Ce MOOC (Massive Open Online Course) on image processing delves into the interdisciplinary nature of this field, encompassing mathematics, physics, and computer science. From manufacturing lines to medical scanners and satellites, images play a crucial role in extracting ubiquitous information. The course emphasizes the systematic processing of images to overcome acquisition challenges, isolate relevant objects, and conduct analysis. Modules cover essential topics such as pixels, colors, resolution, and quantification, alongside hands-on training in Python programming for image processing operations.
Participants will grasp the rudimentary mathematics and computer science concepts required, empowering them to manipulate algorithms and execute fundamental image processing operations. By the course's conclusion, learners will be adept at loading and observing images, analyzing their quality, enhancing sharpness and contrast, applying blur, and detecting edges. Successful completion of the course warrants a certificate of achievement from Coursera, provided the individual attains a score exceeding 50%.
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This course comprises modules that introduce the interdisciplinary nature of image processing, covering fundamental concepts, historical background, and practical applications.
Module 1 provides an overview of the course objectives and structure, including a brief history of image processing and basic exercises to familiarize participants with the subject matter.
Module 2 delves into the historical context of images and their acquisition, alongside essential mathematical foundations. Participants will engage in exercises related to image definition, resolution, and file formats, supplemented with codecasts for practical implementation.
Module 3 focuses on filtering through convolution and edge detection. It covers basic operations, wave and frequency concepts, linear filters, and convolution, offering practical exercises and codecasts for a hands-on learning experience.
Module 4 explores image enhancement and histogram manipulation, encompassing topics such as histogram equalization and manipulation, and includes an interview with a radiologist. Participants can reinforce their learning through practical exercises and codecasts.
Module 5 delves into noise treatment, covering random variables, Gaussian and salt-and-pepper noise, noise reduction, and a discussion with an opto-electronics engineer. Practical exercises and challenges provide opportunities for application and assessment.
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