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Automatic Colorization of Digital Movies using Decolorization Models and SSIM Index

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Śluzek A. , Dudziński M. , Świsłocki T.

Annals of Computer Science and Information Systems

2023

tom Vol. 35

843--853

Re-colorization of images or movies is a challenging problem due to the infinite RGB solutions for a monochrome object. In general, the process is assisted by humans, either by providing colorization hints or relevant training data for ML/AI algorithms. Our intention is to develop a mechanism for fully unguided (and with no training data used) colorization of movies. In other words, we aim to create acceptable colored counterparts of movies in domains where only monochrome visualizations physically exist (e.g. IR, UV, MRI, etc. data). Following our past approach to image colorization, the method assumes arbitrary rgb2gray models and utilizes a few probabilistic heuristics. Additionally, we maintain the temporal stability of colorization by locally using structural similarity (SSIM) between adjacent frames. The paper explains the details of the method, presents exemplary results and compares them to the state-of-the art solutions.

Implementation of the game model of the Polish Ekstraklasa team using machine learning techniques

88%

Pogodziński J.

Annals of Computer Science and Information Systems

2021

tom Vol. 26

231

The aim of the thesis is to create a model defining the style of play of a team playing in the Polish Ekstraklasa. The limitation to the highest Polish league class is dictated by the differences in the style of play depending on the league. The model is to be created on the basis of data about the team's game. To build the model, supervised and unsupervised learning techniques will be used and compared to find the relationship between the team's statistics and the determination of its playing style.