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I've been asked to implement an edge-based disparity map, but I fundamentally don't understand what a disparity map is. What is the definition of a "disparity map"?
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Given a pair of stereo images, to compute the disparity map, we first match every pixel in the left image with its corresponding pixel in the right image. Then we compute the distance for each pair of matching pixels. Finally, the disparity map is obtained by representing such distance values as an intensity image.
Disparity refers to the distance between two corresponding points in the left and right image of a stereo pair.
Disparity is the horizontal displacement of a point's projections between the left and the right image. Whereas, depth refers to the z coordinate (usually z) of a point located in the real 3D world (x, y, z).
Binocular disparity refers to the difference in image location of an object seen by the left and right eyes, resulting from the eyes' horizontal separation (parallax). The brain uses binocular disparity to extract depth information from the two-dimensional retinal images in stereopsis.
Disparity map refers to the apparent pixel difference or motion between a pair of stereo images. To experience this, try closing one of your eyes and then rapidly close it while opening the other. Objects that are close to you will appear to jump a significant distance while objects further away will move very little. That motion is the disparity.
In a pair of images derived from stereo cameras, you can measure the apparent motion in pixels for every point and make an intensity image out of the measurements.
See this for an example. You can see the objects in the foreground are brighter, denoting greater motion and lesser distance.
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