Data and Model-Driven Selection Using Color Regions.
Abstract
A key problem in model-based object recognition is selection, namely, the problem of determining which regions in the image are likely to come from a single object. In this paper we present an approach that uses color as a cue to perform selection either based solely on image-data (data-driven), or based on the knowledge of the color description of the model (model-driven). Specifically, the paper argues for the specification of color in terms of color categories as being appropriate for the task of selection. These color categories are used to develop a fast region segmentation algorithm that extracts perceptual color regions in images. The color regions extracted form the basis for performing data and model-driven selection. Data-driven selection is achieved by selecting salient color regions as judged by a color-saliency measure that emphasizes attributes that are also important in human color perception. The approach to model-driven selection, on the other hand, exploits the color region information in the model to locate instances of the model in a given image. The approach presented tolerates some of the problems of occlusion, pose and illumination changes that make a model instance in an image appear different from its original description. Finally, the utility of color-based data and model-driven selection is discussed in the context of reducing the search involved in recognition.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1992
- Accession Number
- ADA260101
Entities
People
- Tanveer F. Syeda-mahmood
Organizations
- Massachusetts Institute of Technology