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Optimized registration based on an ant colony for markerless augmented reality systems
Registro optimizado basado en colonia de hormigas para sistemas de realidad aumentada sin marcadores
DOI:
https://doi.org/10.15446/dyna.v87n212.84039Palabras clave:
augmented reality, markerless registration, meta-heuristic method, ant colony optimization (en)realidad aumentada, registro sin marcadores, método meta-heurístico, optimización de colonias de hormigas (es)
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Accurate registration in augmented reality systems is essential to guarantee the visual consistency of the augmented environment. Although error in the virtual-real alignment is almost unavoidable, different approaches have been proposed to quantify and reduce such errors. However, many of the existing solutions require a lot of a priori information, or they only focus on camera calibration to guarantee good results in the registration. This article presents a heuristic method that aims to reduce registration errors in markerless augmented reality systems. The proposed solution sees error reduction as a mono-objective optimization problem, which is addressed by means of the Ant Colony Optimization (ACO) algorithm. Experimental results reveal the validity of the proposed method, reaching an average error of 1.49 pixels for long video sequences.
Un registro preciso en sistemas de realidad aumentada es esencial para garantizar la consistencia visual del entorno aumentado. Aunque el error en la alineación virtual-real es casi inevitable, en la literatura se han propuesto varios enfoques para cuantificar y reducir dicho error. Sin embargo, muchos de los trabajos existentes requieren mucha información a priori, o sólo se centran en la calibración de la cámara para garantizar buenos resultados. En este artículo, se presenta un método meta-heurístico para reducir el error en el registro. Nuestra solución considera la reducción del error como un problema de optimización mono-objetivo, que se aborda mediante el Algoritmo de Colonias de Hormigas (ACO). Los resultados experimentales revelan la validez del método propuesto, alcanzando un error promedio de 1,49 píxeles.
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
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