What is Computer Vision? Ongoing Research and Application Areas.
Geometric aspects, Radiometric Aspects, Digital Images, The Human Eye, Camera parameters.
Linar Filters and Convolution, Spatial Frequency and Fourier Transform, Sampling and Aliasing, Noise Reduction small.
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.
Multiple View Geometry:
Stereo, The Correspondence Problem, Epipolar Geometry, 3D Reconstruction.
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.
There is no required text. We will distribute material from a variety of sources. Grading Policy: There will be six assignments/projects in the class accounting for 60% of the grade. There will be a midterm and a final exam each accounting for 20% of the grade. Assignments handed in by the deadline will receive a 5% bonus.
Lecture 1 - Jan 28th: Correlation and Convolution [pdf]