Data and model-driven selection using closely-spaced parallel-line groups

Selecting regions in an image likely to come from a single object is important for reducing the amount of searching involved in object recognition. Such selections can be purely based on image data (data-driven), or based on the knowledge of the model object (model-driven). In this paper, we present methods for data- and model-driven selection by grouping closely-spaced parallel lines in images. Data-driven selection is achieved by selecting salient line groups that emphasize the likelihood of the groups coming from single objects. Model-driven selection is achieved by selectively generating image line groups that are likely to be the projections of the model groups, taking into account the effect of occlusions, illumination changes and imaging errors. We also present results that indicate a vast improvement in the search performance of a recognition system that is integrated with parallel fine group-based selection