Lecture
This lecture covers the fundamentals of image formation and transformation in computer vision. It begins with a discussion on the basic linear model of a camera, emphasizing the importance of projective geometry and the role of optics in capturing images. The instructor explains how different types of eyes in nature, such as pinhole and lens-based eyes, relate to camera models. The lecture then transitions to image transformations, detailing various mathematical transformations like affine and projective transformations, and their applications in aligning images. The concept of correspondences between images is introduced, highlighting the shift from handcrafted features to data-driven approaches using neural networks for finding correspondences. The lecture concludes with a discussion on dynamic perspectives in video, including motion sensing and optical flow, and the challenges of 3D reconstruction from 2D images. Overall, the lecture provides a comprehensive overview of the principles underlying image processing and computer vision.