Course info

Image processing is a multidisciplinary field at the intersection of computer science, mathematics, and engineering, focused on extracting meaningful information from digital images and manipulating them for various applications. With the rapid advancements in technology, image processing has become a cornerstone of innovations in fields such as healthcare, surveillance, multimedia, robotics, and autonomous systems. This course provides a comprehensive understanding of the theoretical foundations and practical applications of image processing techniques.

Through the modules outlined in this course, learners will explore:

• Fundamentals of Image Processing: Building a strong foundation, learners will begin by understanding how images are formed, represented, and manipulated. Topics include visual perception, sampling, quantization, intensity transformations, and techniques like histogram equalization to enhance image contrast effectively.

• Image Restoration and Filtering: Noise and distortion are common challenges in image processing. This module equips learners with methods to restore degraded images using spatial filters and advanced techniques like the Wiener filter to enhance clarity and reduce noise.

• Colour Spaces and Colour Image Processing: Colour plays a vital role in image analysis and recognition. This module introduces various colour spaces such as RGB, HSV, and YUV, and explores techniques for leveraging colour information to enhance and analyze images for diverse applications.

• Morphological Image Processing: Morphological operations are essential for understanding the structure and shape of objects within images. This module covers fundamental techniques like erosion and dilation, along with advanced methods such as the hit-or-miss transform, shape decomposition, and thinning.

• Advanced Image Processing and Vision Techniques: At the forefront of image analysis, this module delves into advanced topics such as segmentation using edge detection, thresholding, and region-based methods. Learners will also gain insights into image compression techniques, vision systems, object recognition, and even human motion categorization. Techniques for 3D shape reconstruction from stereo images will provide a glimpse into the world of computer vision.