Feature Based Stereo Matching Using Two-Step Expansion

Algorithm 1

Two-step expansion algorithm.

Input: A pair of rectified images and from different viewpoints of one scene;

     Set the values of ratio, , and .

Output: The disparity map with respect to .

Begin:

 Step 1. Finding initial point correspondences by using state-of-the-art matching method;

 Step 2. Using the mean-shift to partition the image to different areas denoted as: ;

 Step 3. Assigning the points into the corresponding area ;

 Step 4. Removing the coarse mismatches in the area by using regional affine transformation;

 Step 5. Computes gradient of each pixel in the image and selects the pixels

    whose gradient as our candidate feature points;

 Step 6. Assign each feature to a corresponding label ;

 Step 7. The first step for the matched feature expansion

  Repeat:

   for: : size()

    ensure via to: ;

    find a set of samples , where ;

    if: size

     compute the point by estimation model via cross ratio;

    end if

   end for

  Until is empty

 Step 8. The second step expansion by using a regular seed-growing method

 Step 9. Obtain dense disparity map by using fitting process or synthesized method