Secondary Hardening of a High-N Ni-Free Stainless Steel.

High-N Ni-free stainless steels are used for their excellent mechanical properties combined with their high corrosion resistance, especially for biomedical applications. Even though it is well-known that secondary hardening during annealing after cold working has been observed in many materials, this phenomenon was not reported for these materials, one of the best known being Biodur108©, although numerous efforts have been made to increase its hardness. In this work, thermomechanical treatments at low temperature of cold-deformed Biodur108© were conducted to increase the hardness. Hardness as high as 830 Hv was obtained. For this material, the annealing of a deformed sample at intermediate temperature leads to a secondary hardening phenomenon. The mechanisms responsible for this secondary hardening were analyzed. It was found that for deformed samples, annealing at 575 °C leads to the formation of small Cr2N precipitates along grain boundaries and sub-grain boundaries, and simultaneously with a new body-centered cubic (BCC) phase that possesses a super structure. The newly formed phases have sub-micrometric grain sizes.

Adjustment of Sonar and Laser Acquisition Data for Building the 3D Reference Model of a Canal Tunnel.

In this paper, we focus on the construction of a full 3D model of a canal tunnel by combining terrestrial laser (for its above-water part) and sonar (for its underwater part) scans collected from static acquisitions. The modeling of such a structure is challenging because the sonar device is used in a narrow environment that induces many artifacts. Moreover, the location and the orientation of the sonar device are unknown. In our approach, sonar data are first simultaneously denoised and meshed. Then, above- and under-water point clouds are co-registered to generate directly the full 3D model of the canal tunnel. Faced with the lack of overlap between both models, we introduce a robust algorithm that relies on geometrical entities and partially-immersed targets, which are visible in both the laser and sonar point clouds. A full 3D model, visually promising, of the entrance of a canal tunnel is obtained. The analysis of the method raises several improvement directions that will help with obtaining more accurate models, in a more automated way, in the limits of the involved technology.

The guided bilateral filter: when the joint/cross bilateral filter becomes robust.

The bilateral filter and its variants, such as the joint/cross bilateral filter, are well-known edge-preserving image smoothing tools used in many applications. The reason of this success is its simple definition and the possibility of many adaptations. The bilateral filter is known to be related to robust estimation. This link is lost by the ad hoc introduction of the guide image in the joint/cross bilateral filter. We here propose a new way to derive the joint/cross bilateral filter as a particular case of a more generic filter, which we name the guided bilateral filter. This new filter is iterative, generic, inherits the robustness properties of the robust bilateral filter, and uses a guide image. The link with robust estimation allows us to relate the filter parameters with the statistics of input images. A scheme based on graduated nonconvexity is proposed, which allows converging to an interesting local minimum even when the cost function is nonconvex. With this scheme, the guided bilateral filter can handle non-Gaussian noise on the image to be filtered. A complementary scheme is also proposed to handle non-Gaussian noise on the guide image even if both are strongly correlated. This allows the guided bilateral filter to handle situations with more noise than the joint/cross bilateral filter can work with and leads to high peak signal-to-noise ratio values as shown experimentally.

Robust pose estimation and recognition using non-gaussian modeling of appearance subspaces.

We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm.

Affine-invariant geometric shape priors for region-based active contours.

We present a new way of constraining the evolution of a region-based active contour with respect to a reference shape. Minimizing a shape prior, defined as a distance between shape descriptors based on the Legendre moments of the characteristic function, leads to a geometric flow that can be used with benefits in a two-class segmentation application. The shape model includes intrinsic invariance with regard to pose and affine deformations.