Model Learning in Iconic Vision
Herman Martins Gomes · ERA · 2002
Generally, object recognition research falls into three main categories: (a) geometric, symbolic or structure based recognition, which is usually associated with CAD-based vision and 3-D object recognition; (b) property, vector or feature based recognition, involving techniques that vary from specific feature vectors, multiple filtering to global descriptors for shape, texture and colour; and (c) iconic or image based recognition, which either complies with the traditional sensor architecture of an uniform array of sampling units, or uses alternative representations. An example is the log-polar image, which is inspired by the human visual system and besides requiring less pixels, has some useful mathematical properties. The context of this thesis is a combination of the above categories in the sense that it investigates the area of iconic based recognition using image features and geometric relationships. It expands an existing vision system that operates by fixating at interesting regions in a scene, extracting a number of raw primal sketch features from a log-polar image and matching new regions to previously seen ones.