Abstract
This paper is concerned with the estimation of the motions and the segmentation of the spatial supports of the different layers involved in transparent X-ray image sequences. Classical motion estimation methods fail on sequences involving transparent effects since they do not explicitly model this phenomenon. We propose a method that comprises three main 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. It is validated on synthetic and real clinical X-ray image sequences. Secondly, we derive an original transparent motion compensation method compatible with any spatiotemporal filtering technique. A direct transparent motion compensation method is proposed. To overcome its limitations, a novel hybrid filter is introduced which locally selects which type of motion compensation is to be carried out for optimal denoising. Convincing experiments on synthetic and real clinical images are also reported.
Highlights
Most image sequence processing and analysis tasks require an accurate computation of image motion
We introduce a new version of the TMC (1) that we call the Parametric Transparent Motion Constraint (PTMC): r x, y, wθ1, wθ2 = I x + uθ1 + uθ2, y + vθ1 + vθ2, t − 1
We present motion estimation results out of three real clinical image sequences and one video
Summary
Most image sequence processing and analysis tasks require an accurate computation of image motion. We deal both with transparent motion estimation and spatial segmentation of the transparent layers in the images. Motions, whereas a separation framework [3,4,5] leads to recover the gray value images of the different transparent objects The latter can be handled so far in restricted situations only (e.g., specific motion must be assumed for at least one layer, the image globally includes only two layers), while we consider any type of motions and any number of layers.
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