Stereo matching system and method of operating thereof

1. A method of operating a stereo matching system, the method comprising:

receiving a first image from a first camera of a stereo camera, and a second image from a second camera of a stereo camera, wherein the first and second images are of an object taken with different viewing directions;

downscaling the first image and the second image by a smoothing filter in a ratio of a downscale factor DF to generate a first downscaled image and a second downscaled image, respectively;

generating an edge map by detecting an edge pixel from the first downscaled image;

generating an initial cost volume matrix from the first downscaled image and the second downscaled image according to the edge map;

generating an initial disparity estimate from the initial cost volume matrix;

refining the initial disparity estimate using the initial disparity estimate to generate a final disparity set; and

generating a depth map from the first image and the second image using the final disparity set,

wherein the first image includes a plurality of W column pixels and a plurality of H row pixels,

wherein the first downscaled image includes a plurality of W/DF column pixels, and

wherein the first downscaled image is formed of every DF th column pixels of the pluralty of W column pixels so that a resolution of the first downscaled image is reduced by 1/DF times a resolution of the first image in a row direction.

2. The method of claim 1 ,

wherein the downscale factor DF is two (2).

3. The method of claim 1 , further comprising:

wherein the generating of the initial disparity estimate of the edge pixel includes:

generating an initial disparity set including a plurality of initial disparity candidates, wherein a maximum initial disparity candidate of the initial disparity set is equal to d max /DF, wherein d max is a maximum disparity;

generating an initial cost volume matrix of H×(W/DF)×(d max /DF) storing a cost value of each pixel of the first downscaled image; and

searching the initial disparity estimate from the initial cost volume matrix.

4. The method of claim 3 ,

wherein the searching of the initial disparity estimate from the initial cost volume matrix includes:

performing an dynamic programming to find the initial disparity estimate,

wherein the dynamic programming is performed in a unit of two scanlines adjacent to each other.

5. The method of claim 4 ,

wherein the dynamic programming includes a vertical smoothness between the two scanlines.

6. The method of claim 4 ,

wherein the dynamic programming is performed in a first direction on an even-numbered row pixel, and

wherein the dynamic programming is performed in a second direction opposite to the first direction on an odd-numbered row pixel.

7. The method of claim 1 ,

wherein the generating of the initial cost volume matrix further includes:

generating an aggregated cost volume matrix from the initial cost volume matrix.

8. The method of claim 1 ,

wherein the generating of the initial cost volume matrix includes:

if a pixel is determined as an edge pixel, computing a cost for the edge pixel over an initial disparity set and storing the cost to the initial cost volume matrix; and

if a pixel is determined as a non-edge pixel, assigning a predetermined maximum cost to the initial cost volume matrix without computing a cost.

9. The method of claim 1 ,

wherein the refining of the initial disparity estimate using the first image and the second image includes:

generating a final disparity set using the initial disparity estimate;

computing a final matching cost volume using the final disparity set from the first image and the second image; and

determining a final disparity candidate of the final disparity set from a minimum final cost.

10. The method of claim 9 ,

wherein the final disparity set includes a plurality of final disparity candidates, and

wherein a number of the plurality of final disparity candidates is 2*DF+1.

11. The method of claim 1 ,

wherein the receiving of the first image and the second image further comprising:

rectifying a left image and a right image; and

compensating an intensity of the left image and an intensity of the right image to generate the first image and the second image.

12. The method of claim 11 ,

wherein the generating of the edge map includes:

if the edge pixel is determined, assigning a value indicating to a non-edge pixel to pixels adjacent to the edge pixel within a predetermined range step.

13. A method of generating a three-dimensional image, the method comprising:

receiving a first image from a first camera of a stereo camera, and a second image from second camera of a stereo camera, wherein the first and second images are of an object taken with different viewing directions, and wherein each of the first image and the second image includes a plurality of W column pixels and a plurality of H ow pixels;

downscaling the first image and the second image by a smoothing filter in a ratio of a downscale factor DF to generate a first downscaled image arid a second downscaled image, respectively, so that each of the first image and the second image includes a plurality of W/DF column pixels and the plurality H row pixels; and

generating an initial cost volume matrix of the first downscaled image and the second downscaled image with an initial disparity set by calculating a cost for an edge pixel of the first downscaled image,

wherein the initial cost volume matrix has a dimension of H×(W/DF)×d max , and

wherein d max is a maximum initial disparity of the initial disparity set, and

performing a dynamic program on the initial cost volume matrix to search an initial disparity estimate.

14. The method of claim 13 , further comprising:

refining the initial disparity estimate to generate a final disparity set;

calculating a final matching cost volume matrix from the first image and the second image using the final disparity set; and

searching a minimum final cost from the final matching cost volume matrix to generating a depth map.

15. The method of claim 13 ,

wherein the generating of the initial cost volume matrix of the first downscaled image and the second downscaled image further includes:

assigning a predetermined maximum cost to a non-edge pixel without computing a cost for the non-edge pixel.

16. The method of claim 15 , further comprising:

detecting the edge pixel and the non-edge pixel from the downscaled first image.

17. A stereo matching system comprising:

a smoothing filter receiving a first image and a second image and generating a first downscaled image and a second downscaled image in a ratio of a downscale factor DF;

an edge detector receiving the first downscaled image and generating an edge map;

a cost volume matrix generator receiving the first downscaled image and the second downscaled image and generating an initial cost volume matrix, wherein the cost volume matrix generator computes a cost for an edge pixel and assigns a predetermined maximum cost value tor a non-edge pixel;

a disparity estimator receiving the initial cost volume matrix and generating an initial disparity estimate; and

a disparity refiner receiving the initial disparity estimate and generating a plurality of final disparity candidates, wherein a number of the plurality of final disparity candidates is 2*DF+1.

18. The stereo matching system of claim 17 , further comprising:

a rectifier receiving a left image and a right image; and

an illumination correction circuit receiving the left image and the right image and generating the first image and the second image to compensate differences in intensities between the left image and the right image.