In this paper we present an improved approach for the
localization and classification of 3-D objects
in 2-D gray level images.
We use an appearance-based approach and
calculate local feature vectors by the
coefficients of the wavelet multiresolution analysis.
Thereby the features are modelled statistically.
Since the appearance of the objects,
i.e. also the size of the objects in the image,
vary due to out-of-image-plane transformations,
the features themselves as well as the
region of interest
are modelled as
function of the external transformations.
We present and test
different measurements for the recognition.
The experiments on a large dataset
with more than 40000 images
show that the approach is well
suited for this recognition task.
