http://www.cs.technion.ac.il/~octavian/PUBLICATIONS/RangeImageCurvatures.pdf

A comparison of Gaussian and mean curvature estimation methods on triangular meshes of range image data

Postgraduate Students: Vladimír Arnot, Daniel Čapek, Rudolf Čejka, Dao Minh, Tomá Dulík, Martin Hrubý, Radek Kočí, Petr Kotásek, Marek Křejpský, Bohuslav Křena, Vladislav Kubíček, Vladimír Marek, Petr Matouek, Ale Mičín, Jiří Očenáek, Tomá Ondráček, Petr Peňás, Jaroslav Ráb, Richard Růička, Luká Sekanina, Ivan Schwarz, Azedien Sllame, Petr Smolík, Jiří Staroba, Josef Strnadel, Luká Szemla, Pavel Tinovský, Michal Tomů, Milan Urbáek, Michal Vojkůvka, Petr Vurm, Frantiek Zbořil

Department of Computer Science and Engineering

Quantification of the evolution of the 3D intermetallic structure in a 6005A aluminium alloy during a homogenisation treatment

Autonomous aerial refueling (AAR) is an important capability of unmanned aerial vehicles (UAVs) for further applications in both military and civilian domains. This paper focuses on the description and comparison of visual measurement strategies in the simulation environment of AAR of UAVs. The visual measurement methods are employed for pose estimation between tanker and UAV receiver aircrafts. Within this effort, methods and techniques for marker detection, pose estimation, and visualization of aerial refueling in virtual reality have been designed and implemented. Series of comparative experiments indicate the preference of various visual measurement approaches in our work. Boom and stationkeeping controllers are designed to ensure system stability when wind turbulence is induced. Furthermore, the hardware-in-loop simulation platform is built to demonstrate the feasibility and effectiveness of the proposed scenarios in actual conditions. The visual attention mechanism is adopted in the preprocessing for the images of virtual scene to remove potential noises. The achieved results make excellent performances of our proposed scheme during the docking maneuver in AAR.

Visual Measurement in Simulation Environment for Vision-Based UAV Autonomous Aerial Refueling

Rapid surface registration of 3D volumes using a neural network approach

Curvature-based surface features are well suited for use in multimodal medical image registration. The accuracy of such feature-based registration techniques is dependent upon the reliability of the feature computation. The computation of curvature features requires second derivative information that is best obtained from a parametric surface representation. We present a method of explicitly parameterizing surfaces from volumetric data. Surfaces are extracted, without a global thresholding, using active contour models. A monge/spl acute/ basis for each surface patch is estimated and used to transform the patch into local, or parametric, coordinates. Surface patches are fit to a bicubic polynomial in local coordinates using least squares solved by singular value decomposition. We tested our method by reconstructing surfaces from the surface model and analytically computing Gaussian and mean curvatures. The model was tested on analytical and medical data.

Surface parameterization in volumetric images for curvature-based feature classification

Curvature-based surface features are well suited for use in multimodal medical image registration. The accuracy of such feature-based registration techniques is dependent upon the reliability of the feature computation. The computation of curvature features requires second derivative information that is best obtained from a parametric surface representation. The authors present a method of explicitly parametrizing surfaces from volumetric data. Surfaces are extracted, without a global thresholding, using active contour models. A monge basis for each surface patch is estimated and used to transform the patch into local, or parametric, coordinates. Surface patches are fit to a bicubic polynomial in local coordinates using least squares solved by singular value decomposition. The authors tested their method by reconstructing surfaces from the surface model and analytically computing gaussian and mean curvatures. The model was tested on analytical and medical data.

R.W.I. Yarger

Papers

Surface parameterization in volumetric images for curvature-based feature classification

Surface parameterization in volumetric images for feature classification