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DISTRIBUTED MULTI-DIMENSIONAL FEATURE RECOGNITION AND MULTI-LEVEL SEMANTIC CLASSIFICATION MODEL FOR ARTIFICIAL VISION

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JANUARY-DECEMBER 2016   -  Volume: 3 -  Pages: [11 p.]

DOI:

https://doi.org/10.6036/NT7791

Authors:

LISARDO PRIETO GONZALEZ - BEATRIZ PUERTA HOYAS - ANTONIO DE AMESCUA SECO

Disciplines:

[No data]

Downloads:   38

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Received Date :   27 August 2015

Accepted Date :   15 December 2016


Key words:
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.
Article type:
ARTICULO DE INVESTIGACION / RESEARCH ARTICLE
Section:
RESEARCH ARTICLES

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.

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