<p>If you have rectified images, finding disparity is a matter of calculating costs between pixels in left and right images on the same horizontal line.</p>

<p>You can take a few selected points in the images (for example the ones that have high gradient or feature points coming from SIFT), set those as roots/seeds of your regions and calculate cost for a range of disparities using SAD/SSD or whatever cost function you prefer.</p>

<p>Then take the best disparity for a root and assign it to a neighbor. If the cost for that is lower than a predefined threshold, add it to the region otherwise go to next neighbor. When you cannot add any more points the region growing is finished.</p>

<p>This is a detailed example of the process: <a href="http://arxiv.org/pdf/0812.1340.pdf" rel="nofollow">http://arxiv.org/pdf/0812.1340.pdf</a></p>
Selecting initial seeds of rectified images in matlab
<p>Dear friends I am currently working on a disparity algorithm that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. But before that I am implementing the standard region growing algorithm in matlab to understand how it works.
The first step of the baseline growing algorithm says that:</p>

<p><strong>Require</strong>: Rectified images Il, Ir, initial correspondence
seeds S, image similarity threshold. Compute similarity simil(s) for every seed s belonging to  S.</p>

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  <p>Now i cannot understand this step. First of all how do i calculate initial seed points from two rectified images. Should i use SIFT algorithm in matlab or is there any better way to do it.???Can anybody also  give me  some idea about how does a region growing based disparity calculating algorithm works and whether it is better than SAD or SSD.</p>
</blockquote>