Herbert Süße

On the Suitability of Different Features for Anomaly Detection in Wire Ropes

Automatic visual inspection of wire ropes is an important but challenging task, as anomalies in the rope are usually unobtrusive. Certainly, a reliable anomaly detection is essential to assure the safety of the ropes. A one-class classification approach for the automatic detection of anomalies in wire ropes is presented. Furthermore, the performance of different well-established features from the field of textural defect detection are compared with respect to this task. The faultless rope structure is thereby modeled by a Gaussian mixture model and outliers are regarded as anomaly. To prove the practical applicability, a careful evaluation of the presented approach is performed on real-life rope data. In doing so, a special interest was put on the robustness of the model with respect to unintentional outliers in the training and on its generalization ability given further data from an identically constructed rope. The results prove good recognition rates accompanied by a high generalization ability and robustness to outliers in the training set.

Novel computer vision algorithm for the reliable analysis of organelle morphology in whole cell 3D images--A pilot study for the quantitative evaluation of mitochondrial fragmentation in amyotrophic lateral sclerosis.

The function of intact organelles, whether mitochondria, Golgi apparatus or endoplasmic reticulum (ER), relies on their proper morphological organization. It is recognized that disturbances of organelle morphology are early events in disease manifestation, but reliable and quantitative detection of organelle morphology is difficult and time-consuming. Here we present a novel computer vision algorithm for the assessment of organelle morphology in whole cell 3D images. The algorithm allows the numerical and quantitative description of organelle structures, including total number and length of segments, cell and nucleus area/volume as well as novel texture parameters like lacunarity and fractal dimension. Applying the algorithm we performed a pilot study in cultured motor neurons from transgenic G93A hSOD1 mice, a model of human familial amyotrophic lateral sclerosis. In the presence of the mutated SOD1 and upon excitotoxic treatment with kainate we demonstrate a clear fragmentation of the mitochondrial network, with an increase in the number of mitochondrial segments and a reduction in the length of mitochondria. Histogram analyses show a reduced number of tubular mitochondria and an increased number of small mitochondrial segments. The computer vision algorithm for the evaluation of organelle morphology allows an objective assessment of disease-related organelle phenotypes with greatly reduced examiner bias and will aid the evaluation of novel therapeutic strategies on a cellular level.

Filterentwurf im Frequenzraum

Vom Gibbsschen Phanomen kennen wir schon den Begriff „idealer Tiefpass“. Dort haben wir bemerkt, dass beim idealen Tiefpass „Ringing-Artefakte“ in der Nahe von Kanten entstehen. Daher ist ein idealer Tiefpass im Sinne der Datenkompression uberhaupt nicht ideal. Idealer Tiefpass heist nun, wir multiplizieren die Fourierkoeffizienten eines Bildes mit der Rechteckfunktion. Wir schreiben dies einmal fur das Modell \(A1[X]\) auf:

Orts-Frequenz-Darstellungen

\displaystyle\alpha_{k}=\begin{cases}1&|k|\leq n\\ 0&\text{sonst}.\end{cases}

Momente, Matching und Merkmale

(7.1) Durch die Multiplikation im Frequenzraum haben wir im Ortsraum eine Faltung des Bildes \(f(x)\) mit der Funktion \(h(x)\), deren Fouriertransformierte die Rechteckfunktion darstellt. Daher transformieren wir nun die Rechteckfunktion zuruck:

Die Operationen Faltung und Korrelation

\sqrt{X}\cdot h(x) =D_{n}(x)=\sum_{k=-n}^{n}1\cdot e^{+2\pi ik\frac{x}{X}}=1+2\cdot\sum_{k=1}^{n}\cos\left(2\pi k\frac{x}{X}\right)