Description
This particular assignment requires the use of a digital camera. The report shall contain images, psedo-code and output of your algorithms. Homework is out of 10pts. You may pick one of the following 10 point problems:
- A Manual 3D Scanner
- Pick a salient object in your vicinity. Using a digital camera, take a stereo image of a single object. That is, take two pictures from slightly different camera positions. Ideally, the object takes up a large portion of the frame.
- Manually identify and label visually a few sparse corresponding points in both pictures. You will need to identify the image coordinates of these points as well.
- Recover the camera parameters using the essential or fundamental matrix using 8-point algorithm (You can assume one of your camera is fixed at origin with rotation matrix to be identity). Include pseudocode.
- Recover the sparse 3D cloud points of the object from your marked points. Show results for both:
- linear optimization
- non-linear optimization (hint: You may use Matlab’s fminunc or lsqnonline )
- Primitive Panorama Stitching
- Pick a wide-angle scene you would like to capture. Using a digital camera, take two images from the same position but with different angles. Ideally, there will be some overlap between the two images.
- Find the SIFT-key points and descriptors for both the images (hint: You may use open source code and package).
- Match the correspondence points. Include pseudocode. Show visually these matched pairs of points.
- Run RANSAC to estimate homography and stitch the two images together and include the final image in your report.
