Object recognition with constrained elastic models

Abstract

We present a model-based method for object identification in images of natural scenes. It has successfully been implemented for the classification of cars based on their rear view. In a first step, characteristic features such as lines and corners are detected within the image. Generic models of object-classes, described by the same set of features, are stored in a database. Each model represents a whole class of objects (e.g., passenger cars, vans, big trucks). In a preprocessing stage, the most probable object is selected by means of a corner-feature based Hough transform. This transformation also suggests the position and scale of the object in the image. Guided by similarity measures, the model is then aligned with image features using a matching algorithm based on the elastic net technique [1]. During this iterative process, the model is allowed to undergo changes in scale, position and certain deformations. Deformations are kept within limits such that one model can fit to all objects belonging to the same class, but not to objects of other classes. In each iteration step, quantities to assess the matching process are obtained.