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content/notes/342-assignment-01.md
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@ -16,7 +16,7 @@ use a range of input images
# Experiment 1: Feature Matching # Experiment 1: Feature Matching
HYPOTHESIS: 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: EXPERIMENT DESIGN:
@ -24,14 +24,22 @@ Variables
- Independent Variables: Level of detail (Number of SIFT features detected) - Independent Variables: Level of detail (Number of SIFT features detected)
- Dependent Variable: reprojection error of image mosaics - 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 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**