Name
Abstract | ||
|---|---|---|
Recent studies in the field of human vision science suggest that the human
responses to the stimuli on a visual display are non-deterministic. People may
attend to different locations on the same visual input at the same time. Based
on this knowledge, we propose a new stochastic model of visual attention by
introducing a dynamic Bayesian network to predict the likelihood of where
humans typically focus on a video scene. The proposed model is composed of a
dynamic Bayesian network with 4 layers. Our model provides a framework that
simulates and combines the visual saliency response and the cognitive state of
a person to estimate the most probable attended regions. Sample-based inference
with Markov chain Monte-Carlo based particle filter and stream processing with
multi-core processors enable us to estimate human visual attention in near real
time. Experimental results have demonstrated that our model performs
significantly better in predicting human visual attention compared to the
previous deterministic models. |
Year | Venue | Keywords |
|---|---|---|
2010 | Clinical Orthopaedics and Related Research | stochastic model,near real time,particle filter,markov chain monte carlo,multi core processor,evolutionary computing,dynamic bayesian network,stream processing,pattern recognition |
Field | DocType | Volume |
Variable-order Bayesian network,Pattern recognition,Human visual system model,Inference,Computer science,Particle filter,Markov chain,Artificial intelligence,Stochastic modelling,Machine learning,Vision science,Dynamic Bayesian network | Journal | abs/1004.0 |
Citations | PageRank | References |
2 | 0.38 | 16 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akisato Kimura | 1 | 244 | 28.03 |
| Derek Pang | 2 | 29 | 3.95 |
| Tatsuto Takeuchi | 3 | 35 | 3.54 |
| Kouji Miyazato | 4 | 65 | 2.58 |
| Junji Yamato | 5 | 1120 | 165.72 |
| Kunio Kashino | 6 | 285 | 68.41 |