Thesis (Selection of subject)Thesis (Selection of subject)(version: 278)
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Pattern recognition for in-game spell systems
Thesis title in Czech: Rozpoznávání tvarů pro herní systémy kouzel
Thesis title in English: Pattern recognition for in-game spell systems
Key words: hry, rozpoznávání vzorů, neuronové sítě
English key words: games, pattern recognition, neural networks
Academic year of topic announcement: 2016/2017
Type of assignment: Bachelor's thesis
Thesis language: angličtina
Department: Department of Software Engineering (32-KSI)
Supervisor: Mgr. Miroslav Kratochvíl
Author: hidden - assigned and confirmed by the Study Dept.
Date of registration: 25.07.2017
Date of assignment: 31.07.2017
Confirmed by Study dept. on: 22.11.2017
Date and time of defence: 06.09.2018 09:00
Date of electronic submission:20.07.2018
Date of submission of printed version:20.07.2018
Date of proceeded defence: 06.09.2018
Reviewers: Mgr. Vít Šefl
For the purpose of improving systems of casting magic spells in computer games, the thesis aims to implement an algorithm that recognizes structured combinations (e.g. embeddings, agglomerations or convolutions) of magical symbols (e.g. runes, letters or simple shapes) and demonstrate its functionality by providing a comprehensive mapping of the recognized features to a spell system in a matching game environment.

Recognition will be implemented by a method chosen from the reviewed literature and modified to handle the combinations of the basic shapes. Algorithm should run in time negligible to the user, while recognizing sufficiently complex combinations of the shapes.
Bishop, Christopher M. Neural networks for pattern recognition. Oxford university press, 1995.

Seidl, Markus. Computational Analysis of Petroglyphs. Diss. Technische Universität Wien, 2016.

Petrakis, Euripides G. M., Aristeidis Diplaros, and Evangelos Milios. "Matching and retrieval of distorted and occluded shapes using dynamic programming." IEEE Transactions on Pattern Analysis and Machine Intelligence 24.11 (2002): 1501-1516.

Wu, Yi-Chao, Fei Yin, and Cheng-Lin Liu. "Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models." Pattern Recognition 65 (2017): 251-264.
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