Angled stereo vision

The distance between the lenses in a typical stereo camera (the intra-axial distance) is about the distance between one's eyes (known as the intra-ocular distance) and is about 6.35cm, though a longer base line (greater inter-camera distance) produces more extreme 3-dimensionality.

However we have 2 cameras which are angled in contrast to the stereo vision camera where both cameras are parallel on a line

Disparity Map

In stereo vision we have 2 camera's at a constant distance from each other. we take a picture of a scene. the image that we get from each camera is slightly different from each other. if we place both of them together we will see that they are a little blurred. some of the objects in the scene will look mis-aligned. between the left and right image. if the alignment of an object in the scene is very BIG, then the object is closer to the camera's if the object is less aligned or clear or lined up better, that means its further away from the camera.













Im using the dataset available here:

DATASET HERE >>>
H. Hirschmüller and D. Scharstein. Evaluation of cost functions for stereo matching.
In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), Minneapolis, MN, June 2007.

this is because i dont have 2 cameras to take pictures.
they also keep a list of algorithms on who has the best implementation: HERE>>



data is the similarity of the right image from the left, the code uses left to right. i made a mistake in correcting the image dataset. i thought the image left side is from the left camera while its from the other infact.

comparing the wrong images will give unwanted results. the left image and the right image should clearly be defined in order for the disparity map to be correct.
another important factor is the disparity range, i used 0-16 with a window size of 9x9. you can see the results are not so clear.

for this disparity map image i used the 9x9 window with a disparity range of 0-16. the similarity measurement which was used is the sum of square difference.

also the higher the disparity range the longer it will take to process. so im guessing each image should have a different disparity range just like different threshold in background subtraction. you can see from the image below that the disparity range gave a different result.


Its clear that a higher disparity range will give a better result as you can see from the image below i used a disparity range of 0-4 with the same window size of 9x9

CODE TO FOLLOW