The paper describes a machine learning approach for improving active shape model segmentation, which can achieve high detection rates. Rather than represent the image structure using intensity gradients, we extract local edge features for each landmark using steerable filters. A machine learning algorithm based on AdaBoost selects a small number of critical features from a large set and yields extremely efficient classifiers. These non-linear classifiers are used, instead of the linear Mahalanobis distance, to find optimal displacements by searching along the direction perpendicular to each landmark. These features give more accurate and reliable matching between model and new images than modeling image intensity alone. Experimental results demonstrated the ability of this improved method to accurately locate edge features. © Springer-Verlag 2004.
CITATION STYLE
Li, S., Zhu, L., & Jiang, T. (2004). Active shape model segmentation using local edge structures and adaboost. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3150, 121–128. https://doi.org/10.1007/978-3-540-28626-4_15
Mendeley helps you to discover research relevant for your work.