1. Automatic cylinder detection using Hough Transform.
T.Rabbani
November 23, 2018
Section of Photogrammetry and Remote Sensing

2. Hough Transform for automatic cylinder detection
85% of objects found in industrial scenes can be approximated by planes, spheres, cones and cylinders
5-free parameters for cylinder.
November 23, 2018

3. Hough Transform for automatic cylinder detection
Space Complexity
Time complexity
Example:
S=100 Cs = 9.3 GB
S=200 Cs = 300 GB
Solution: Two-step approach
Orientation Estimation
Position and Radius estimation
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4. Basic Idea
Behaviour of normals on Gaussian sphere
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5. Step 1: Orientation Estimation
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6. Step 2: Position and Radius Estimation
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7. Uniform Sampling of orientation space
Uniform sampling essential for Hough transform
Place points so that each point represents equal area on sphere surface
No closed form solution
Iterative solution too slow, as Hough transform needs bi-directional mapping
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8. Spherical sampling linear sampling of and Highly nonlinear
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9. Cartesian sampling Use implicit equation of sphere
Uniformaly sample x and y
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10. Approximate Uniform Sampling
Number of samples in phi =
For mapping back and forth:
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11. Approximate Uniform Sampling
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12. Example
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13. Example
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14. Results
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15. Results on NAM modeling
20 million points
Processing:
Segmentation
Automatic cylinder and plane detection
Planar patches: 946
Cylinders: 1392
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16. Problem: Model Selection
Domains of planes and cylinders overlap
A cylinder can be represented by a number of planar patches
A plane by a set of cylindrical segments
(a)
(b)
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17. Solution: Hypothesis verification
Hough transform will always give a hit
Consider each hit as a strong hypothesis
Employ model matching for hypothesis verification
Accept only if hypothesis verification is successful
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18. Planarity Test Histogram of Distances Angles with normal
Aspect ratio =
Max Extent/Min Extent
Max-extent
Min-extent
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19. Cylindricality Test Difference between predicted and actual normal
Distribution of points on cylinder surface
Max min radius
theta
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20. Results on NAM modeling
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21. Future Plans Results refinements Merging of multiple hits
Connection of separated segments due to occlusions etc
Automatic constraint detection
Detection and fitting of curves
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22. Questions:
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