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Comparison of the Teknomo-Fernandez algorithm vs a Monte-Carlo inspired algorithm vs the median filter for background generation.

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Computer Vision Final Project

Orchestrated by Alejandro Castaneda and Renee Wu at Portland State University for CS 410 - Computer Vision with Feng Liu.

The following project compares the Teknomo-Fernandez algorithm, a spin-off of a Monte-Carlo inspired algorithm, and the median filter to view which algorithm works best to generate background images in varying sets of datas.

Directory Dissection


data/ - contains all the datasets used in the project.

results/ - contains all the resulting images in tf* (Teknomo Fernandez), mc* (Monte-Carlo inspired) and median* with the dataset following its name.

Report.pdf - contains the final report submitted with the project discussing the results, method, and more formal information.

bggen.py - The mitochondria of this project. Contains all the code executed.

requirements.txt - Libraries used with the project.

References

Teknomo-Fernandez Algorithm

Implementation for the Monte-Carlo inspired algorithm

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Comparison of the Teknomo-Fernandez algorithm vs a Monte-Carlo inspired algorithm vs the median filter for background generation.

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