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In the first two problems, you will apply geometric modification and spatial warping techniques to do some interesting image processing tricks. During this process, you may need to solve some linear equations to get the matrix parameters.
Problem 1: Geometric Image Modification (25%)
Design and implement a spatial warping technique that transforms an input square image into an output image of a star-shaped image, out of the square image. An example is given in Figure 1.
The warped image should satisfy the following requirements:
• Pixels that lie on boundaries of the square should still lie on the boundaries of the star.
• Pixels on the two diagonals of the square should remain where they are.
• The thickness (height) of each black arc region is 64 pixels.
Apply the same developed spatial warping algorithm to Forky and Twenty-two images in Figure 2.
(1) Describe your approach as clearly as possible and show the resulting images.
(2) Apply the reverse spatial warping to each warped image to recover its original image.
(3) Compare the recovered square image with the original square image. Is there any difference between two images? If any, explain sources of distortion in detail.
Problem 2: Homographic Transformation and Image Stitching (25%)
One can use homographic transformation and image stitching techniques to create panorama that consisting of multiple images. One example (Taken from MATLAB examples ) is shown in Figure 3.
The left images were taken with an uncalibrated smart phone camera by sweeping the camera from left to right along the horizon to capture all parts of the building. The right panorama is the desired output by stitching transformed images.
This example involves five images. However, the basic principle is to process in terms of consecutive pair of images. It could be achieved by following these steps:
• Select control points from both images. You can use SIFT/SURF to detect control points. You are allowed to use OpenCV or other open source code for selecting control points, online source refer to .
• Apply homographic transformation to find a homograph mapping (described below).
• Warp one image onto the other using the estimated transformation.
• Create a new image big enough to hold the panorama and composite the warped image into it.
You can composite by simply averaging the pixel values where the two images overlap.
The homographic transformation procedure is stated below. Images of points in a plane, from two different camera viewpoints, under perspective projection (pin hole camera models) are related by a homography: