MuSSE: A Tool to Extract Meta-Data from Game Sprite Sheets Using Blob Detection Algorithm

Barbosa, Marcelo de Barros; Barbosa, Cecília de Barros; Barbosa, André Freitas

doi:10.1109/sbgames.2015.22

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…This paper is an extension of the previous work published on a conference paper in 2015 [4]. The current version, however, come up with considerable improvements.…”

Section: Introductionmentioning

confidence: 78%

Proposing a practical approach to extract and read XML meta-data from sprite sheets using Blob detection algorithm

Barbosa¹,

Barbosa²,

Barbosa³

2016

JIS

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This paper introduces a tool to semi-automatically generate meta-data from game sprite sheets. MuSSE is a tool developed to extract XML data from sprite sheet images with non-uniform – multi-sized – sprites. MuSSE (Multi-sized Sprite Sheet meta-data Exporter) is based on a Blob detection algorithm that incorporates a connected-component labeling system. Hence, blobs of arbitrary size can be extracted by adjusting component connectivity parameters. This image detection algorithm defines boundary blobs for each individual sprite in a sprite sheet. Every specific blob defines a sprite characteristic within the sheet: position, name and size, which allows for subsequent data specification for each blob/image. Those blobs are carefully optimized through an imbued threshold selection. This work also presents a parser to organize these meta-data into a readable solution for game engines. The parser is built to read XML metadata generated through MuSSE and allow developers to set up game objects that can be used by an engine. Several examples on real images illustrate the performance of the proposed algorithm and working tool.

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“…This paper is an extension of the previous work published on a conference paper in 2015 [4]. The current version, however, come up with considerable improvements.…”

Section: Introductionmentioning

confidence: 78%

Proposing a practical approach to extract and read XML meta-data from sprite sheets using Blob detection algorithm

Barbosa¹,

Barbosa²,

Barbosa³

2016

JIS

Self Cite

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“…The parameters extraction Figure 2.14: Data segmentation and annotation was done using the Praat script "ProsodyTime", cf. Barbosa (2015). The script generates a .txt file containing speaker identity, vowel names, vowel durations in seconds, mean formant frequencies in Hertz, and other parameters.…”

Section: Experiments I: Vowel Formant Analysismentioning

confidence: 99%

Multiparametric analysis of acoustic-phonetic measures in genetically and non-genetically related speakers

Cesar Cavalcanti de Oliveira

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I had -special thanks to Prof. Dr. Livia Oushiro-for inspiring lectures.I thank the Department of Linguistics at Stockholm University (SU) for providing me all the support I needed during the long period I have stayed in Sweden conducting the research, from staff to dear colleagues that I had the chance to learn from and share pleasant moments.I thank Dr. Miguel Oliveira Jr. (Federal University of Alagoas -UFAL) for being a constant source of inspiration and encouragement. I have learnt some much from him. Also, thanks to the "Group of Studies in Phonetics and Phonology (fonUFAL)" for all the technical support and assistance I received during the data acquisition process.Thanks to Dr. Cristina Felipeto (UFAL) for introducing me to the realm of linguistic studies while I was still an undergrad student, and Dr. Eduardo Calil (UFAL) for guiding me in my first years of research as a student, as well as Dr. Luzia Payão (UNCISAL) for fostering my interest in phonetics/phonology studies at the speech-language pathology and audiology faculty. Thanks to Dr. Ana Carolina Constantini for supervising me during my research qualification at UNICAMP. Thanks to Dr. Justin Lo (University of York) for providing one of the scripts used to carry out the statistical analyses and kindly responding to my queries. Also, thanks to Prof. Dr. Marcin Wlodarczak (Stockholm University) for his valuable assistance concerning data visualization.I want to express my gratitude to Dr. Sandra Madureira (PUC-SP), Dr. Livia Oushiro (UNI-CAMP), Prof. Dr. Luciana Lucente (UFMG), Dr. Pablo Arantes (UFSCar), Dr. Renata Passetti (PUC-SP) for reading the thesis dissertation and for presenting relevant comments/suggestions.

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Drone Detection by Neural Network Using GLCM and SURF Features

Ahmed¹,

Rahman²,

Roy³

et al. 2021

JIST

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MuSSE: A Tool to Extract Meta-Data from Game Sprite Sheets Using Blob Detection Algorithm

Cited by 3 publications

References 13 publications

Proposing a practical approach to extract and read XML meta-data from sprite sheets using Blob detection algorithm

Proposing a practical approach to extract and read XML meta-data from sprite sheets using Blob detection algorithm

Multiparametric analysis of acoustic-phonetic measures in genetically and non-genetically related speakers

Drone Detection by Neural Network Using GLCM and SURF Features

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