Building Shape Models from Image Sequences using Piecewise Linear Approximation

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 automatically extracted 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 Distribution Model [16]. A `fit score' metric is defined which gives an indication of how well a given shape fits a model.

• Type: Conference
• Year: 1998
• Where: BMVC
• Authors: Derek R. Magee, Roger D. Boyle
• Keywords: BMVC 1998, Computer Vision, Non-rigid Shapes, Polygons, Principal Component Analysis