Fitting grey level point distribution models to animals in scenes

摘要

Point distribution models allow a compact description of an object's shape to be found from a set of example images. In previous work by the first author, a method of incorporating grey level information into a PDM was developed. This paper investigates fitting such a composite model to image data consisting of a set of images of a pig viewed from above. Model fitting is achieved by optimizing an objective function consisting of two components, one that measures the degree of grey level correspondence between the model and the data, and the other that measures how well the boundary of the model fits the data. The shape of the objective function as the model parameters are varied is investigated, and an optimization strategy developed. The strategy is used to find a pig in a number of images with backgrounds of increasing complexity. The strategy performs well with both an uncluttered and a realistic background. The performance with a simulated noisy background is not so good when the boundary component is included in the objective function. This is a result of the boundary component being more sensitive to noise in the image. In this case, it is better to optimize with the grey level component alone. A problem is identified when the grey level distribution changes significantly as the pig moves under the light source. It is suggested that this could be overcome by including variations in grey level distribution as modes in the model