Target detection and classification using a deformable template

Two common image processing problems are determining the location of an object using a template when the size and rotation of the true target are unknowns and classifying an object into one of a library of objects again using a template-based matching technique. When employing a maximum likelihood approach to these problems, complications occur due to local maxima on the likelihood surface. In previous work, we demonstrated a technique for object localization which employs a library of templates starting from the smooth approximation and adding detail until the exact template is reached. Successively estimating the geometric parameters (i.e. size and rotation) using these templates achieves the accuracy of the exact template while remaining within a well-behaved 'bowl' in the search space which allows standard maximization techniques to be used. In this work, we show how this technique can be extended to solve the classification problem using a multiple template library. We introduce a steering parameter which at every scale, allows us to compute a template as a linear combination of templates in the library. The algorithm begins the template matching using a smooth blob which is the smooth approximation common to all templates in the library. As the location and geometric parameter estimates are improved and detail is added, the smooth template is 'steered' towards the most likely template in the library and thus classification is achieved.