MICROSERVICES ARCHITECTURE FOR MULTI-DIMENSIONAL FEATURE RECOGNITION USING A KNOWLEDGE REPRESENTATION MODEL, BOTH BASED ON HUMAN PERCEPTION
Keywords:
Visión artificial, percepción humana, aprendizaje automático, sistemas distribuidos, agentes inteligentes, computación en la nube, reconocimiento de patrones, artificial vision, human perception, automatic learning, distributed system, intelligent agent, cloud computing, pattern recognition.Abstract
Pattern recognition, semantic evaluation and classification in artificial vision are complex problems that are being tackled from a wide range of specific approaches. Most of these perspectives are based in the analysis of the information from a specific dimensional perspective (e.g. bi-dimensional images or video) considering a narrow set of indicators, and in the application of particular algorithmic techniques, with less or more success. This work presents a model intended to combine existing and future algorithms in order to evaluate visual information from a multi-dimensional perspective, inferring advanced properties and features by the distributed analysis of multiple source imagery, enabling the identification of environment elements in a similar way human perception works. After implementing a simplified version of the proposed model and executing it under a MPI cluster, low level features of test images are extracted and aggregated, and successful preliminary results are presented.Downloads
Published
2017-05-01
Issue
Section
ARTICULOS