Tuesday, August 27, 2013

Lab Meeting, August 29, 2013 (Chiang Yi): Pose Estimation using Local Structure-Specific Shape and Appearance Context

Title: Pose Estimation using Local Structure-Specific Shape and Appearance Context

Authors: Anders Glent Buch, Dirk Kraft, Joni-Kristian Kamarainen, Henrik Gordon Petersen and Norbert Kr ̈uger

Abstract: We address the problem of estimating the alignment pose between two models using structure-specific local descriptors. Our descriptors are generated using a combination of 2D image data and 3D contextual shape data, resulting in a set of semi-local descriptors containing rich appearance and shape information for both edge and texture structures. This is achieved by defining feature space relations which describe the neighborhood of a descriptor. By quantitative evaluations, we show that our descriptors provide high discriminative power compared to state of the art approaches. In addition, we show how to utilize this for the estimation of the alignment pose between two point sets. We present experiments both in
controlled and real-life scenarios to validate our approach.

From: ICRA 2013

Link: http://covil.sdu.dk/publications/paper1099.pdf

Monday, August 19, 2013

Lab Meeting, August 22, 2013 (Yen-Ting): Joint Optimization for Object Class Segmentation and Dense Stereo Reconstruction

Title: Joint Optimization for Object Class Segmentation and Dense Stereo Reconstruction

Authors: Lubor Ladick√Ĺ, Paul Sturgess, Chris Russell, Sunando Sengupta, Yalin Bastanlar, William Clocksin and Philip H.S. Torr

Abstract: The problems of dense stereo reconstruction and object class segmentation can both be formulated as Random Field labeling problems, in which every pixel in the image is assigned a label corresponding to either its disparity, or an object class such as road or building. While these two problems are mutually informative, no attempt has been made to jointly optimize their labelings. In this work we provide a flexible framework configured via cross-validation that unifies the two problems and demonstrate that, by resolving ambiguities, which would be present in real world data if the two problems were considered separately, joint optimization of the two problems substantially improves performance. To evaluate our method, we augment the Leuven data set (http://cms.brookes.ac.uk/research/visiongroup/files/Leuven.zip), which is a stereo video shot from a car driving around the streets of Leuven, with 70 hand labeled object class and disparity maps. We hope that the release of these annotations will stimulate further work in the challenging domain of street view analysis. Complete source code is publicly available (http://cms.brookes.ac.uk/staff/Philip-Torr/ale.htm).

From: International Journal of Computer Vision (IJCV), 2012

Link: Click here

Wednesday, August 07, 2013

Lab Meeting, August 8, 2013 (Channing): Multi-Robot System for Artistic Pattern Formation

Title: Multi-Robot System for Artistic Pattern Formation (ICRA 2011)
Authors: Javier Alonso-Mora, Andreas Breitenmoser, Martin Rufli, Roland Siegwart and Paul Beardsley

Abstract: This paper describes work on multi-robot pattern formation. Arbitrary target patterns are represented with an optimal robot deployment, using a method that is independent of the number of robots. Furthermore, the trajectories are visually appealing in the sense of being smooth, oscillation free, and showing fast convergence. A distributed controller guarantees collision free trajectories while taking into account the kinematics of differentially driven robots. Experimental results are provided for a representative set of patterns, for a swarm of up to ten physical robots, and for fifty virtual robots in simulation.

Paper Link: click here.