This thesis presents the Progressive Probabilistic Hough Transform (PPHT). Unlike the Probabilistic HT [46] where the Standard HT is performed on a pre-selected fraction of input points, the PPHT minimises the amount of computation needed to detect lines by exploiting the difference in the fraction of votes needed to reliably detect lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the probabilistic HT; it is a function of the inherent complexity of data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved. The most salient features are likely to be detected first. While retaining its robustness, experiments show PPHT has, in many circumstances, advantages over the Standard HT.