The LaserFIB: new application opportunities combining a high-performance FIB-SEM with femtosecond laser processing in an integrated second chamber.

The development of the femtosecond laser (fs laser) with its ability to provide extremely rapid athermal ablation of materials has initiated a renaissance in materials science. Sample milling rates for the fs laser are orders of magnitude greater than that of traditional focused ion beam (FIB) sources currently used. In combination with minimal surface post-processing requirements, this technology is proving to be a game changer for materials research. The development of a femtosecond laser attached to a focused ion beam scanning electron microscope (LaserFIB) enables numerous new capabilities, including access to deeply buried structures as well as the production of extremely large trenches, cross sections, pillars and TEM H-bars, all while preserving microstructure and avoiding or reducing FIB polishing. Several high impact applications are now possible due to this technology in the fields of crystallography, electronics, mechanical engineering, battery research and materials sample preparation. This review article summarizes the current opportunities for this new technology focusing on the materials science megatrends of engineering materials, energy materials and electronics.

Recovering articulated pose: a comparison of two pre and postimposed constraint methods.

We contrast the performance of two methods of imposing constraints during the tracking of articulated objects, the first method preimposing the kinematic constraints during tracking and, thus, using the minimum degrees of freedom, and the second imposing constraints after tracking and, hence, using the maximum. Despite their very different formulations, the methods recover the same pose change. Further comparisons are drawn in terms of computational speed and algorithmic simplicity and robustness, and it is the last area which is the most telling. The results suggest that using built-in constraints is well-suited to tracking individual articulated objects, whereas applying constraints afterward is most suited to problems involving contact and breakage between articulated (or rigid) objects, where the ability to test tracking performance quickly with constraints turned on or off is desirable.

Guided-MLESAC: faster image transform estimation by using matching priors.

MLESAC is an established algorithm for maximum-likelihood estimation by random sampling consensus, devised for computing multiview entities like the fundamental matrix from correspondences between image features. A shortcoming of the method is that it assumes that little is known about the prior probabilities of the validities of the correspondences. This paper explains the consequences of that omission and describes how the algorithm's theoretical standing and practical performance can be enhanced by deriving estimates of these prior probabilities. Using the priors in guided-MLESAC is found to give an order of magnitude speed increase for problems where the correspondences are described by one image transformation and clutter. This paper describes two further modifications to guided-MLESAC. The first shows how all putative matches, ratherthan just the best, from a particularfeature can be taken forward into the sampling stage, albeit at the expense of additional computation. The second suggests how to propagate the output from one frame forward to successive frames. The additional information makes guided-MLESAC computationally realistic at video-rates for correspondence sets modeled by two transformations and clutter.

Reactive control of zoom while fixating using perspective and affine cameras.

This paper describes reactive visual methods of controlling the zoom setting of the lens of an active camera while fixating upon an object. The first method assumes a perspective projection and adjusts zoom to preserve the ratio of focal length to scene depth. The active camera is constrained to rotate, permitting self-calibration from the image motion of points on the static background. A planar structure from motion algorithm is used to recover the depth of the foreground. The foreground-background segmentation exploits the properties of the two different interimage homographies which are observed. The fixation point is updated by transfer via the observed planar structure. The planar method is shown to work on real imagery, but results from simulated data suggest that its extension to general 3D structure is problematical under realistic viewing and noise regimes. The second method assumes an affine projection. It requires no self-calibration and the zooming camera may move generally. Fixation is again updated using transfer, but now via the affine structure recovered by factorization. Analysis of the projection matrices allows the relative scale of the affine bases in different views to be found in a number of ways and, hence, controlled to unity. The various ways are compared and the best used on real imagery captured from an active camera fitted with a controllable zoom lens in both look-move and continuous operation.