Project 6: Mosaics

Method

I created several mosaics with the method in the handout. I did a simple alpha blend with biquadratic weights that are 1 at the center of the image and 0 around the whole edge.

As for the feature detection, consult the following images:
Original Features 1 Suppressed/Matched/Agreed Features 1 Suppressed/Matched/Agreed Features 2 Original Features 2

On the ends are the images with all original harris corner feature points. In the middle two images, I show in red the 500 best samples after adaptive non-maximal suppression. I then circle in green and blue those samples which have a match in the other image. I use green circles for those that pass RANSAC and blue for those that fail.

In order for two samples to match, they must both be the best partner of the other with less than 80% of the sq-distance of the other's second best partner (i.e. a Russian-granny coefficient of 0.8). This is relatively permissive, but RANSAC easily picks up the slack.

Results -- Indoor Scene

Results -- Chatham University Scene

Results -- Apartment Scene

Results -- SCS Quad Scene

Results -- Hall Scene (Automatic Only)

Results -- Cathedral Scene (Automatic Only)

Results -- Path Scene (Automatic Only)

Results -- Stairs Scene (Automatic Only)

Results -- Bikes Scene (Automatic Only)

Project 6-Grad: Tour into a Picture

Method

I implemented the method from the paper. I was able to get novel views for the provided painting. With every other scene, MATLAB would just save straight white images, and I was too tired and frsutrated to work around MATLAB being bad. In place of novel views, I have provided images which can be printed, cut, and folded to create a model of the scene!

Results -- Painting

Original Novel View 1 Novel View 2 Novel View 3

Results -- Other Scene Fold-Ups

Original Fold-Up