Pattern Recognition
Review of shape coding techniques
Image and Vision Computing
On the Sensitivity of the Hough Transform for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
A Parallel Mechanism for Detecting Curves in Pictures
IEEE Transactions on Computers
An analysis of the elastic net approach to the traveling salesman problem
Neural Computation
Elastic Matching of Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.