Applied image processing
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Augmented Reality and Semi-automated Landmarking of Cephalometric Radiographs
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Fingerprint classification using a feedback-based line detector
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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This paper proposes an automated target recognition algorithm using Support Vector Machine (SVM) to extract landmark points for craniofacial features in cephalometry radiograph. The features are extracted by subjecting the radiograph to the Projected Principle Edge Distribution (PPED) algorithm. Edge flags are accumulated in every four columns and spatial distribution of edge flags are represented by a histogram. The resultants are the front end of support vector machine. Vectors, which posses land marks, are separated from all other vectors. The centroid points, automatically determined from PPED vectors, are the location of landmarks. The landmark points which are serving as a guide for construction and measurement of planes, are used to evaluate the dento-facial relationship, study of growth and development, and also for treatment planning.