Optimized registration based on an ant colony for markerless augmented reality systems
| Autor: | John William Branch Bedoya, Gloria Elena Jaramillo-Rojas |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Welfare economics Ant colony optimization algorithms General Engineering 020207 software engineering Video sequence 02 engineering and technology Ant colony 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Augmented reality systems Error reduction |
| Zdroj: | DYNA. 87:259-266 |
| ISSN: | 2346-2183 0012-7353 |
| Popis: | espanolUn registro preciso en sistemas de realidad aumentada es esencial para garantizar la consistencia visual del entorno aumentado. Aunque el error en la alineacion 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 informacion a priori, o solo se centran en la calibracion de la camara para garantizar buenos resultados. En este articulo, se presenta un metodo meta-heuristico para reducir el error en el registro. Nuestra solucion considera la reduccion del error como un problema de optimizacion mono-objetivo, que se aborda mediante el Algoritmo de Colonias de Hormigas (ACO). Los resultados experimentales revelan la validez del metodo propuesto, alcanzando un error promedio de 1,49 pixeles EnglishAccurate 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 |
| Databáze: | OpenAIRE |
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