An Evaluation of Mesh Model Algorithms For Direct Feature Detection on Compressed Image Representations

Recent developments in mesh modelling of images have provided algorithms that can achieve accurate and efficient image representations without the high computational cost associated with earlier optimisation-based methods. Hence nonuniform sampling of images combined with the use of irregular content-based meshing has provided a successful basis for recent developments in image compression techniques. The evaluation of these techniques has focussed on the accuracy and efficiency with which the mesh model can represent the image. For real-time applications, the usefulness of a mesh model may be assessed by its ability to yield compressed image representations that can be processed directly to provide output that is sufficiently accurate. Hence we present an evaluation of mesh model algorithms that is based on feature detection on the associated compressed image representations. Such an approach is built on the recent development of systematic design procedures for scalable and adaptive image processing operators that can be applied directly to non-uniformly sampled images. We demonstrate the approach using image derivative operators on compressed images.