Visual recognition of three-dimensional objects is a fundamental task in content-based retrieval applications that follow a query-by-example approach. It helps to provide comfortable and efficient ways to access databases via visual descriptions of objects. As the role of product and object databases steadily increases, the development of effective and efficient recognition systems gains in importance. One of the problems to be solved in visual recognition is the image segmentation, whose goal is to find an image partition composed of regions that have a correspondence to real objects. As a general vision problem, the segmentation task is ill-posed, and can only be solved under consideration of additional information that is not contained in the images. This work presents a segmentation framework based on a general model of visual processing. The task is split into three stages, each dealing with knowledge at different levels of abstraction. The image-based segmentation stage exclusively considers low-level image information to detect homogeneous regions. The surface-based stage incorporates knowledge about the scene composition in order to find segments that correspond to expected surfaces. The object-based stage identifies regions as parts of the objects known to the application. This is achieved through the interaction with different recognition processes that provide the necessary additional information about the objects. A multi-objective optimization algorithm is employed to evaluate the first two segmentation stages. This evaluation concept allows to quantitatively compare optimal parameterizations for each algorithm, where the optimality criterion is given through an aggregate fitness measure that considers several aspects of the segmentation result simultaneously. The object-based segmentation stage can be indirectly evaluated using the recognition rates of the complete retrieval system. The proposed framework is tested with object sets containing up to 200 objects. Modern image processing techniques have been combined and enhanced in the algorithmic specification of the framework. These include feature-space clustering, color edgeness detection based on color contrast techniques, watershed transform for color images, split and merge methods based on adjacency graph image representations, a color zooming approach relying on the whitening transformation, combination of several information sources using Bayesian Belief Networks, and the detection of relevant image locations by means of a scale-space analysis. The optimization process used in the evaluation of the algorithms relies on an evolutionary approach that finds a front of optimal parameterizations for a given reference set. It allows to objectively verify the adequateness of the proposed methods over several state-of-the-art algorithms. Additionally, the three-staged segmentation framework permits to concentrate the optimization of the segmentation into different aspects of the algorithms, which increases the robustness and improves the results
