Building Shape Models from Image Sequences using Piecewise Linear Approximation
Derek R. Magee, R. D. Boyle · 1998
A method of extracting, classifying and modelling non-rigid shapes from an image sequence is presented. Shapes are approximated by polygons where the number of sides is related to the physical features of a shape class rather than any particular shape. A method of `seeding' the polygonal approximation is given where `seeds' are automaticallyextracted from a set of data. Multiple models are built using polygons with different numbers of sides to allow for feature occlusion. Principal component analysis (PCA) is performed on vector representations of the sides of the polygons which are normalised by the total perimeter. This removes the need for normalisation of scale and translation as required in the Point DistributionModel [16]. A `fit score' metric is defined which gives an indicationof how well a given shape fits a model. 1 Introduction There has been much work carried out on the extraction and classification of non-rigid shapes from image sequences. In this paper a new ...