Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
n-SIFT: n-dimensional scale invariant feature transform
IEEE Transactions on Image Processing
Fusion Moves for Markov Random Field Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Cephalometric Landmark Identification Using Shape and Local Appearance Models
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
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In this paper an automated method is presented for the localization of cephalometric landmarks in craniofacial cone-beam computed tomography images. This methodmakes use of a statistical sparse appearance and shape model obtained fromtraining data. The sparse appearance model captures local image intensity patterns around each landmark. The sparse shape model, on the other hand, is constructed by embedding the landmarks in a graph. The edges of this graph represent pairwise spatial dependencies between landmarks, hence leading to a sparse shape model. The edges connecting different landmarks are defined in an automated way based on the intrinsic topology present in the training data. A maximum a posteriori approach is employed to obtain an energy function. To minimize this energy function, the problem is discretized by considering a finite set of candidate locations for each landmark, leading to a labeling problem. Using a leave-one-out approach on the training data the overall accuracy of the method is assessed. The mean and median error values for all landmarks are equal to 2.41 mm and 1.49 mm, respectively, demonstrating a clear improvement over previously published methods.