Intendierte Lernergebnisse
Welcome to the introductory course on the fundamentals of digital image processing. This course will lay the groundwork for understanding how to effectively process images and extract valuable information from them.Throughout the course, you will gain a solid understanding of the basic concepts and techniques used in image processing. You will learn how to apply various methods to enhance, manipulate, and analyze images, enabling you to extract the desired information effectively. By the end of the course, you will have achieved the following learning outcomes: Proficiency in applying fundamental image processing methods: You will be equipped with the necessary skills to perform essential image processing tasks, such as filtering, segmentation, and enhancement. You will gain hands-on experience in implementing these techniques using industry-standard tools.Understanding of image features and object recognition: You will develop a deep understanding of image features, including edges, textures, and shapes, and learn how to utilize them for object recognition and classification. You will explore advanced algorithms and methodologies used in this context.Application of image processing knowledge to real-world problems: The acquired knowledge and skills in image processing will be applied to solve practical problems encountered in various industries and research domains. You will gain the ability to analyze and interpret images, enabling you to address challenges in fields such as medical imaging, surveillance, and remote sensing.
Lehrmethodik inkl. Einsatz von eLearning-Tools
The course will be structured into modules, each focusing on key aspects of image processing, such as filtering, segmentation, enhancement, object recognition, and real-world applications. Teaching will involve interactive lectures using visual aids, real-world examples, and live demonstrations to encourage questions and discussions. Practical exercises and assignments will require students to apply the learned concepts using industry-standard tools, promoting hands-on experience. Students will be organized into small teams for group projects, aimed at solving real-world problems collaboratively, fostering practical application of knowledge, and developing problem-solving skills. A dedicated eLearning platform will provide access to a variety of online resources including video tutorials, reading materials, and software tools, serving as a repository for all course materials and allowing students to reinforce their learning at their convenience. Regular quizzes and assessments will be conducted to evaluate students' understanding and practical application of concepts, with feedback provided to identify areas for improvement and enhance the learning experience.
Inhalt/e
Understanding the concept of image (e.g., camera technology, digital images, image types and representation, thermal images)Simple image manipulations (e.g., color depth, size, cropping)Image filtering and convolutionsMorphological transformation and thresholdingGeometric transformationsFeature detectionImage registration and stitchingImage restoration (e.g., de-blurring, interpolations, Fourier domain)
Erwartete Vorkenntnisse
For this course, prerequisites typically include basic programming skills, preferably in Python, as many image processing tasks involve coding. A strong mathematical background in linear algebra, calculus, and statistics is crucial for understanding and implementing image processing techniques.
Literatur
Digital Image Processing (third Edition); Rafael C. Gonzalez, Richard E. Woods.Computer Vision: Algorithms and Applications; Richard Szeliski.