Fitting parametric curve models to images using local self-adapting separation criteria

The task of fitting parametric curve models to boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as image segmentation, pose estimation, 3-D reconstruction, and object tracking. In this thesis, we propose the Contracting Curve Density (CCD) algorithm and the CCD tracker as solutions to this problem. The CCD algorithm solves the curve-fitting problem for a single image whereas the CCD tracker solves it for a sequence of images. The CCD algorithm extends the state-of-the-art in two important ways. First, it applies a novel likelihood function for the assessment of a fit between the curve model and the image data. This likelihood function can cope with highly inhomogeneous image regions because it is formulated in terms of local image statistics that are learned on the fly from the vicinity of the expected curve. Second, the CCD algorithm employs blurred curve models as efficient means for iteratively optimizing the posterior density over possible model parameters. Blurred curve models enable the algorithm to trade-off two conflicting objectives, namely a large area of convergence and a high accuracy. The CCD tracker is a fast variant of the CCD algorithm. It achieves a low runtime, even for high-resolution images, by focusing on a small set of carefully selected pixels. In each iteration step, the tracker takes only such pixels into account that are likely to further reduce the uncertainty of the curve. Moreover, the CCD tracker exploits statistical dependencies between successive images, which also improves its robustness. We show how this can be achieved without substantially increasing the runtime. In extensive experimental investigations, we demonstrate that the CCD approach outperforms other state-of-the-art methods in terms of accuracy, robustness, and runtime. The CCD algorithm and the CCD tracker achieve sub-pixel accuracy and robustness even in the presence of strong texture, shading, clutter, partial occlusion, poor contrast, and substantial changes of illumination. We present results for different curve-fitting problems such as image segmentation, 3-D pose estimation, and object tracking. i