Course Objectives

This is an introductory course on computer vision and computational photography. This course will explore image formation, image features, image segmentation, image stitching, image recognition, motion estimation, 3D point clouds and will touch upon basics of augmented reality.

Topics

The following list of topics is very tentative. Depending on time, some topics may be added or dropped, and the order of topics may change.

Introduction:

What is Computer Vision? Ongoing Research and Application Areas

Image Formation:

Geometric aspects, Radiometric Aspects, Digital Images, The Human Eye, Camera parameters

Filters:

Linear Filters and Convolution, Spatial Frequency and Fourier Transform, Sampling and Aliasing, Noise Reduction

Edge Detection:

Gradient based Edge Detectors, Laplacian, Parametric Models

Other Image Features:

Hough Transform, Ellipse fitting, Deformable contours

Lightness and Color:

Surface Reflectance, Recovering Lightness, The Physics of Color Human Color Perception, Color Representations

Camera Calibration:

Intrinsic Parameters, Extrinsic Parameters

Muliple View Geometry:

Stereo, The Correspondence Problem, Epipolar Geometry, 3D Reconstruction

Motion:

The Image Motion Field, Estimation of 3D Motion and Structure, Segmentation on the basis of different Motion, Image Compression

Shape from Single Image Cues:

Surface Descriptions, Shape from Contours, Shape from Shading, Shape from Texture