Section: New Results

Visual tracking

We addressed the challenge of detecting and localizing a poorly textured known object, by initially estimating its complete 3D pose in a video sequence [45] . Our solution relies on the 3D model of the object and synthetic views. The full pose estimation process is then based on foreground/background segmentation and on an efficient probabilistic edge-based matching and alignment procedure with the set of synthetic views, classified through an unsupervised learning phase. Our study focuses on space robotics applications and the method has been tested on both synthetic and real images, showing its efficiency and convenience, with reasonable computational costs.

This study has been realized in collaboration with Brahim Tamadazte and Nicolas Andreff from Femto-ST, Besançon. Following our developments in visual tracking and visual servoing from the mutual information [3] , it concerned mutual information-based registration of white light images vs. fluorescence images for microrobotic laser microphonosurgery of the vocal folds. Nelder-Mead Simplex for nonlinear optimization has been used to minimize the cost-function [43] .

RGB-D sensors have become in recent years a product of easy access to general users. They provide both a color image and a depth image of the scene and, besides being used for object modeling, they can also offer important cues for object detection and tracking in real-time. In this context, the work presented in this paper investigates the use of consumer RGB-D sensors for object detection and pose estimation from natural features. Two methods based on depth-assisted rectification are proposed, which transform features extracted from the color image to a canonical view using depth data in order to obtain a representation invariant to rotation, scale and perspective distortions. While one method is suitable for textured objects, either planar or non-planar, the other method focuses on texture-less planar objects [18]

This study is realized in the scope of the ANR VisioLand project (see Section  9.2.2 . In a first step, we have considered and adapted our model-based tracker [2] to localize the aircraft with respect to the airport surroundings. Satisfactory results have been obtained from real image sequences provided by Airbus. In a second step, we have started to perform this localization from a set of keyframe images corresponding to the landing trajectory.