Using classifiers as heuristics to describe local structure in Active Shape Models with small training sets
We show a method for small data sets in ASMs called Heuristic Local Appearance Model (HLAM). We extend the procedure to multi-channel images. We compare HLAM to the Mahalanobis appearance model and the edge search method. We use therefore a synthetic and a real dataset of images. The results show that HLAM outperforms the other two approaches.
heuristics active shape model small data set local appearance model machine learning