Currently, Deep Learning (DL) shows us powerful capabilities for image processing. But it cannot output the exact photometric process parameters and shows non-interpretable results. Considering such limitations, this paper presents a robot vision system based on Convolutional Neural Networks (CNN) and Monte Carlo algorithms. As an example to discuss about how to apply DL in industry. In the approach, CNN is used for preprocessing and offline tasks. Then the 6- DoF object position are estimated using a particle filter approach. Experiments will show that our approach is efficient and accurate. In future it could show potential solutions for human-machine collaboration systems.
This paper presents a principle for scene-related camera calibration in Manhattan worlds. The proposed estimation of extrinsic camera parameters from vanishing points represents a useful alternative to the traditional target-based calibration methods, especially in large urban or industrial environments. We analyse the effects of errors in the calculation of camera poses and derive general restrictions for the use of our approach. In addition, we present methods for calculating the position and orientation of several cameras to a world coordinate system and discuss the effect of imprecise or incorrectly calculated vanishing points. Our approach was evaluated with real images of a prototype for human-robot collaboration installed at ZBS e.V. The results were compared with a perspective n-Point (PnP) method.
The Kelvin Probe Force Microscopy (KPFM) is a method to detect the surface potential of micro- and nanostructured
samples using a common Scanning Probe Microscope (SPM). The electrostatic force has a very long
range compared to other surface forces. By using SPM systems the KPFM measurements are performed in the
noncontact region at surface distances greater than 10 nm. In contrast to topography measurement, the measured
data is blurred. The KPFM signal can be described as a convolution of an effective surface potential and a
microscope intrinsic point spread function, which allows the restoration of the measured data by deconvolution.
This paper deals with methods to deconvolute the measured KPFM data with the objective to increase the
lateral resolution. An analytical and a practical way of obtaining the point spread function of the microscope
was compared. In contrast to other papers a modern DoF-restricted deconvolution algorithm is applied to the
measured data. The new method was demonstrated on a nanoscale test stripe pattern for lateral resolution and
calibration of length scales (BAM-L200) made by German Federal Istitute for Materials Research and Testing.