| Landmark recognition, identified as one of the most important research areas in robot navigation, has heretofore mainly focused on simple landmarks, limiting the use of robots in complex environments. In this dissertation, the problem of generalized landmark recognition in complex environments is addressed.; A complete framework is presented for landmark recognition and scene understanding with a combination of data-driven and model-driven approaches. In the data-driven approach, natural scene analysis is performed using a proposed texture model called intensity interactive maps (IIM). Natural scene synthesis is then studied via multi-eigenspace decomposition that is useful in differentiating background from objects. The model-driven approach achieves recognition by removing the background while processing only potential objects. Two approaches are presented for object detection and recognition: a multilevel Markov Random Field (MRF) based model and an active contour model that integrates color, texture and shape priors. To address dynamic environment changes and illumination variances, a feedback strategy is introduced and modeled into both the multilevel MRF model and the active contour model to achieve illumination adaptation.; Various experiments illustrate the performance of the proposed framework. The segmentation results in natural scene analysis are a major step forward, unlike previous results, they include semi-meaningful segments consistent with human perception. The results of natural scene synthesis are also perceptually acceptable, which has not been achieved by other texture synthesis based technology. The multi-level MRF model demonstrates robust performance for object detection and recognition under changing environments. The active contour model with prior knowledge can reliably detect and segment an object in a complex background, a cluttered environment, and a scene with partial occlusion.; Although the framework was initially proposed to address complex landmark recognition in natural environments, it has proven to be applicable for simple landmark recognition under constrained environments with emphasis on real-time performance. A specific application, the real-time tracking of objects in the RoboCup 2002 competition, was implemented and proved to be very successful. In addition, the framework also demonstrates potential in medical and biomedical image analysis in locating cells of interest. |
