Image Processing in Python: Manipulation and Convolution Techniques

This lecture covers the fundamentals of image processing using Python, focusing on manipulation techniques and convolution operations. It begins with an introduction to image types, distinguishing between raster and vector images, and explaining how pixel data is structured in memory. The instructor discusses how to create and manipulate grayscale and RGB images using libraries like NumPy and Pillow. Key concepts such as pixel representation, image dimensions, and the importance of data structures for efficient processing are emphasized. The lecture also introduces convolution, explaining how it can be used for image smoothing and edge detection through the application of kernels. The Sobel operator is highlighted as a method for detecting gradients in images. Practical examples demonstrate how to implement these techniques in Python, including creating images, applying filters, and saving results. The session concludes with a discussion on the upcoming mini-project, which involves applying these concepts to real-world image processing tasks, reinforcing the theoretical knowledge gained during the lecture.