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Line Detect

This document explores advanced methods for line detection using distance voting and the Bresenham line algorithm. It discusses two primary methods: the original distance voting technique, which takes 208 seconds, and a more efficient threading approach that reduces this time to 54 seconds. Furthermore, the implications of the Hough transform are analyzed, emphasizing the complexities in identifying optimal line segments in an image. Various criteria are applied to refine line detection, including length thresholds and the handling of cluttered lines in images, particularly focused on highway scenes.

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Line Detect

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  1. Line Detect

  2. Brutal 1.Original Method: use distance vote 208sec  use thread 54sec 2.Vote with bresenham line algorithm 6sec

  3. Tile • 3941point 6sec • 線段完美 • 可以用100%去match • 去掉一些長度<10的完美直線

  4. highway • 線段稍差 • 大概要用80% • 雜線有點多去掉了<35以下的直線

  5. Haugh transform Due to one line in haugh plane is only represented by one point . It’s hard to determine which part of these points can draw the best line.

  6. High way (min 25 sep30 pl0.8)

  7. Tile (min 50 sep5.0 pl0.8)

  8. Highway.raw –sobel 180 (fault)

  9. Tile.raw -Sobel 180 (fault)

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