Visual Computing als Basis für Prozessinnovation im Produktlebenszyklus

Aus der Einführung: "Die Informationstechnik ist seit den Anfängen von CAD vor ca. 50 Jahren ein wesentlicher Impulsgeber für die Produktentwicklung und hat maßgeblichen Anteil an Prozessinnovationen wie dem Global Engineering oder der Digitalen Fabrik. Längst geht es aber heute nicht mehr allein um die Geometriebeschreibung zukünftiger Produkte, sondern um die möglichst umfassende Begleitung und Ergänzung des realen Produkts durch das virtuelle Produkt: von der ersten Idee bis zum Recycling. Die umfassende Vision des virtuellen Produkts als Pendant zum realen Produkt (Spur & Krause 1997) ist untrennbar mit dem Fortschritt der Informationstechnologie verbunden.

Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections

We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://github.com/alexander-g/INBD.Comment: CVPR 202

Visual Computing als Basis für Prozessinnovation im Produktlebenszyklus

Aus der Einführung: "Die Informationstechnik ist seit den Anfängen von CAD vor ca. 50 Jahren ein wesentlicher Impulsgeber für die Produktentwicklung und hat maßgeblichen Anteil an Prozessinnovationen wie dem Global Engineering oder der Digitalen Fabrik. Längst geht es aber heute nicht mehr allein um die Geometriebeschreibung zukünftiger Produkte, sondern um die möglichst umfassende Begleitung und Ergänzung des realen Produkts durch das virtuelle Produkt: von der ersten Idee bis zum Recycling. Die umfassende Vision des virtuellen Produkts als Pendant zum realen Produkt (Spur & Krause 1997) ist untrennbar mit dem Fortschritt der Informationstechnologie verbunden.

Visual Computing als Basis für Prozessinnovation im Produktlebenszyklus

Aus der Einführung: "Die Informationstechnik ist seit den Anfängen von CAD vor ca. 50 Jahren ein wesentlicher Impulsgeber für die Produktentwicklung und hat maßgeblichen Anteil an Prozessinnovationen wie dem Global Engineering oder der Digitalen Fabrik. Längst geht es aber heute nicht mehr allein um die Geometriebeschreibung zukünftiger Produkte, sondern um die möglichst umfassende Begleitung und Ergänzung des realen Produkts durch das virtuelle Produkt: von der ersten Idee bis zum Recycling. Die umfassende Vision des virtuellen Produkts als Pendant zum realen Produkt (Spur & Krause 1997) ist untrennbar mit dem Fortschritt der Informationstechnologie verbunden.

Marine snow detection and removal: underwater image restoration using background modeling

It is a common problem that images captured underwater (UW) are corrupted by noise. This is due to the light absorption and scattering by the marine environment; therefore, the visibility distance is limited up to few meters. Despite blur, haze, low contrast, non-uniform lightening and color cast which occasionally are termed noise, additive noises, such as sensor noise, are the center of attention of denoising algorithms. However, visibility of UW scenes is distorted by another source termed marine snow. This signal not only distorts the scene visibility by its presence but also disturbs the performance of advanced image processing algorithms such as segmentation, classification or detection. In this article, we propose a new method that removes marine snow from successive frames of videos recorded UW. This method utilizes the characteristics of such a phenomenon and detects it in each frame. In the meanwhile, using a background modeling algorithm, a reference image is obtained. Employing this image as a training data, we learn some prior information of the scene and finally, using these priors together with an inpainting algorithm, marine snow is eliminated by restoring the scene behind the particles

Background modeling: dealing with pan, tilt or zoom in videos

Even simple camera movements like pan, tilt or zoom constitute enormous problems for background subtraction algorithms since the modeling of the background works only under the assumption of a static camera. The problem has been mostly ignored and other algorithms have been used for videos with non-static cameras. Nonetheless, in this paper we introduce a method that adapts the background model to these camera movements by using affine transformations in combination with a similarity metric, and thereby the algorithm makes background subtraction usable for these situations. Also, to keep the generality of this approach, we first apply a detection step to avoid unnecessary adaptions in videos with a static camera because even small adaptions might otherwise deteriorate the background model over time. The method is evaluated on the extensive changedetection.net data set and could reliably detect camera motion in all videos as well as precisely adapt the model of the background to that motion. This does improve the quality of the background models significantly which consequently leads to a higher accuracy of the segmentations

As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible : the convolutional neural network “RootDetector”

Plant roots influence many ecological and biogeochemical processes, such as carbon, water and nutrient cycling. Because of difficult accessibility, knowledge on plant root growth dynamics in field conditions, however, is fragmentary at best. Minirhizotrons, i.e. transparent tubes placed in the substrate into which specialized cameras or circular scanners are inserted, facilitate the capture of high-resolution images of root dynamics at the soil-tube interface with little to no disturbance after the initial installation. Their use, especially in field studies with multiple species and heterogeneous substrates, though, is limited by the amount of work that subsequent manual tracing of roots in the images requires. Furthermore, the reproducibility and objectivity of manual root detection is questionable. Here, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise. Our minirhizotron data come from various wetlands on organic soils, i.e. highly heterogeneous substrates consisting of dead plant material, often times mainly roots, in various degrees of decomposition. This may be seen as one of the most challenging soil types for root segmentation in minirhizotron images. RootDetector showed a high capability to correctly segment root pixels in minirhizotron images from field observations (F1 = 0.6044; r2 compared to a human expert = 0.99). Reproducibility among humans, however, depended strongly on expertise level, with novices showing drastic variation among individual analysts and annotating on average more than 13-times higher root length/cm2 per image compared to expert analysts. CNNs such as RootDetector provide a reliable and efficient method for the detection of roots and root length in minirhizotron images even from challenging field conditions. Analyses with RootDetector thus save resources, are reproducible and objective, and are as accurate as manual analyses performed by human experts