1. Outline
Announcement
Perceptual organization, grouping, and segmentation
Hough transform
Read Chapter 17 of the textbook
File: week14-m.ppt

2. Visual Perception Modeling
Announcement
Class evaluation this coming Friday
Please come to class on time so that we can start the class evaluation first
Class presentation
Your presentation should be about 20 minutes following a brief question-and-answer section which is about 5 minutes
4/6/2019
Visual Perception Modeling

3. Visual Perception Modeling
Introduction – cont.
Computational models/implementations
There are generally two kinds of computational models/implementations for segmentation
Based on homogeneity measure to group pixels with similar attributes together
Region growing/split-and-merge
Based on discontinuity of attributes to detect boundaries/contours of regions
Active contours
4/6/2019
Visual Perception Modeling

4. Visual Perception Modeling
Introduction – cont.
Hough transform
An algorithm to group edge points from edge detectors or from any other process
4/6/2019
Visual Perception Modeling

5. Visual Perception Modeling
Hough Transform
Straight line case
Consider a single isolated edge point (xi, yi)
There are an infinite number of lines that could pass through the points
Each of these lines can be characterized by some particular equation
4/6/2019
Visual Perception Modeling

6. Visual Perception Modeling
Hough Transform – cont.
4/6/2019
Visual Perception Modeling

7. Visual Perception Modeling
Hough Transform – cont.
Hough transform algorithm
1. Find all of the desired feature points in the image
2. For each feature point
For each possibility i in the accumulator that passes through the feature point
Increment that position in the accumulator
3. Find local maxima in the accumulator
4. If desired, map each maximum in the accumulator back to image space
4/6/2019
Visual Perception Modeling

8. Visual Perception Modeling
Hough Transform – cont.
A better way of expressing lines for hough transform
4/6/2019
Visual Perception Modeling

9. Visual Perception Modeling
Hough Transform – cont.
4/6/2019
Visual Perception Modeling

10. Visual Perception Modeling
Hough Transform – cont.
4/6/2019
Visual Perception Modeling

11. Visual Perception Modeling
Hough Transform – cont.
Circles
Hough transform can also be used for circles
4/6/2019
Visual Perception Modeling

12. Visual Perception Modeling
Hough Transform – cont.
Here the radius is fixed
4/6/2019
Visual Perception Modeling

13. Visual Perception Modeling
Hough Transform – cont.
A 3-dimensional parameter space for circles in general
4/6/2019
Visual Perception Modeling

14. Visual Perception Modeling
Hough Transform – cont.
More complicated shapes
As you can see, the hough transform can be used to find shapes with arbitrary complexity as long as we can describe the shape with some fixed number of parameters
The number of parameters required indicates the dimensionality of the accumulator
4/6/2019
Visual Perception Modeling

15. Generalized Hough Transform
Some shapes may not be easily expressed using a small set of parameters
In this case, we explicitly list all the points on the shape
This variation of Hough transform is known as generalized Hough transform
4/6/2019
Visual Perception Modeling

16. Visual Perception Modeling
Hough Transform – cont.
Implementation issues
Quantization of the accumulator space
Utilization of additional information
For line-matching Hough transform, the orientation of an edge point from the Canny edge detector can be used to limit the votes in the accumulator space
Smoothing the accumulator
To reduce the effects of noise
Gray-level voting
4/6/2019
Visual Perception Modeling

17. Visual Perception Modeling
Hough Transform – cont.
Implementation issues - continued
Refining the accumulator
Find a maximum and vote only near the maximum with a higher resolution of the parameter space
Randomized Hough transform
4/6/2019
Visual Perception Modeling