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Part 1: Image Formation and Image Models

1. Cameras

   • Pinhole cameras, including perspective and affine projection
   • Cameras with lenses, including geometric optics, thin lenses and real lenses
   • The human eye
   • Sensing, including CCD cameras and sensor models

   Slides

2. Geometric Camera Models

   • Elements of analytical Euclidean Geometry, including coordinate systems, homogenous coordinates, and rigid transformations
   • Camera parameters and perspective projection, including intrinsic and extrinsic parameters
   • Affine cameras and affine projection

   Slides

3. Geometric Camera Calibration

   • Least squares parameter estimation, including linear least squares
   • A linear approach to camera calibration, including estimation of the projection matrix, extrinsic and intrinsic parameters and degenerate point configurations
   • Taking radial distortion into account, including estimation of the projection matrix, extrinsic and intrinsic parameters and degenerate point configurations
   • Analytical photogrammetry
   • An application: mobile robot localization

   Slides

4. Radiometry --- Measuring Light

   • Light in space, including foreshortening, solid angle, radiance
   • Light at surfaces, including simplifying assumptions, the bidirectional reflectance distribution function and the radiometry of thin lenses
   • Important special cases, including radiosity, directional hemispheric reflectance, lambertian surfaces and the lambertian+specular model

   Slides

5. Sources, Shadows and Shading

   • Qualitative radiometry
   • Sources and their effects, including radiometric properties of light sources, point sources, line sources and area sources
   • Local shading models, including models for point sources, area sources and their shadows, and ambient illumination
   • Photometric stereo
   • Interreflections: global shading models, including an interreflection model, solving for radiosity and the qualitative effects of interreflections

   Slides

6. Color

   • The physics of color, including radiometry, the color of sources, and the color of surfaces
   • Human color perception, including color matching and color receptors
   • Representing color, including linear color spaces, non-linear color spaces and spatial and temporal effects
   • A model for image color, including cameras, and specularity finding
   • Surface color from image color, including color constancy in people, inferring lightness and finite dimensional linear models

   Slides

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