We describe 'Active Shape Models' which iteratively adapt to refine estimates of the pose, scale and shape of models of image objects. The method uses flexible models derived from sets of training examples. These models, known as Point Distribution Models, represent objects as sets of labelled points. An initial estimate of the location of the model points in an image is improved by attempting to move each point to a better position nearby. Adjustments to the pose variables and shape parameters are calculated. Limits are placed on the shape parameters ensuring that the example can only deform into shapes conforming to global constraints imposed by the training set. An iterative procedure deforms the model example to find the best fit to the image object. Results of applying the method are described. The technique is shown to be a powerful method for refining estimates of object shape and location.

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 shapes tend to deform from the average. In this way allowed variation in shape can be included in the model. The method produces a compact flexible 'Point Distribution Model' with a small number of linearly independent parameters, which can be used during image search. We demonstrate the application of the Point Distribution Model in describing two classes of shapes.

The Active Shape Model(ASM) is an iterative algorithm for image interpretation based upon a Point Distribution Model. Each iteration of the ASM has two steps: Image data interrogation followed by shape approximation. Here we consider the shape approximation step in detail. We present a new method of shape approximation which uses directional constraints. We show how the error term for the shape approximation problem can be extended to cope with directional constraints and present iterative solutions to the 2D and 3D problems. We also show how the error term can be modified to allow a closed solution in the 2D case.

We describe a generic approach to image interpretation, based on combining a general method of building flexible template models with Genetic Algorithm (GA) search. The method can be applied to a given image interpretation problem simply by training a Point Distribution Model (PDM), using a set of examples of the image structure to be located. A local optimisation technique, developed for use with PDMs, has been incorporated into the GA search with the aim of improving the speed of convergence and optimality of solution. We present results, from three practical applications, demonstrating that the new method offers significant improvements when compared to previously reported approaches to flexible template matching. The benefits include the ability to deal with different domains of application using a standard method, the ability to deal with complex multi-part models and improved search performance.

In an earlier paper [1] we have proposed a shape representation called the CLD (Chord Length Distribution) which possesses many of the often-quoted desirable properties of a shape representation. It also captures shape variability and complements an object location method using belief updating which integrates low-level evidence and shape constraints. Promising results on synthetic and real rigid objects were given. This paper describes a development to the original definition which makes the location method robust with respect to clutter. We give experimental results which demonstrate the performance of the revised scheme on a class of flexible shapes, both singly and overlapping.We are currently engaged in a research project [see acknowledgements] concerned with automated 2-D inspection of complex (industrial) assemblies. In common with many machine vision applications we seek to exploit object shape and other geometrical constraints to assist in locating objects in scenes and evaluating interpretations with respect to expected appearance. To this end we need suitable representations for shape (intra-object) and inter-object relationships together with location and verification schemes capable of exploiting such representations. Ideally we seek a scheme capable of addressing both shape and inter-object relationships in a uniform manner.We have argued [1] that a shape representation not only needs to satisfy often-quoted [2,3] properties of being easily computable, unique, and exhibiting proportional behaviour, but must also describe expected variability and invariance within a class of shapes and be capable of describing a wide range of shape classes.We have proposed such a representation called a Chord Length Distribution (CLD) and an associated object location scheme which exploits and integrates geometrical (shape) constraints with low-level (edge) evidence in a principled way, originally based on ideas derived from probabilistic reasoning using networks [4].Unlike many reported methods of applying shape models [5,6,7,8] our approach does not work by matching image primitives to related model elements. Rather, it seeks to label each point in an ordinate space with a likelihood of correspondence to the