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This paper presents MESSIE, a multi-specialist architecture for scene interpretation in a robotic application.
However, scene interpretation is still in its infancy, in comparison with human cognition.
Scene interpretation of CCD-camera images generates spline fitted segments of future trajectory.
Two remote sensing applications on 2D scene interpretation and a third one, presented in this paper, on 3D scene interpretation allow the validation of the proposed architecture as well as the main features of MESSIE.
We also explain how deductive and abductive reasoning is performed within the model to accomplish scene interpretation and tracking improvement.
We survey in this paper Tower of Knowledge (ToK) approaches, which take advantage of human language, for scene interpretation.
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Moreover, there is evidence that we favor scene interpretations that hold over a greater range of viewer positions [2] and light source positions [3].
Second, we show that our neuronal representation is further processed to extract different junction types such as L, T- and X-junctions which are perceptually important features that provide basic cues for global scene interpretations.
Though it is intuitive to suppose that encoding strictly precedes interpretation, the present results reinforce evidence for a reverse direction of influence: depending on cues for scene-interpretation, masking is enhanced when image components stick together, and relieved when the components peel apart.
Carthy made his name in the early 1960s folk scene, his interpretations of ancient melodies influencing the likes of Bob Dylan and Paul Simon.
We aren't going anywhere until we share our key scenes and interpretations.
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