A saliency-based bottom-up visual attention model for dynamicscenes analysis
This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work addsmotion saliency calculations to a neural network model withrealistic temporal dynamics [(e.g., building motion salienceon top of De Brecht and Saiki Neural Networks 19:1467–1474, (2006)]. The resulting...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | Colombia |
| Recursos: | Universidad Autónoma de Occidente |
| Repositorio: | RED: Repositorio Educativo Digital UAO |
| Idioma: | inglés |
| OAI Identifier: | oai:red.uao.edu.co:10614/11584 |
| Acesso em linha: | http://red.uao.edu.co//handle/10614/11584 |
| Access Level: | acceso abierto |
| Palavra-chave: | Redes neuronales (Computadores) Neural networks (Computer science) Visual attention Saliency map Motion saliency Neural network Synaptic depression Neural latency Asymmetry phenomenon Lyapunov stabilit |
| Resumo: | This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work addsmotion saliency calculations to a neural network model withrealistic temporal dynamics [(e.g., building motion salienceon top of De Brecht and Saiki Neural Networks 19:1467–1474, (2006)]. The resulting network elicits strong transientresponses to moving objects and reaches stability withina biologically plausible time interval. The responses arestatistically different comparing between earlier and latermotion neural activity; and between moving and non-movingobjects. We demonstrate the network on a number of syn-thetic and real dynamical movie examples. We show thatthe model captures the motion saliency asymmetry phenom-enon. In addition, the motion salience computation enablessudden-onset moving objects that are less salient in the staticscene to rise above others. Finally, we include strong consid-eration for the neural latencies, the Lyapunov stability, andthe neural properties being reproduced by the model |
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