Geometric Computer Vision

From David's Wiki
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Notes for CMSC733 Classical and Deep Learning Approaches for Geometric Computer Vision taught by Prof. Yiannis Aloimonos.

Convolution and Correlation

See Convolutional neural network.
Traditionally, fixed filters are used instead of learned filters.

Edge Detection

Two ways to detect edges:

  • Difference operators
  • Models

Image Gradients

  • Angle is given by \(\displaystyle \theta = \arctan(\frac{\partial f}{\partial y}, \frac{\partial f}{\partial x})\)
  • Edge strength is given by \(\displaystyle \left\Vert (\frac{\partial f}{\partial x}, \frac{\partial f}{\partial y}) \right\Vert\)


Sobel operator is another way to approximate derivatives:
\(\displaystyle s_x = \frac{1}{8} \begin{bmatrix} -1 & 0 & 1\\ -2 & 0 & 2\\ -1 & 0 & 1 \end{bmatrix} \) and \(\displaystyle s_y = \frac{1}{8} \begin{bmatrix} 1 & 2 & 1\\ 0 & 0 & 0\\ -1 & -2 & -1 \end{bmatrix} \)