Adaptive structure tensors and their applications

The structure tensor, also known as second moment matrix or Förstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This article wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the dataadaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered

The Early Royal Society and Visual Culture

Recent studies have fruitfully examined the intersection between early modern science and visual culture by elucidating the functions of images in shaping and disseminating scientific knowledge. Given its rich archival sources, it is possible to extend this line of research in the case of the Royal Society to an examination of attitudes towards images as artefacts –manufactured objects worth commissioning, collecting and studying. Drawing on existing scholarship and material from the Royal Society Archives, I discuss Fellows’ interests in prints, drawings, varnishes, colorants, images made out of unusual materials, and methods of identifying the painter from a painting. Knowledge of production processes of images was important to members of the Royal Society, not only as connoisseurs and collectors, but also as those interested in a Baconian mastery of material processes, including a “history of trades”. Their antiquarian interests led to discussion of painters’ styles, and they gradually developed a visual memorial to an institution through portraits and other visual records.AH/M001938/1 (AHRC

A Coarse To Fine Multiscale Approach For Linear Least Squares Optical Flow Estimation

Tensor-based approaches for optical flow have been often criticized because of their limitations in handling large motions. This paper shows how to adapt them in a multiscale coarse-to-fine strategy. We show how the same ideas used in the variational framework can be adapted by working with both a multiscale image sequence as well as a multiscale, motion compensated tensor field. Several experiments are presented in order to compare it to some recent well known multiscale techniques. We demonstrate how this approach offers a good compromise between precision and computational efficiency.

A Coarse to Fine Multiscale Approach for Linear Least Squares Optical Flow Estimation

Tensor-based approaches for optical flow have been often criticized because of their limitations in handling large motions. This paper shows how to adapt them in a multiscale coarse-to-fine strategy. We show how the same ideas used in the variational framework can be adapted by working with both a multiscale image sequence as well as a multiscale, motion compensated tensor field. Several experiments are presented in order to compare it to some recent well known multiscale techniques. We demonstrate how this approach offers a good compromise between precision and computational efficiency