Training echo state networks for rotation-invariant bone marrow cell classification

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

     

Das IT-Sicherheitsgesetz

Datenschutz und Datensicherheit - DuD - Die öffentliche Sicherheit zählt zu den wichtigsten Aufgaben der Innenpolitik. Dazu gehören der Schutz der Bürgerinnen und Bürger...     

An observer‐metamerism sensitivity index for electronic displays

The effect of observer metamerism induced by electronic displays depends to a large extent on their primary spectra (red, green, and blue in the most common case). In particular, for narrow‐band primary spectra whose peak wavelength lies in the range of high variability of the observer's color‐matching function, some observers can experience very large differences between actual surface colors (e.g. in a light booth) and displayed colors if the monitor is optimized for the International Commission on Illumination (CIE) 1931 standard observer. However, because narrow‐band light‐emitting diodes lead to larger color gamuts, more and more monitors with very narrow band primaries are coming onto the market without manufacturers taking into account the associated problem of observer variations. Being able to measure these variations accurately and efficiently is therefore an important objective. In this paper, we propose a new approach to predict the extent of observer metamerism for a particular multiprimary display. Unlike existing dedicated models, ours does not depend on a reference illuminant and a set of reflectance spectra and is computationally more efficient.     

In-situ measurement of higher-order strain derivatives for advanced analysis of forming processes using spatio-temporal optical flow

In-situ full-field measurements became one of the drivers for process understanding, model creation, validation and inverse analysis. Therefore, a novel spatio-temporal optical flow method for the robust measurement of higher-order strain derivatives is proposed. This computer vision approach overcomes inherent restrictions of established DIC methods. For advanced process analysis of shear cutting processes, the deformation curvature (2nd-order displacement derivative) and the respective rate (3rd-order displacement derivative) are of high interest. For the first time, it is possible to quantify experimentally these higher-order derivatives in sufficient quality with the proposed spatio-temporal optical flow approach. In addition, interesting correlations between the microstructure of the material and macroscopic process results are determined. This demonstrates the potential of the novel in-situ measurement approach for the advanced process analysis of metal forming processes in general.