From eb5ba9519be7399108bc0ae7843ea6fcd4679d2b Mon Sep 17 00:00:00 2001 From: Jet Hughes Date: Fri, 31 Mar 2023 21:46:04 +1300 Subject: [PATCH] vault backup: 2023-03-31 21:46:04 --- content/notes/342-assignment-01.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/content/notes/342-assignment-01.md b/content/notes/342-assignment-01.md index 917e416b6..24e3df2a5 100644 --- a/content/notes/342-assignment-01.md +++ b/content/notes/342-assignment-01.md @@ -16,7 +16,7 @@ use a range of input images # Experiment 1: Feature Matching HYPOTHESIS: -Level of detail in images will have more of an affect on the accuracy of image mosaics created using FLANN based matching that those creating using Brute-Force mathing +Level of detail in images will have more of an effect on the accuracy of image mosaics created using FLANN based matching that those creating using Brute-Force mathing EXPERIMENT DESIGN: @@ -24,14 +24,22 @@ Variables - Independent Variables: Level of detail (Number of SIFT features detected) - Dependent Variable: reprojection error of image mosaics -Collect a number of highly detailed images and a number of less detailed images. Highly detailed images with have a lot of information such as trees, text, buildings, landscapes etc. Less detailed images with be sparse, these could be bare buildings, walls, images with large blocks of color. +Collect a number of highly detailed pairs of images and the same number pairs of less detailed images. Highly detailed images will have a lot of information such as trees, text, buildings, landscapes etc. Less detailed images with be sparse, these could be bare buildings, walls, images with large blocks of color, etc. -Detailed Images will result in a large number of features, and the opposite should be true for less detailed images. This could affect the accuracy of image mosaics. +Detailed Images will result in a large number of features which are tightly grouped. This could significantly affect the accuracy of FLANN based matching To conduct the experiment I will measure the reprojection error of image mosiacs created using FLANN and Brute force matching -_control for other factors: lighting, camera settings (exposure, iso, etc), resolution,_ +1. Select a set of highly detailed image pairs, and a set of sparsly detailed image pairs +2. For each image pair, detect SIFT features and perform both FLANN and Brute force matching +3. Calculate the reprojection error of features matched using both methods for each image pair +4. For each method, plot the accuracy as a function of the number of features/level of detail +_control for other factors: lighting, camera settings (exposure, iso, etc), resolution?__ + +**hypothesis super wordy +what is your error metric +how will you decide statistically if your hypothesis is true or not**