compviz.md

Hough Transform

1. Create a grid of parameter values
2. Each point votes for a set of parameters, incrementing those values in grid
3. Find maximum or local maxima in grid

Good

• Robust to outliers: each point votes separately
• Fairly efficient (much faster than trying all sets of parameters)
• Provides multiple good fits

Bad

• Same sensitivity to noise
• Bin size trades off between noise tolerance, precision, and speed/memory
  • Can be hard to find sweet spot
• Not suitable for more than a few parameters
  • grid size grows exponentially

Common applications

• Line fitting (also circles, ellipses, etc.)
• Object instance recognition (parameters are affine transform)
• Object category recognition (parameters are position/scale)