Search Results for author:
Found 16 papers, 0 papers with code
Towards Efficient Time Stepping for Numerical Shape Correspondence
The goal of this investigation is to assess, if one may identify a useful property of methods for time integration for the shape analysis context.
An Approach to Colour Morphological Supremum Formation using the LogSumExp Approximation
These are based on the construction of a supremum or infimum with respect to an order over the tonal range in a certain section of the image.
Morphological Sampling Theorem and its Extension to Grey-value Images
In classic literature, a morphological sampling theorem has been established, which shows how sampling interacts by morphological operations with image reconstruction.
The Polynomial Connection between Morphological Dilation and Discrete Convolution
In this paper we consider the fundamental operations dilation and erosion of mathematical morphology.
On the Regularising Levenberg-Marquardt Method for Blinn-Phong Photometric Stereo
Photometric stereo refers to the process to compute the 3D shape of an object using information on illumination and reflectance from several input images from the same point of view.
Long Short-Term Memory Neural Network for Temperature Prediction in Laser Powder Bed Additive Manufacturing
This is realized by employing heat maps produced by an optimized printing protocol simulation and used for training a specifically tailored recurrent neural network in terms of a long short-term memory architecture.
YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment
In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik industry 4. 0 application.
Adaptive neural domain refinement for solving time-dependent differential equations
In the present work, we propose a novel adaptive neural approach to meet this aim for solving time-dependent problems.
Collocation Polynomial Neural Forms and Domain Fragmentation for solving Initial Value Problems
Several neural network approaches for solving differential equations employ trial solutions with a feedforward neural network.
Computational characteristics of feedforward neural networks for solving a stiff differential equation
Computational approaches are in general highly dependent on a variety of computational parameters as well as on the choice of optimisation methods, a point that has to be seen together with the structure of the cost function.
Optimisation of photometric stereo methods by non-convex variational minimisation
Estimating shape and appearance of a three dimensional object from a given set of images is a classic research topic that is still actively pursued.
Photometric stereo for strong specular highlights
We address 3-D reconstruction by PS using a more realistic set of assumptions combining for the first time the complete Blinn-Phong reflectance model and perspective projection.
Clustering-Based Quantisation for PDE-Based Image Compression
We are lead to the central question which kind of feature vectors are best suited for image compression.
Fast and Accurate Surface Normal Integration on Non-Rectangular Domains
The integration of surface normals for the purpose of computing the shape of a surface in 3D space is a classic problem in computer vision.
Bregman Iteration for Correspondence Problems: A Study of Optical Flow
Bregman iterations are known to yield excellent results for denoising, deblurring and compressed sensing tasks, but so far this technique has rarely been used for other image processing problems.
Direct Variational Perspective Shape from Shading with Cartesian Depth Parametrisation
First, we propose a novel variational model that operates directly on the Cartesian depth.
