Textbook:

• David Forsyth and Jean Ponce. Computer Vision: A Modern Approach, 2nd edition.

Supplementary reading:

• Christopher Bishop. Pattern Recognition and Machine Learning. (PRML)

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1 Image Formation

1.1 Geometric Camera Models

1.1.1 Pinhole Perspective

1.1.2 Weak Perspective and Orthographic Projection

1.2 Light and Shading

1.2.1 Lambertian+Specular Model

1.2.2 Photometric Stereo

1.3 Color

1.3.1 Human Color Perception and Gamma Correction

1.3.2 Color Models and Spaces

1.3.2.1 RGB/RGBA Models: sRGB

1.3.2.2 Luminance/Luma and Chrominance/Chroma: YUV

1.3.2.3 Hue and Saturation: HSL, HSV and HSI

1.3.2.4 CIE

1.3.2.5 CMYK

1.3.3 Tone Mapping and High Dynamic Range (HDR)

2 Image Processing and Vision

2.1 Geometric Transformations

2.2 Linear Filters and Convolution

2.3 Features and Descriptors

2.3.1 Edge Detection

2.3.2 Corner Detection

2.3.3 Blob Detection

2.3.4 SIFT and SURF

2.3.5 Histogram of Oriented Gradients (HOG)

2.4 Texture

2.5 Segmentation

2.6 Fitting

2.7 Tracking

2.8 Stereo Vision

3 High-Level Vision

3.1 Registration

3.2 Classification

3.3 Object and Pattern Recognition

3.3.1 Facial Recognition

3.3.2 Optical Character Recognition (OCR)

3.4 Content-Based Image Retrieval (CBIR)