A practical example of Linear Algebra and just how strong matrices are in real life
Image compression
We compare different compression ratios by how much information is retained in the image as we use less and less memory. To reduce the memory we use the Singular Value Decomposition (SVD) algorithm on the matrices. We can retain a smaller part of the singular values obtained in the S matrix of the SVD algorithm and by doing this we lose some image quality.
Face recognition
We take a few images that we know contain faces and extract the eigenvalues of the images (that are after all only matrices of values). After this we check on other images that we give as input to obtain a certain percentage of the eigenvalues similar and after a certain threshold, we consider there are no faces in that image.
More detailes can be found in the "Tema2_MN_2019.pdf" file.
-
Notifications
You must be signed in to change notification settings - Fork 0
A practical example of Linear Algebra and just how strong matrices are in mathematics. We compare different compression ratios by how much information is retained in the image as we use less and less memory. To reduce the memory we use the Singular Value Decomposition (SVD) algorithm on the matrices. We can retain a smaller part of the singular …
Robert-ML/Image-compression-and-Face-recognition
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
A practical example of Linear Algebra and just how strong matrices are in mathematics. We compare different compression ratios by how much information is retained in the image as we use less and less memory. To reduce the memory we use the Singular Value Decomposition (SVD) algorithm on the matrices. We can retain a smaller part of the singular …
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published