Coherent attributes in the interpretation and perception of the visual world

In producing image analysis, video surveillance, remote sensing, medical diagnosis software tools, the usual problem is the actual parameterizing of the processing algorithms. In most cases the setting is done by applying the experience of retrieved training data from previous experiments.
We address the fundamental problem of automatic extraction of visual information from raw sensory data. The main theoretical contribution consists of coherent models for sensory understanding the visual world. In particular, we will investigate how human vision works and adapts itself via perceptual learning to solve special tasks like the interpretation of medical images or analysis of aerial and satellite images. Mathematical and computational models of these techniques will then be derived and applied to image processing algorithms capable to solve similar tasks.
A retrieval system will also provide latent relations between objects, events, scenes and scenarios and will be able to generate hypotheses of scene objects and events based on the prior knowledge and experience. The interaction of retrieval subsystem with the fusion and inference mechanisms will provide a more reliable scenario description in case of incomplete data.
Models of human visual attentional selection, scene understanding and perceptual learning will be investigated and relevant knowledge will be applied in the computer vision systems proposed. The following specific questions will be addressed: how learning affects fixation patterns and attentional selection processes in complex visual images and cluttered natural scenes; 2. how learning effects integration and selection of visual features of the target and suppression of distractor features.
This is a collaborative project about a higher level interpretation of the visual world: cooperation among image processing groups and behavioral sciences is an extraordinary chance and a great value for the above goals.