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Spatial–temporal modeling of interactive image interpretation

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image of Spatial Vision
For more content, see Multisensory Research and Seeing and Perceiving.

We consider the problem of spatial–temporal modeling of interactive image interpretation. The interactive process is composed of a sequential prediction step and a change detection step. Combining the two steps leads to a semi-automatic predictor that can be applied to a time-series, yields good predictions, and requests new human input when a change point is detected. The model can effectively capture changes of image features and gradually adapts to them. We propose an online framework that naturally addresses these problems in a unified manner. Our empirical study with a synthetic data set and a road tracking dataset demonstrate the efficiency of the proposed approach.

Affiliations: 1: National ICT Australia, Canberra, Australia; 2: Toyota Technological Institute, Chicago, USA; 3: University of Alberta, Edmonton, AB T6G 2E9 Canada. Email:


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