Recognition of objects from images is one of the central research topics of computer vision. The use of shape for recognizing objects has been actively studied since the beginning of object recognition in 1950s. Several authors suggest that object shape is more informative than its appearance -- the object appearance properties such as texture and color vary between object instances more than the shape e.g. bottle, caps, cars, airplanes, cows, horses etc. Recent methods are concentrated on extracting shape features and learning the object models directly from images which impose such problems as object occlusion, incomplete and often fragmented object boundaries, varying camera view-points. While these approaches are designed to learn object models from fragmented and incomplete object boundaries, achieving invariance to rotation, scale and affine transformations has not been fully solved.
This thesis address the problem of learning object models that use shape properties with full rotational and scale invariance. A new approach is proposed where invariance to image transformations is obtained through invariant matching rather than typical invariant features. This philosophy is especially applicable to shape features, represented by edges detected in images which do not have a specific scale or specific orientation until assembled into an object. Our primary contributions are: a new shape-based image descriptor that encodes a spatial configuration of edge parts, a technique for matching descriptors that is rotation and scale invariant and shape clustering that can extract frequently appearing image structures from training images without a supervision.
This thesis also presents an overview of the object recognition field and our other contributions in the area of local appearance based methods, texture detection and image segmentation.