Week 4 Hough Transform and PCA

Question 1

What is the Hough Transform used for?

Answer 1

The Hough Transform is a technique for detecting shapes in images, like lines and circles.

Question 2

How does the Hough Transform map points in image space?

Answer 2

It maps points in an image space to curves in a parameter space.

Question 3

How is the line equation reformulated in the Hough Transform?

Answer 3

For line detection, the equation y = mx + c is reformulated as ρ = xcos(θ) + ysin(θ).

Question 4

What does the Hough space accumulate?

Answer 4

The Hough space accumulates votes for parameter combinations, identifying strong candidates.

Question 5

What is the purpose of the Probabilistic Hough Transform?

Answer 5

The Probabilistic Hough Transform optimises performance by sampling points randomly.

Question 6

What input is commonly used for the Hough Transform?

Answer 6

Edge maps from detectors like Canny are commonly used as input for the Hough Transform.

Question 7

What are accumulator arrays used for in the Hough Transform?

Answer 7

Accumulator arrays store votes for potential shape parameters during the transform.

Question 8

What is PCA in computer vision?

Answer 8

PCA is a dimensionality reduction technique widely used in computer vision and data analysis.

Question 9

What does PCA transform data into?

Answer 9

PCA transforms data to a new coordinate system based on variance.

Question 10

What are principal components?

Answer 10

Principal components are the directions with maximum variance in the data.