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Model-based vision is firmly established as a robust approach to recognizing and locating known rigid objects in the presence of noise, clutter, and occlusion. It is more problematic to apply model-based methods to images of objects whose appearance can vary, though a number of approaches based on the use of flexible templates have been proposed. The(More)
A method for building flexible shape models is presented in which a shape is represented by a set of labelled points. The technique determines the statistics of the points over a collection of example shapes. The mean positions of the points give an average shape and a number of modes of variation are determined describing the main ways in which the example(More)
We have developed a trainable method of shape representation which can automatically capture the invariant properties of a class of shapes and provide a compact parametric description of variability. We have applied the method to a family of flexible ribbons (worms) and to heart shapes in echocardiograms. We show that in both cases a natural(More)
Any model-based image interpretation system must be capable of describing objects, whose appearance in real images can vary widely, in sufficient detail to ensure that robust location of objects is possible. The system must cope with circumstances where data is incomplete , for example when touching and occlusion occur. We argue that to achieve this it is(More)
This paper is concerned with generating object cues from grey-level images for use in model-based image interpretation. We describe the idea of local grey-level symmetry and illustrate how points in the grey-level image with this property form local axes of symmetry. These axes together with appropriate scale information form the object cues. The degree of(More)