This is an introductory lab course on the fundamentals of digital image processing using Python. It aims to familiarize students with image processing functions and algorithms in Python, enabling them to apply their knowledge in real-world scenarios.By the end of the course, students can program various image-processing methods using Python and further expand their understanding of new applications and programming languages.

The fundamental concepts of image processing will be introduced by drawing upon well-established literature and authoritative references. To supplement these foundational principles and delve into more advanced topics, an interactive approach will be adopted, tailored to the individual potential and abilities of the students. Additionally, the course will incorporate the effective utilization of eLearning tools to facilitate a more engaging and comprehensive learning experience.

Introduction & Fundamentals:This lecture provides an introduction to image processing, covering the fundamental concepts, techniques, and applications in the field.The Basics of Intensity/Point Transformations:This lecture explores the basic concepts of intensity transformations in image processing, focusing on manipulating pixel intensities to enhance or modify images.The Basics of Histogram and Pixels Relationship:This lecture delves into the relationship between histograms and pixel distributions, discussing how histograms can be utilized for image analysis and enhancement.The Basics of Geometric Transformations:In this lecture, students learn about geometric transformations, including rotation, scaling, and translation, and their applications in image processing.The Basics of Image Enhancement (Spatial Domain):This lecture introduces techniques for spatial domain image enhancement, covering methods such as contrast stretching, histogram equalization, and spatial filtering.The Basics of Image Enhancement (Spatial and Frequency Domain):This lecture explores image enhancement techniques in both the spatial and frequency domains, including Fourier analysis and filtering in the frequency domain.The Basics of Edge Detection:This lecture focuses on edge detection algorithms and methods used to identify and extract edges in digital images.The Basics of Segmentation and Morphological Operations:In this lecture, students learn about image segmentation techniques and morphological operations, which involve extracting meaningful regions and manipulating image structures.The Basics of Segmentation Part II:Building upon the previous lecture, this session delves deeper into image segmentation algorithms and advanced techniques for partitioning images into meaningful regions.The Basics of Hough Transform:This lecture covers the Hough transform, a technique used to detect shapes and patterns in images, including lines, circles, and more complex geometries.The Basics of Color Space:This lecture explores different color spaces used in image processing, including RGB, HSL, and CMYK, and discusses color models and conversions between them.

The image processing exam will consist of two main components: Homework assignments and a final project. The exam will assess your understanding of the concepts, techniques, and applications covered in the course.

The exam will cover a range of topics related to image processing. These topics include, but are not limited to:Image restoration: Noise reduction, image deblurring.Image transformation: Fourier transform, wavelet transform.Image segmentation: Thresholding, region-based segmentation, edge detection.Feature extraction: Texture analysis, shape descriptors.Image compression: Lossless and lossy compression techniques.Morphological operations: Dilation, erosion, opening, closing.Object recognition and tracking.Deep learning in image processing.Image processing applications in various fields (medical, surveillance, entertainment, etc.).

The exam will be divided into two major components:Homework Assignments (40%): There will be a total of 4 homework assignments, each accounting for 10% of the final grade. These assignments will require you to apply the concepts learned in class to solve practical image-processing problems. You will need to submit your completed homework assignments through the Moodle platform.Final Project (60%): For the final project, you are required to select an image processing task of your choice and present your solution during the last session of the class. The final project will have two components: the presentation and the code.Presentation (30%): Your presentation should have a minimum of 5 pages and should cover the following sections:Introduction: Introduce the problem and its significance.Problem Definition: Clearly define the image processing task you are addressing.System Architecture: Explain your solution approach and the techniques you used to solve the problem.Input -> Output Samples: Showcase examples of input images and the corresponding processed output.Future Improvement: Discuss potential enhancements or extensions to your solution.Live Demo of System (20%): During the presentation, demonstrate your image processing system in action. Show how it processes images and produces the desired outputs.Code (10%): Along with the presentation, you need to submit the code implementation of your image processing solution. The code should be well-documented and organized, reflecting your understanding of coding practices and the application of image-processing techniques.Both the presentation and the code must be uploaded to the Moodle platform by the specified deadline.