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Christopher Parrish ECE533 Project December 2006

The Hough Transform for Vertical Object Recognition in 3D Images Generated from Airborne Lidar Data. Christopher Parrish ECE533 Project December 2006. Airborne Lidar. GPS Reference. Station . Airport Obstruction Surveying. Lidar Point Cloud.

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Christopher Parrish ECE533 Project December 2006

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  1. The Hough Transform for Vertical Object Recognition in 3D Images Generated from Airborne Lidar Data Christopher Parrish ECE533 Project December 2006

  2. Airborne Lidar GPS Reference Station Airport Obstruction Surveying

  3. Lidar Point Cloud Hough transform- based approach for detecting vertical objects of cylindrical shape: Voxelize 3D Grayscale Intensity Image 3D Sobel operator 3D Grayscale Edge Image Threshold segmentation 3D Binary Edge Image Hough Transform to identify vertical cylinders Vertical objects of interest

  4. 2D Color Image Laser Point Cloud 3D Grayscale Image

  5. Computing Binary Edge Image: Gradient of a 3D image, f(x,y,z): Magnitude of the gradient: 3D Sobel operator (three 3x3x3 filters expressed here as sets of three 2D matrices) Thresholded (binary) edge image

  6. 3D Binary Edge Images

  7. HT Cylinder Detection Algorithm: Assume cylinders are vertical (axes parallel to mapping frame Z axis) => # of parameters reduced from 5 to 3. Representation: (X-s)2+(Y-t)2 = r2 Input = 3D binary edge image Quantize 3D parameter space. • Initialize all accumulator cells to zero. • For each nonzero voxel in 3D binary edge image, step through all values of s and t. At each location: • Solve for r • Round r to its nearest accumulator cell value • Increment counter for that (s,t,r) accumulator cell. • Find entry in 3D accumulator array with highest # of votes.

  8. Cylinders Detected Using Hough Transform:

  9. Comparison of radii & axes locations of HT-detected cylinders with field-surveyed data:

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