Active shape model unleashed with multi-scale local appearance

We focus on optimising the Active Shape Model (ASM) with several extensions. The modification is threefold. First, we tackle the over-constraint problem and obtain an optimal shape with minimum energy considering both the shape prior and the salience of local features, based on statistical theory: a compact closed form solution to the optimal shape is deduced. Second, we enhance the ASM searching method by modelling and removing the variations of local appearance presented in the training data. Third, we speed up the convergence of shape fitting by integrating information from multi-scale local features simultaneously. Experiments show significant improvement brought by these modifications, i.e., optimal shape against standard relaxation methods dealing with inadequate training samples; enhanced searching method against standard gradient descent methods in searching accuracy; multi-scale local features against popular coarse-to-fine strategies in convergence speed.