Unsupervised bayesian convex deconvolution based on a field with an explicit partition function

This paper proposes a non-Gaussian Markov field with a special feature: an explicit partition function. To the best of our knowledge, this is an original contribution. Moreover, the explicit expression of the partition function enables the development of an unsupervised edge-preserving convex deconvolution method. The method is fully Bayesian, and produces an estimate in the sense of the posterior mean, numerically calculated by means of a Monte-Carlo Markov Chain technique. The approach is particularly effective and the computational practicability of the method is shown on a simple simulated example

Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201

Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201

Extraction of Unfoliaged Trees from Terrestrial Image Sequences

This thesis presents a generative statistical approach for the fully automatic three-dimensional (3D) extraction and reconstruction of unfoliaged deciduous trees from wide-baseline image sequences. Tree models improve the realism of 3D Geoinformation systems (GIS) by adding a natural touch. Unfoliaged trees are, however, difficult to reconstruct from images due to partially weak contrast, background clutter, occlusions, and particularly the possibly varying order of branches in images from different viewpoints. The proposed approach combines generative modeling by L-systems and statistical maximum a posteriori (MAP) estimation for the extraction of the 3D branching structure of trees. Background estimation is conducted by means of mathematical (gray scale) morphology as basis for generative modeling. A Gaussian likelihood function based on intensity differences is employed to evaluate the hypotheses. A mechanism has been devised to control the sampling sequence of multiple parameters in the Markov Chain considering their characteristics and the performance in the previous step. A tree is classified into three typical branching types after the extraction of the first level of branches and more specific Production Rules of L-systems are used accordingly. Generic prior distributions for parameters are refined based on already extracted branches in a Bayesian framework and integrated into the MAP estimation. By these means most of the branching structure besides tiny twigs can be reconstructed. Results are presented in the form of VRML (Virtual Reality Modeling Language) models demonstrating the potential of the approach as well as its current shortcomings.Diese Dissertationsschrift stellt einen generativen statistischen Ansatz für die vollautomatische drei-dimensionale (3D) Extraktion und Rekonstruktion unbelaubter Laubbäume aus Bildsequenzen mit großer Basis vor. Modelle für Bäume verbessern den Realismus von 3D Geoinformationssystemen (GIS), indem sie Letzteren eine natürliche Note geben. Wegen z.T. schwachem Kontrast, Störobjekten im Hintergrund, Verdeckungen und insbesondere der möglicherweise unterschiedlichen Ordnung der Äste in Bildern von verschiedenen Blickpunkten sind unbelaubte Bäume aber schwierig zu rekonstruieren. Der vorliegende Ansatz kombiniert generative Modellierung mittels L-Systemen und statistische Maximum A Posteriori (MAP) Schätzung für die Extraktion der 3D Verzweigungsstruktur von Bäumen. Hintergrund-Schätzung wird auf Grundlage von mathematischer (Grauwert) Morphologie als Basis für die generative Modellierung durchgeführt. Für die Bewertung der Hypothesen wird eine Gaußsche Likelihood-Funktion basierend auf Intensitätsunterschieden benutzt. Es wurde ein Mechanismus entworfen, der die Reihenfolge der Verwendung mehrerer Parameter für die Markoff-Kette basierend auf deren Charakteristik und Performance im letzten Schritt kontrolliert. Ein Baum wird nach der Extraktion der ersten Stufe von Ästen in drei typische Verzweigungstypen klassifiziert und es werden entsprechend Produktionsregeln von spezifischen L-Systemen verwendet. Basierend auf bereits extrahierten Ästen werden generische Prior-Verteilungen für die Parameter in einem Bayes’schen Rahmen verfeinert und in die MAP Schätzung integriert. Damit kann ein großer Teil der Verzweigungsstruktur außer kleinen Ästen extrahiert werden. Die Ergebnisse werden als VRML (Virtual Reality Modeling Language) Modelle dargestellt. Sie zeigen das Potenzial aber auch die noch vorhandenen Defizite des Ansatzes