Data and Model-Driven Selection Using Parallel-Line Groups
A key problem in model-based object recognition is selection, namely, the problem of isolating regions in an image that are likely to come from a single object. This isolation can be either based solely on image data (data-driven) or can incorporate the knowledge of the model object (model-driven). In this paper we present an approach that exploits the property of closely-spaced parallelism between lines on objects to achieve data and model-driven selection. Specifically, we present a method of identifying groups of closely-spaced parallel lines in images that generates a linear number of small-sized and reliable groups thus meeting several of the desirable requirements of a grouping scheme for recognition. The line groups generated form the basis for data and model-driven selection. Data-driven selection is achieved by selecting salient line groups as judged by a saliency measure that emphasizes the likelihood of the groups coming from single objects. The approach to model-driven selection, on the other hand, uses the description of closely-spaced parallel line groups on the model object to selectively generate line groups in the image that are likely to eb the projections of the model groups under a set of allowable transformations and taking into account the effect of occlusions, illumination changes, and imaging errors. We then discuss the utility of line groups-based selection in the context of reducing the search involved in recognition, both as an independent selection mechanism, and when used in combination with other cues such as color. Finally, we present results that indicate a vast improvement in the performance of a recognition system that is integrated with parallel line groups-based selection.