1. Experiments Irena Farberov,Andrey Terushkin

2. Stereo The word "stereo" comes from the Greek word "stereos" which means firm or solid. With stereo vision you see an object as solid in three spatial dimensions-- width, height and depth--or x, y and z. It is the added perception of the depth dimension that makes stereo vision so rich and special.

3. The person sees world around volume. Therefore quite natural desire is the desire to embody this world such what it is - having not only width and height, but also depth. Complexities arise when we will want to see the stereo image removed thus. For this purpose it is necessary, that each eye would see the image intended for it, and did not see the image for other eye. Without special training of an eye at the person look, as a rule, how it is offered to them the nature, instead of the volume image see two flat.

4. Our Goals We want to receive the stereo image from two regular pictures The program should identify three-dimensional subjects Examining the influence of different factors to received stereo picture.

5. Finding Correspondences:

6. Comparing Windows: =?f g Mostpopular For each window, match to closest window on epipolar line in other image.

7. It is closely related to the SSD: Maximize Cross correlation Minimize Sum of Squared Differences

8. Processing Stages Loading the pictures Generate disparity map Generate depth map Smooth with Gaussian 3D Preview

9. Examples and results: Simpsons

10. Examples and results: Result:

11. Examples and results: Example 2:

12. Examples and results: :

13. Example3:.

14. Results:

15. Conclusions: The algorithm is very successful on artificial pictures. Success on recognizing shape from random noise It is very sensitive to deviation in the epipolar line, issue that common in real photos. Real photos are never correct : it is impossible to set the cameras exactly in the same angle in each camera the objects are differently pushed into pixels many other problems like color correction on digital cameras. That fact make real images extremely difficult to recognize.

16. References: en.wikipedia.org/wiki/Stereopsis www.vision3d.com www.lessons4living.com/free.htm http://www.knowdotnet.com/articles/examplesandtutorial.html Ohad Ben Shahar, - Lectures on "Introduction to Computational and Biological Vision", BGU computer science department Vishvjit S.Nalwa:”A Guided Tour of Computer Vision” Christopher Brown: “Advances in Computer Vision “