Course Details

 

Course Details Language of Instruction English Level of Course Unit Doctorate's Degree Department / Program ELECTRICAL AND COMPUTER ENGINEERING Type of Program Formal Education Type of Course Unit Elective Course Delivery Method Face To Face Objectives of the Course • Explaining the fundamental principles and diverse types of image fusion techniques to build a comprehensive understanding of their functionality and applications. • Analyzing the performance of various image fusion algorithms by evaluating their effectiveness in addressing specific real-world challenges. • Designing advanced image fusion methods to solve interdisciplinary problems across domains such as medical imaging, remote sensing, and robotics. • Proposing innovative research ideas and solutions in the field of image fusion by integrating knowledge from multiple disciplines and applying scientific methodologies. Course Content Image Fusion Algorithms and Applications is an advanced PhD course focusing on techniques that combine information from multiple images or sensors to create a single enhanced image. The course covers fundamental concepts, state-of-the-art algorithms, and practical applications across domains like enhanced night vision, remote sensing, medical imaging, and robotics. Students will explore pixel-level, feature-level, and decision-level fusion methods, alongside machine learning-based approaches, with a focus on mathematical foundations, optimization, and evaluation metrics. Through programming assignments and research projects, students will develop and implement innovative solutions, equipping them with the expertise to tackle real-world challenges and contribute to advancements in image fusion technology. Course Methods and Techniques Introduction to Image Fusion Types of Image Fusion Techniques (Pixel, Feature, Decision-Level) Optimization, Machine Learning and Deep Learning in Image Fusion Applications in Remote Sensing, Medical Imaging, Survaillence Emerging Trends and Challenges in Image Fusion Research Methodologies in Image Fusion Case Studies and Projects in Image Fusion Prerequisites and co-requisities None Course Coordinator Associate Prof.Dr. Rifat Kurban - rifat.kurban@agu.edu.tr Name of Lecturers Associate Prof.Dr. RIFAT KURBAN Assistants None Work Placement(s) No Recommended or Required Reading Resources Kurban, R. (2023). Gaussian of Differences: A Simple and Efficient General Image Fusion Method. Entropy, 25(8), 1215. https://doi.org/10.3390/e25081215 Course Notes Tania Stathaki, Image Fusion: Algorithms and Applications, 2008, Elsevier. Documents Tania Stathaki, Image Fusion: Algorithms and Applications, 2008, Elsevier. Assignments - Exams - Course Category Mathematics and Basic Sciences %30 Engineering %30 Engineering Design %20 Social Sciences %0 Education %0 Science %10 Health %0 Field %10

 
 
 
 
  
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