CS 194-26 Project 5A [acc id: aez]

Overview

• CS 194-26 Project 5A [acc id: aez]
  • Overview
  • Part 1: Shoot The Pictures / Foreword
  • Part 2: Recover Homographies
  • Part 3: Warp The Image
  • Part 4: Image Rectification
  • Part 5: Mosaic
    • Input
    • Ouput
    • Coolest things learnt

Part 1: Shoot The Pictures / Foreword

• Images were shot mostly from the room where I am currently quarantined at.

Part 2: Recover Homographies

• Following this image from lecture, further derivation was done to obtain a nice equation form (derivation attached in code file).
• np.linalg.lstsq was used to solve equation to recover coefficients of H.

Part 3: Warp The Image

• The warpImage function was implemented using inverse warping
• 2 homographies was created – H_warp_to_original and H_original_to_warp
  • H_original_to_warp was used to find the bounds of the warped images, but passing in the coordinates of the corners of the original image
  • H_warp_to_original was used for inverse warping

Part 4: Image Rectification

• Planar surfaces were taken and warped such that they are frontal parallel.

Description	Original	Rectified
Biscuit box
Door

Part 5: Mosaic

Input

Image 1	Image 2	Image 3

Ouput

• The images look more or less smooth except for the parts where there are a lot of fine details – which is where the images are overlaid. Perhaps it would look better with other reference points.
• The one area to be optimized is how the images are overlayed – curently, np.max is used to merge the two photos together

Coolest things learnt

• Probably that the result is highly influenced by the area of correspondence – if the correspondence area picked is highly detailed, the mosaic would likely not look great. I am curious to see if there are any techniques available to mitigate this, especially in manual stitching.