A system for automatic detection of cephalometric landmarks is presented. Landmark detection is carried out in two steps: a line detection module searches for significant, well-contrasted lines of the image, such as the jaw line or the nasal spine. The landmark detection module uses the lines located in the first module to determine the search areas and then applies a pattern detection algorithm, based on mathematical morphology techniques. Relations between landmarks and lines are determined by means of a training process. The system has been tested for the detection of 17 landmarks on 20 images: more than 90% of the landmarks are accurately identified.

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In this paper, we present an automatic classification framework combining appearance based features and hidden Markov models (HMM) to detect unusual events in image sequences. One characteristic of the classification task is that anomalies are rare. This reflects the situation in the quality control of industrial processes, where error events are scarce by nature. As an additional restriction, class labels are only available for the complete image sequence, since frame-wise manual scanning of the recorded sequences for anomalies is too expensive and should, therefore, be avoided. The proposed framework reduces the feature space dimension of the image sequences by employing subspace methods and encodes characteristic temporal dynamics using continuous hidden Markov models (CHMMs). The applied learning procedure is as follows. 1) A generative model for the regular sequences is trained (one-class learning). 2) The regular sequence model (RSM) is used to locate potentially unusual segments within error sequences by means of a change detection algorithm (outlier detection). 3) Unusual segments are used to expand the RSM to an error sequence model (ESM). The complexity of the ESM is controlled by means of the Bayesian Information Criterion (BIC). The likelihood ratio of the data given the ESM and the RSM is used for the classification decision. This ratio is close to one for sequences without error events and increases for sequences containing error events. Experimental results are presented for image sequences recorded from industrial laser welding processes. We demonstrate that the learning procedure can significantly reduce the user interaction and that sequences with error events can be found with a small false positive rate. It has also been shown that a modeling of the temporal dynamics is necessary to reach these low error rates.

We propose a new technique for elastic deformation restriction of active contour models to particular object shapes. For this purpose we apply localized multi-scale contour parametrization based on the 1D dyadic Wavelet Transform (WT) as a multi-scale boundary curve analysis tool. Our approach determines the WT-coefficients within a certain scale range, which differ significantly from the correspondent WT-coefficients of the most similar model in a training set. Those WTcoefficients are replaced by the correspondent model WT-coefficients to perform the reconstruction of the contour. The difference of the original deformed contour and the reconstructed contour is used as inner snake forces. By this technique it can be avoided, that the deformable contour is trapped into disturbing local minima of the snakes potential due to noise or irrelevant image features. The contour deformation method is integrated in a coarse to fine segmentation frame based on a multiscale image edge representation using the local modulus maxima of the dyadic Wavelet Transform. For detection of the object's position and initialization of the snake we apply a multiresolution binary matched filter at a coarse scale containing few detail information.