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CS223B Assignment 1 Recap

CS223B Assignment 1 Recap. Lots of Solutions!. 37 Groups Many different approaches Let’s take a peek at all 37 results on one image from the test set. Possible Things to Look For. Symmetry Color Shadow Corners Edges Texture Taillights. Road Finding Tactic 1.

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CS223B Assignment 1 Recap

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  1. CS223BAssignment 1 Recap

  2. Lots of Solutions! • 37 Groups • Many different approaches • Let’s take a peek at all 37 results on one image from the test set

  3. Possible Things to Look For • Symmetry • Color • Shadow • Corners • Edges • Texture • Taillights

  4. Road Finding Tactic 1 • Run adaptive flood fill on the image starting at the pixels near the bottom • Everything that gets filled in is considered road • Holes inside the road are likely car candidates

  5. Road Finding Tactic 2 • Run an edge detector (such as Prewitt or Canny) and a Hough transform, and then look for lines with the right approximate angle and position.

  6. Road Finding Tactic 3 • Sample the color from some pixels directly in front of car (bottom of image) • Generate a difference map using this color • Run an edge detector on this difference map, followed by a Hough transform

  7. Road Finding Tactic 4 • Assume the road is in pretty much the same place in every image and hard code it in

  8. Edge Approach 1 • Detect edges with Canny detector • Dilate image to join nearby edges • Fill in closed regions

  9. Edge Approach 2 • Custom edge mask that considers vertical and horizontal edges in likely configurations

  10. Shadow Finding • Use a mask that enhances horizontal edges • Look for several adjacent rows of dark pixels in the road region

  11. Corner Approach • Compute strong corners • Cluster them with kmeans • Find the densest clusters and compute their convex hulls

  12. Texture Approach • Look for regions of high frequency • Find density of edges in rectangular regions • Mask or sum the derivative across rectangular regions • Take FFT of image and set magnitudes to 1, leaving only phase info; inverse FFT then enhances portions with many frequencies

  13. Taillight Detection • Transforming the color space can help spot the reddest pixels • Groups of these pixels are good candidates for taillights • If there’s a horizontal pair of taillights, even better • Or, use a mask with the pair built in

  14. Going from Pixels to Regions • Count the number of “car pixels” in square regions; if above a threshold, consider it a car region

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