Image-Stitching-using-SIFT-features-for-Panorama-Creation-in-Matlab

Image Stitching using SIFT features for Panorama Creation in Matlab

Image Stitching Based on Affine and Homography

Part A:

In part A, two images parliament-left and parliament-right are being stitched.

1. Step1: Preprocessing
   1.1. Conversion to Single
   1.2. Gray Conversion
   
2. Step2: Detect keypoints and extract descriptors
   2.1. Frames and descriptors will be extracted Using vl_sift function from vl_feat library.
   2.2. They are plotted using vl_plotframe
   2.3. Descriptors are plotted using vl_plotsiftdescriptor.m
   
3. Step3: Match Features
   3.1. Extracted Descriptors are matched using vl_ubcmatch.m
   
4. Step4: Prune features
   4.1. Select the closest matches based on the matrix of pairwise descriptor distances
   
5. Step5: Robust Transformation Estimation
   5.1. Affine Transformation and inliers are calculated using ransacfitaffine.m
   5.2. Transformation Plotting using fun_plotmatches.m
   5.3. Outliers are also calculated
   
6. Step6: Compute Optimal Transformation
   
7. Step7: Create panorama
   7.1. Panorama is created using above data and passed to vl_imwbackward.m
    
   

   Part B:
   

   In part B, two images Glendon-Hall-left, Glendon-Hall-middle and Glendon-Hall-right are being stitched.
   

8. Step1: Preprocessing
   1.1. Conversion to Single
   1.2. Gray Conversion
   
9. Step2: Detect keypoints and extract descriptors
   2.1. Frames and descriptors will be extracted Using vl_sift function from vl_feat library.
   2.2. They are plotted using vl_plotframe
   2.3. Descriptors are plotted using vl_plotsiftdescriptor.m
   
10. Step3: Match Features
    3.1. Extracted Descriptors are matched using vl_ubcmatch.m
    
11. Step4: Prune features
    4.1. Select the closest matches based on the matrix of pairwise descriptor distances
    
12. Step5: Robust Transformation Estimation
    5.1. vl_colsubset is used for homography estimation.
    5.2. Kronecker tensor and VL_HAT Hat operator are also used.
    5.3. Svd is calculated.
    5.4. Hence score homography is calculated.
    
13. Step6: Compute Optimal Transformation
    6.1. Max Score is selected from homography score.
    6.2. Homography based points are then plotted.
    
14. Step7: Create panorama
    7.1. Panorama is created using above data and passed to vl_imwbackward.m