Vincent Auvray

Motion-based segmentation of transparent layers in video sequences

We present a method for segmenting moving transparent layers in video sequences. We assume that the images can be divided into areas containing at most two moving transparent layers. We call this configuration (which is the mostly encountered one) bi-distributed transparency. The proposed method involves three steps: initial block-matching for two-layer transparent motion estimation, motion clustering with 3D Hough transform, and joint transparent layer segmentation and parametric motion estimation. The last step is solved by the iterative minimization of a MRF-based energy function. The segmentation is improved by a mechanism detecting areas containing one single layer. The framework is applied to various image sequences with satisfactory results.

Multiresolution parametric estimation of transparent motions

A new framework dealing with motion estimation in transparent images is presented. It relies on a block-oriented estimation involving an efficient multiresolution minimization. A downhill simplex method provides an appropriate initialization to this scheme. The estimated velocity vectors are greatly improved by an original postprocessing stage which performs a single motion estimation on differences of warped images. Finally, a regularization step is carried out. It is demonstrated on a large set of simulations that a quarter-pixel accuracy can be attained on noise-free images. The case of noisy images is also addressed and provides satisfactory results, even in the case of low-contrasted medical images. An example on real clinical images is also reported with promising results.

Motion Estimation in X-Ray Image Sequences with Bi-Distributed Transparency

This paper is concerned with motion estimation in transparent X-ray image sequences. Most of these medical images can be divided into areas containing at most two moving transparent layers. We will call it bi-distributed transparency. The first contribution of this paper is a motion estimation framework for the two-layer transparency case, able to handle noisy and low-contrasted X-ray image sequences. It involves three steps: block-matching, affine fit and gradient-based parametric estimation. This estimation scheme is then extended to the bi-distributed transparency case. The second step is now formulated as a joint motion segmentation-estimation problem solved by the iterative minimization of a MRF-based energy function. This framework has been applied to synthetic and real image sequences with quite satisfactory results.