This paper presents a novel approach for detection and segmentation of generic shapes in cluttered images. We use vectorial eigenvectors to compactly represent a large set of possible appearances of primitive shapes. A posterior energy probability map of the image is calculated in the vectorial eigenspace to yield a relative similarity measure. The detection of generic shapes is realized by detecting local peaks of the probability map. We find that eigenspace energy is more suitable for representation of sparse sets such as our affine set. At each local probability maxima, a fast search approach based on a novel representation by an angle space is employed to determine the best matching between models and the underlying sub-image. We find that angular representation in multidimensional search corresponds better to Euclidean distance than conventional projection and yields improved classification of noisy shapes. Experiments are performed in various interfering distortions, and robust detection and segmentation are achieved.