic, and optimization algorithms. The review summarises the significant contributions and advancements in the field by synthesizing insights from numerous research papers. It also examines the combination of edge detection with current image processing methods and discusses its impact on real-life applications. The review highlights the strengths and limitations of existing edge detection strategies and proposes future avenues for investigation. Various research shows that classical edge detection methods like Sobel, Canny, and Prewitt still play a significant role in the field. However, advanced methods utilizing deep learning, fuzzy logic, and optimization algorithms have shown promising results in enhancing edge detection accuracy. Combining edge detection with current image processing methods has demonstrated improved clarity and interpretation of images in real-life applications, including medical imaging and machine learning systems. Despite the progress made, there are still limitations and challenges in existing edge detection strategies that require further investigation. Future research should address these shortcomings and explore new edge detection algorithm development avenues. By understanding the current state of the art and its implications, researchers and practitioners can make informed decisions and contribute to advancing edge detection in image processing and analysis. Overall, this review serves as a valuable guide for researchers and practitioners working in the field, providing a thorough understanding of the state-of-the-art edge detection techniques, their implications for image processing, and their potential for further development.

Advancements and Applications of Convolutional Neural Networks in Image Analysis: A Comprehensive Review.

المجلة: Jurnal Ilmiah Computer Science

سنة النشر: 2024

تاريخ النشر: 2024-07-15

Convolutional Neural Networks (CNNs) have revolutionized image analysis, extracting meaningful features from raw pixel data for accurate predictions. This paper reviews CNN fundamentals, architectures, training methods, applications, challenges, and future directions. It introduces CNN basics, including convolutional and pooling layers, and discusses diverse architectures like LeNet, AlexNet, ResNet, and DenseNet. Training strategies such as data preprocessing, initialization, optimization, and regularization are explored for improved performance and stability. CNN applications span healthcare, agriculture, ecology, remote sensing, and security, enabling tasks like object detection, classification, and segmentation. However, challenges like interpretability, data bias, and adversarial attacks persist. Future research aims to enhance CNN robustness, scalability, and ethical deployment. In conclusion, CNNs drive transformative advancements in image analysis, with ongoing efforts to address challenges and shape the future of AI-enabled technologies.

Design and Enhancement of a CNN Model to Augment the Face Recognition Accuracy

المجلة: 2022 3rd International Informatics and Software Engineering Conference (IISEC)

سنة النشر: 2022

تاريخ النشر: 2022-12-29

In the last decade, smart home security applications have relied more on human biometrics in their functions, due to the reliability and the high-precision results these technologies provide. Face recognition is one of the popular biometrics in the field of image processing technologies. Human face recognition processing is a complicated operation that involves different factors and circumstances such as the illumination degree and the position of the face that affects the final recognition rate. In this research, the Convolution Neural Network (CNN) architecture is used in the extracting phase of significant features of the face shape, and the SoftMax classification layer was used to identify faces in the fully connected CNN layer. This paper provides an update of CNN architecture by applying a three-batch normalization layer to the CNN design. By applying this modification, the system network speed increased with a better recognition rate. The recognition rate also increased by applying two DWT levels with a bio5.5 filter to the training group of the database images and the tested image before applying the PCA dimensional reduction algorithm instead of using the PCA algorithm alone. The obtained face recognition rates have been improved to 99.75% by applying the proposed CNN scheme. While applying the proposed hybrid approach (using the PCA next to applying DWT-2 levels with bior5.5 filter) has registered a 99.25% recognition rate compared to a 96.75% recognition rate when obtained by applying the PCA method alone. The research has adopted using a set of 360 training images and 40 test images set of the standard ORL Database in its work.

HAAR WAVELET FOR NUMERIC SOLUTION OF RLC CIRCUIT DIFFERENTIAL EQUATIONS

المجلة: The Journal of Duhok University

سنة النشر: 2022

تاريخ النشر: 2022-11-09

The wavelet transformation is a mathematical method developed over the past decades to be adapted for applications in the fields of science and engineering. The wavelet transform can be applied in the field of numerical analysis to solve the differential equation. This paper is concerned with applying Haar wavelet methods to solve an ordinary differential equation for an RLC series circuit with a known initial state. The matrix construction calculations are proposed in a simple way. Three numerical mathematical examples are shown that include second-order differential equations with variable and constant coefficients. The results showed that the proposed method is quite reasonable while comparing the solution of second order systems by Haar wavelet method with the exact solution in the context of serial RLC circuit. Moreover, the use of Haar waves is found to be simple, accurate, with flexible and appropriate arithmetic computational costs.