Segmentation and tracking of video objects for a content-based video indexing context

This paper examines the problem of segmentation and tracking of video objects for content-based information retrieval. Segmentation and tracking of video objects plays an important role in index creation and user request definition steps. The object is initially selected using a semi-automatic approach. For this purpose, a user-based selection is required to define roughly the object to be tracked. In this paper, we propose two different methods to allow an accurate contour definition from the user selection. The first one is based on an active contour model which progressively refines the selection by fitting the natural edges of the object while the second used a binary partition tree with aPeer ReviewedPostprint (published version

The Discovery of a Companion to the Very Cool Dwarf Gl~569~B with the Keck Adaptive Optics Facility

We report observations obtained with the Keck adaptive optics facility of the nearby (d=9.8 pc) binary Gl~569. The system was known to be composed of a cool primary (dM2) and a very cool secondary (dM8.5) with a separation of 5" (49 Astronomical Units). We have found that Gl~569~B is itself double with a separation of only 0".101$\pm$0".002 (1 Astronomical Unit). This detection demonstrates the superb spatial resolution that can be achieved with adaptive optics at Keck. The difference in brightness between Gl~569~B and the companion is $\sim$0.5 magnitudes in the J, H and K' bands. Thus, both objects have similarly red colors and very likely constitute a very low-mass binary system. For reasonable assumptions about the age (0.12~Gyr--1.0~Gyr) and total mass of the system (0.09~M$_\odot$--0.15~M$_\odot$), we estimate that the orbital period is $\sim$3 years. Follow-up observations will allow us to obtain an astrometric orbit solution and will yield direct dynamical masses that can constrain evolutionary models of very low-mass stars and brown dwarfs

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral